text
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dict | prediction
null | prediction_agent
null | annotation
list | annotation_agent
null | multi_label
bool 1
class | explanation
null | id
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{
"abstract": " We present an analysis of the main systematic effects that could impact the\nmeasurement of CMB polarization with the proposed CORE space mission. We employ\ntimeline-to-map simulations to verify that the CORE instrumental set-up and\nscanning strategy allow us to measure sky polarization to a level of accuracy\nadequate to the mission science goals. We also show how the CORE observations\ncan be processed to mitigate the level of contamination by potentially worrying\nsystematics, including intensity-to-polarization leakage due to bandpass\nmismatch, asymmetric main beams, pointing errors and correlated noise. We use\nanalysis techniques that are well validated on data from current missions such\nas Planck to demonstrate how the residual contamination of the measurements by\nthese effects can be brought to a level low enough not to hamper the scientific\ncapability of the mission, nor significantly increase the overall error budget.\nWe also present a prototype of the CORE photometric calibration pipeline, based\non that used for Planck, and discuss its robustness to systematics, showing how\nCORE can achieve its calibration requirements. While a fine-grained assessment\nof the impact of systematics requires a level of knowledge of the system that\ncan only be achieved in a future study phase, the analysis presented here\nstrongly suggests that the main areas of concern for the CORE mission can be\naddressed using existing knowledge, techniques and algorithms.\n",
"title": "Exploring cosmic origins with CORE: mitigation of systematic effects"
}
| null | null | null | null | true | null |
16901
| null |
Default
| null | null |
null |
{
"abstract": " Peridynamics (PD) represents a new approach for modelling fracture mechanics,\nwhere a continuum domain is modelled through particles connected via physical\nbonds. This formulation allows us to model crack initiation, propagation,\nbranching and coalescence without special assumptions. Up to date, anisotropic\nmaterials were modelled in the PD framework as different isotropic materials\n(for instance, fibre and matrix of a composite laminate), where the stiffness\nof the bond depends on its orientation. A non-ordinary state-based formulation\nwill enable the modelling of generally anisotropic materials, where the\nmaterial properties are directly embedded in the formulation. Other material\nmodels include rocks, concrete and biomaterials such as bones. In this paper,\nwe implemented this model and validated it for anisotropic composite materials.\nA composite damage criterion has been employed to model the crack propagation\nbehaviour. Several numerical examples have been used to validate the approach,\nand compared to other benchmark solution from the finite element method (FEM)\nand experimental results when available.\n",
"title": "A non-ordinary peridynamics implementation for anisotropic materials"
}
| null | null | null | null | true | null |
16902
| null |
Default
| null | null |
null |
{
"abstract": " Continuous attractor neural networks generate a set of smoothly connected\nattractor states. In memory systems of the brain, these attractor states may\nrepresent continuous pieces of information such as spatial locations and head\ndirections of animals. However, during the replay of previous experiences,\nhippocampal neurons show a discontinuous sequence in which discrete transitions\nof neural state are phase-locked with the slow-gamma (30-40 Hz) oscillation.\nHere, we explored the underlying mechanisms of the discontinuous sequence\ngeneration. We found that a continuous attractor neural network has several\nphases depending on the interactions between external input and local\ninhibitory feedback. The discrete-attractor-like behavior naturally emerges in\none of these phases without any discreteness assumption. We propose that the\ndynamics of continuous attractor neural networks is the key to generate\ndiscontinuous state changes phase-locked to the brain rhythm.\n",
"title": "Discrete-attractor-like Tracking in Continuous Attractor Neural Networks"
}
| null | null |
[
"Quantitative Biology"
] | null | true | null |
16903
| null |
Validated
| null | null |
null |
{
"abstract": " In this paper, we develop a framework for an innovative perceptive mobile\n(i.e. cellular) network that integrates sensing with communication, and\nsupports new applications widely in transportation, surveillance and\nenvironmental sensing. Three types of sensing methods implemented in the\nbase-stations are proposed, using either uplink or downlink multiuser\ncommunication signals. The required changes to system hardware and major\ntechnical challenges are briefly discussed. We also demonstrate the feasibility\nof estimating sensing parameters via developing a compressive sensing based\nscheme and providing simulation results to validate its effectiveness.\n",
"title": "Framework for an Innovative Perceptive Mobile Network Using Joint Communication and Sensing"
}
| null | null | null | null | true | null |
16904
| null |
Default
| null | null |
null |
{
"abstract": " We show that the smallest non-abelian quotient of $\\mathrm{Aut}(F_n)$ is\n$\\mathrm{PSL}_n(\\mathbb{Z}/2\\mathbb{Z}) = \\mathrm{L}_n(2)$, thus confirming a\nconjecture of Mecchia--Zimmermann. In the course of the proof we give an\nexponential (in $n$) lower bound for the cardinality of a set on which\n$\\mathrm{SAut}(F_n)$, the unique index $2$ subgroup of $\\mathrm{Aut}(F_n)$, can\nact non-trivially. We also offer new results on the representation theory of\n$\\mathrm{SAut(F_n)}$ in small dimensions over small, positive characteristics,\nand on rigidity of maps from $\\mathrm{SAut}(F_n)$ to finite groups of Lie type\nand algebraic groups in characteristic $2$.\n",
"title": "On the smallest non-abelian quotient of $\\mathrm{Aut}(F_n)$"
}
| null | null | null | null | true | null |
16905
| null |
Default
| null | null |
null |
{
"abstract": " This paper explores the information-theoretic limitations of graph property\ntesting in zero-field Ising models. Instead of learning the entire graph\nstructure, sometimes testing a basic graph property such as connectivity, cycle\npresence or maximum clique size is a more relevant and attainable objective.\nSince property testing is more fundamental than graph recovery, any necessary\nconditions for property testing imply corresponding conditions for graph\nrecovery, while custom property tests can be statistically and/or\ncomputationally more efficient than graph recovery based algorithms.\nUnderstanding the statistical complexity of property testing requires the\ndistinction of ferromagnetic (i.e., positive interactions only) and general\nIsing models. Using combinatorial constructs such as graph packing and strong\nmonotonicity, we characterize how target properties affect the corresponding\nminimax upper and lower bounds within the realm of ferromagnets. On the other\nhand, by studying the detection of an antiferromagnetic (i.e., negative\ninteractions only) Curie-Weiss model buried in Rademacher noise, we show that\nproperty testing is strictly more challenging over general Ising models. In\nterms of methodological development, we propose two types of correlation based\ntests: computationally efficient screening for ferromagnets, and score type\ntests for general models, including a fast cycle presence test. Our correlation\nscreening tests match the information-theoretic bounds for property testing in\nferromagnets.\n",
"title": "Property Testing in High Dimensional Ising models"
}
| null | null | null | null | true | null |
16906
| null |
Default
| null | null |
null |
{
"abstract": " In this paper, we show that the category of module spectra over\n$C^*(B\\mathcal{G},\\mathbb{F}_p)$ is stratified for any $p$-local compact group\n$\\mathcal{G}$, thereby giving a support-theoretic classification of all\nlocalizing subcategories of this category. To this end, we generalize Quillen's\n$F$-isomorphism theorem, Quillen's stratification theorem, Chouinard's theorem,\nand the finite generation of cohomology rings from finite groups to homotopical\ngroups. Moreover, we show that $p$-compact groups admit a homotopical form of\nGorenstein duality.\n",
"title": "Stratification and duality for homotopical groups"
}
| null | null | null | null | true | null |
16907
| null |
Default
| null | null |
null |
{
"abstract": " We present a family of Python modules for the numerical integration of\nordinary, delay, or stochastic differential equations. The key features are\nthat the user enters the derivative symbolically and it is\njust-in-time-compiled, allowing the user to efficiently integrate differential\nequations from a higher-level interpreted language. The presented modules are\nparticularly suited for large systems of differential equations such as used to\ndescribe dynamics on complex networks. Through the selected method of input,\nthe presented modules also allow to almost completely automatize the process of\nestimating regular as well as transversal Lyapunov exponents for ordinary and\ndelay differential equations. We conceptually discuss the modules' design,\nanalyze their performance, and demonstrate their capabilities by application to\ntimely problems.\n",
"title": "Efficiently and easily integrating differential equations with JiTCODE, JiTCDDE, and JiTCSDE"
}
| null | null | null | null | true | null |
16908
| null |
Default
| null | null |
null |
{
"abstract": " Diffusion-based classifiers such as those relying on the Personalized\nPageRank and the Heat kernel, enjoy remarkable classification accuracy at\nmodest computational requirements. Their performance however is affected by the\nextent to which the chosen diffusion captures a typically unknown label\npropagation mechanism, that can be specific to the underlying graph, and\npotentially different for each class. The present work introduces a\ndisciplined, data-efficient approach to learning class-specific diffusion\nfunctions adapted to the underlying network topology. The novel learning\napproach leverages the notion of \"landing probabilities\" of class-specific\nrandom walks, which can be computed efficiently, thereby ensuring scalability\nto large graphs. This is supported by rigorous analysis of the properties of\nthe model as well as the proposed algorithms. Furthermore, a robust version of\nthe classifier facilitates learning even in noisy environments.\nClassification tests on real networks demonstrate that adapting the diffusion\nfunction to the given graph and observed labels, significantly improves the\nperformance over fixed diffusions; reaching -- and many times surpassing -- the\nclassification accuracy of computationally heavier state-of-the-art competing\nmethods, that rely on node embeddings and deep neural networks.\n",
"title": "Adaptive Diffusions for Scalable Learning over Graphs"
}
| null | null |
[
"Statistics"
] | null | true | null |
16909
| null |
Validated
| null | null |
null |
{
"abstract": " Active Learning (AL) methods have proven cost-saving against passive\nsupervised methods in many application domains. An active learner, aiming to\nfind some target hypothesis, formulates sequential queries to some oracle. The\nset of hypotheses consistent with the already answered queries is called\nversion space. Several query selection measures (QSMs) for determining the best\nquery to ask next have been proposed. Assuming binaryoutcome queries, we\nanalyze various QSMs wrt. to the discrimination power of their selected queries\nwithin the current version space. As a result, we derive superiority and\nequivalence relations between these QSMs and introduce improved versions of\nexisting QSMs to overcome identified issues. The obtained picture gives a hint\nabout which QSMs should preferably be used in pool-based AL scenarios.\nMoreover, we deduce properties optimal queries wrt. QSMs must satisfy. Based on\nthese, we demonstrate how efficient heuristic search methods for optimal\nqueries in query synthesis AL scenarios can be devised.\n",
"title": "On the Discrimination Power and Effective Utilization of Active Learning Measures in Version Space Search"
}
| null | null | null | null | true | null |
16910
| null |
Default
| null | null |
null |
{
"abstract": " We introduce torchbearer, a model fitting library for pytorch aimed at\nresearchers working on deep learning or differentiable programming. The\ntorchbearer library provides a high level metric and callback API that can be\nused for a wide range of applications. We also include a series of built in\ncallbacks that can be used for: model persistence, learning rate decay,\nlogging, data visualization and more. The extensive documentation includes an\nexample library for deep learning and dynamic programming problems and can be\nfound at this http URL. The code is licensed under the MIT\nLicense and available at this https URL.\n",
"title": "Torchbearer: A Model Fitting Library for PyTorch"
}
| null | null | null | null | true | null |
16911
| null |
Default
| null | null |
null |
{
"abstract": " Line-intensity mapping surveys probe large-scale structure through spatial\nvariations in molecular line emission from a population of unresolved\ncosmological sources. Future such surveys of carbon monoxide line emission,\nspecifically the CO(1-0) line, face potential contamination from a disjoint\npopulation of sources emitting in a hydrogen cyanide emission line, HCN(1-0).\nThis paper explores the potential range of the strength of HCN emission and its\neffect on the CO auto power spectrum, using simulations with an empirical model\nof the CO/HCN--halo connection. We find that effects on the observed CO power\nspectrum depend on modeling assumptions but are very small for our fiducial\nmodel based on our understanding of the galaxy--halo connection, with the bias\nin overall CO detection significance due to HCN expected to be less than 1%.\n",
"title": "On estimation of contamination from hydrogen cyanide in carbon monoxide line intensity mapping"
}
| null | null | null | null | true | null |
16912
| null |
Default
| null | null |
null |
{
"abstract": " Classifiers can be trained with data-dependent constraints to satisfy\nfairness goals, reduce churn, achieve a targeted false positive rate, or other\npolicy goals. We study the generalization performance for such constrained\noptimization problems, in terms of how well the constraints are satisfied at\nevaluation time, given that they are satisfied at training time. To improve\ngeneralization performance, we frame the problem as a two-player game where one\nplayer optimizes the model parameters on a training dataset, and the other\nplayer enforces the constraints on an independent validation dataset. We build\non recent work in two-player constrained optimization to show that if one uses\nthis two-dataset approach, then constraint generalization can be significantly\nimproved. As we illustrate experimentally, this approach works not only in\ntheory, but also in practice.\n",
"title": "Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints"
}
| null | null | null | null | true | null |
16913
| null |
Default
| null | null |
null |
{
"abstract": " Several studies have shown that the network traffic that is generated by a\nvisit to a website over Tor reveals information specific to the website through\nthe timing and sizes of network packets. By capturing traffic traces between\nusers and their Tor entry guard, a network eavesdropper can leverage this\nmeta-data to reveal which website Tor users are visiting. The success of such\nattacks heavily depends on the particular set of traffic features that are used\nto construct the fingerprint. Typically, these features are manually engineered\nand, as such, any change introduced to the Tor network can render these\ncarefully constructed features ineffective. In this paper, we show that an\nadversary can automate the feature engineering process, and thus automatically\ndeanonymize Tor traffic by applying our novel method based on deep learning. We\ncollect a dataset comprised of more than three million network traces, which is\nthe largest dataset of web traffic ever used for website fingerprinting, and\nfind that the performance achieved by our deep learning approaches is\ncomparable to known methods which include various research efforts spanning\nover multiple years. The obtained success rate exceeds 96% for a closed world\nof 100 websites and 94% for our biggest closed world of 900 classes. In our\nopen world evaluation, the most performant deep learning model is 2% more\naccurate than the state-of-the-art attack. Furthermore, we show that the\nimplicit features automatically learned by our approach are far more resilient\nto dynamic changes of web content over time. We conclude that the ability to\nautomatically construct the most relevant traffic features and perform accurate\ntraffic recognition makes our deep learning based approach an efficient,\nflexible and robust technique for website fingerprinting.\n",
"title": "Automated Website Fingerprinting through Deep Learning"
}
| null | null | null | null | true | null |
16914
| null |
Default
| null | null |
null |
{
"abstract": " This paper explores the characteristics of DataCite to determine its\npossibilities and potential as a new bibliometric data source to analyze the\nscholarly production of open data. Open science and the increasing data sharing\nrequirements from governments, funding bodies, institutions and scientific\njournals has led to a pressing demand for the development of data metrics. As a\nvery first step towards reliable data metrics, we need to better comprehend the\nlimitations and caveats of the information provided by sources of open data. In\nthis paper, we critically examine records downloaded from the DataCite's OAI\nAPI and elaborate a series of recommendations regarding the use of this source\nfor bibliometric analyses of open data. We highlight issues related to metadata\nincompleteness, lack of standardization, and ambiguous definitions of several\nfields. Despite these limitations, we emphasize DataCite's value and potential\nto become one of the main sources for data metrics development.\n",
"title": "DataCite as a novel bibliometric source: Coverage, strengths and limitations"
}
| null | null | null | null | true | null |
16915
| null |
Default
| null | null |
null |
{
"abstract": " We obtain strong consistency and asymptotic normality of a least squares\nestimator of the drift coefficient for complex-valued Ornstein-Uhlenbeck\nprocesses disturbed by fractional noise, extending the result of Y. Hu and D.\nNualart, [Statist. Probab. Lett., 80 (2010), 1030-1038] to a special\n2-dimensions. The strategy is to exploit the Garsia-Rodemich-Rumsey inequality\nand complex fourth moment theorems. The main ingredients of this paper are the\nsample path regularity of a multiple Wiener-Ito integral and two equivalent\nconditions of complex fourth moment theorems in terms of the contractions of\nintegral kernels and complex Malliavin derivatives.\n",
"title": "Parameter Estimation of Complex Fractional Ornstein-Uhlenbeck Processes with Fractional Noise"
}
| null | null | null | null | true | null |
16916
| null |
Default
| null | null |
null |
{
"abstract": " In the article, proposed is a new e-learning information technology based on\nan ontology driven learning engine, which is matched with modern pedagogical\ntechnologies. With the help of proposed engine and developed question database\nwe have conducted an experiment, where students were tested. The developed\nontology driven system of e-learning facilitates the creation of favorable\nconditions for the development of personal qualities and creation of a holistic\nunderstanding of the subject area among students throughout the educational\nprocess.\n",
"title": "E-learning Information Technology Based on an Ontology Driven Learning Engine"
}
| null | null | null | null | true | null |
16917
| null |
Default
| null | null |
null |
{
"abstract": " The Euler-Poisson-Alignment (EPA) system appears in mathematical biology and\nis used to model, in a hydrodynamic limit, a set agents interacting through\nmutual attraction/repulsion as well as alignment forces. We consider\none-dimensional EPA system with a class of singular alignment terms as well as\nnatural attraction/repulsion terms. The singularity of the alignment kernel\nproduces an interesting effect regularizing the solutions of the equation and\nleading to global regularity for wide range of initial data. This was recently\nobserved in the paper by Do, Kiselev, Ryzhik and Tan. Our goal in this paper is\nto generalize the result and to incorporate the attractive/repulsive potential.\nWe prove that global regularity persists for these more general models.\n",
"title": "Global regularity for 1D Eulerian dynamics with singular interaction forces"
}
| null | null |
[
"Mathematics"
] | null | true | null |
16918
| null |
Validated
| null | null |
null |
{
"abstract": " New bispectral orthogonal polynomials are obtained from an unconventional\ntruncation of the Askey-Wilson polynomials. In the limit $q \\to 1$, they reduce\nto the para-Racah polynomials which are orthogonal with respect to a quadratic\nbi-lattice. The three term recurrence relation and q-difference equation are\nobtained through limits of those of the Askey-Wilson polynomials. An explicit\nexpression in terms of hypergeometric series and the orthogonality relation are\nprovided. A $q$-generalization of the para-Krawtchouk polynomials is obtained\nas a special case. Connections with the $q$-Racah and dual-Hahn polynomials are\nalso presented.\n",
"title": "A $q$-generalization of the para-Racah polynomials"
}
| null | null | null | null | true | null |
16919
| null |
Default
| null | null |
null |
{
"abstract": " Unsupervised node embedding methods (e.g., DeepWalk, LINE, and node2vec) have\nattracted growing interests given their simplicity and effectiveness. However,\nalthough these methods have been proved effective in a variety of applications,\nnone of the existing work has analyzed the robustness of them. This could be\nvery risky if these methods are attacked by an adversarial party. In this\npaper, we take the task of link prediction as an example, which is one of the\nmost fundamental problems for graph analysis, and introduce a data positioning\nattack to node embedding methods. We give a complete characterization of\nattacker's utilities and present efficient solutions to adversarial attacks for\ntwo popular node embedding methods: DeepWalk and LINE. We evaluate our proposed\nattack model on multiple real-world graphs. Experimental results show that our\nproposed model can significantly affect the results of link prediction by\nslightly changing the graph structures (e.g., adding or removing a few edges).\nWe also show that our proposed model is very general and can be transferable\nacross different embedding methods. Finally, we conduct a case study on a\ncoauthor network to better understand our attack method.\n",
"title": "Data Poisoning Attack against Unsupervised Node Embedding Methods"
}
| null | null | null | null | true | null |
16920
| null |
Default
| null | null |
null |
{
"abstract": " Two-dimensional (spin-$2$) Affleck-Kennedy-Lieb-Tasaki (AKLT) type valence\nbond solids on the square lattice are known to be symmetry protected\ntopological (SPT) gapped spin liquids [Shintaro Takayoshi, Pierre Pujol, and\nAkihiro Tanaka Phys. Rev. B ${\\bf 94}$, 235159 (2016)]. Using the projected\nentangled pair state (PEPS) framework, we extend the construction of the AKLT\nstate to the case of $SU(3)$, relevant for cold atom systems. The entanglement\nspectrum is shown to be described by an alternating $SU(3)$ chain of \"quarks\"\nand \"antiquarks\", subject to exponentially decaying (with distance) Heisenberg\ninteractions, in close similarity with its $SU(2)$ analog. We discuss the SPT\nfeature of the state.\n",
"title": "Entanglement properties of the two-dimensional SU(3) AKLT state"
}
| null | null | null | null | true | null |
16921
| null |
Default
| null | null |
null |
{
"abstract": " Efficient management of low blood pressure (BP) in preterm neonates remains\nchallenging with a considerable variability in clinical practice. The ability\nto assess preterm wellbeing during episodes of low BP will help to decide when\nand whether hypotension treatment should be initiated. This work aims to\ninvestigate the relationship between heart rate variability (HRV), BP and the\nshort-term neurological outcome in preterm infants less than 32 weeks\ngestational age (GA). The predictive power of common HRV features with respect\nto the outcome is assessed and shown to improve when HRV is observed during\nepisodes of low mean arterial pressure (MAP) - with a single best feature\nleading to an AUC of 0.87. Combining multiple features with a boosted decision\ntree classifier achieves an AUC of 0.97. The work presents a promising step\ntowards the use of multimodal data in building an objective decision support\ntool for clinical prediction of short-term outcome in preterms who suffer\nepisodes of low BP.\n",
"title": "Heart Rate Variability during Periods of Low Blood Pressure as a Predictor of Short-Term Outcome in Preterms"
}
| null | null | null | null | true | null |
16922
| null |
Default
| null | null |
null |
{
"abstract": " The power of the press to shape the informational landscape of a population\nis unparalleled, even now in the era of democratic access to all information\noutlets. However, it is known that news outlets (particularly more traditional\nones) tend to discriminate who they want to reach, and who to leave aside. In\nthis work, we attempt to shed some light on the audience targeting patterns of\nnewspapers, using the Chilean media ecosystem. First, we use the gravity model\nto analyze geography as a factor in explaining audience reachability. This\nshows that some newspapers are indeed driven by geographical factors (mostly\nlocal news outlets) but some others are not (national-distribution outlets).\nFor those which are not, we use a regression model to study the influence of\nsocioeconomic and political characteristics in news outlets adoption. We\nconclude that indeed larger, national-distribution news outlets target\npopulations based on these factors, rather than on geography or immediacy.\n",
"title": "Understanding News Outlets' Audience-Targeting Patterns"
}
| null | null | null | null | true | null |
16923
| null |
Default
| null | null |
null |
{
"abstract": " Selecting a representative vector for a set of vectors is a very common\nrequirement in many algorithmic tasks. Traditionally, the mean or median vector\nis selected. Ontology classes are sets of homogeneous instance objects that can\nbe converted to a vector space by word vector embeddings. This study proposes a\nmethodology to derive a representative vector for ontology classes whose\ninstances were converted to the vector space. We start by deriving five\ncandidate vectors which are then used to train a machine learning model that\nwould calculate a representative vector for the class. We show that our\nmethodology out-performs the traditional mean and median vector\nrepresentations.\n",
"title": "Deriving a Representative Vector for Ontology Classes with Instance Word Vector Embeddings"
}
| null | null | null | null | true | null |
16924
| null |
Default
| null | null |
null |
{
"abstract": " ESA Gaia mission is producing the more accurate source catalogue in astronomy\nup to now. That represents a challenge on the archiving area to make accessible\nthis information to the astronomers in an efficient way. Also, new astronomical\nmissions have reinforced the change on the development of archives. Archives,\nas simple applications to access the data are being evolving into complex data\ncenter structures where computing power services are available for users and\ndata mining tools are integrated into the server side. In the case of astronomy\nscience that involves the use of big catalogues, as in Gaia (or Euclid to\ncome), the common ways to work on the data need to be changed to a new paradigm\n\"move code close to the data\", what implies that data mining functionalities\nare becoming a must to allow the science exploitation. To enable these\ncapabilities, a TAP+ interface, crossmatch capabilities, full catalogue\nhistograms, serialisation of intermediate results in cloud resources like\nVOSpace, etc have been implemented for the Gaia DR1, to enable the exploitation\nof these science resources by the community without the bottlenecks on the\nconnection bandwidth. We present the architecture, infrastructure and tools\nalready available in the Gaia Archive Data Release 1\n(this http URL) and we describe capabilities and\ninfrastructure.\n",
"title": "The ESA Gaia Archive: Data Release 1"
}
| null | null |
[
"Physics"
] | null | true | null |
16925
| null |
Validated
| null | null |
null |
{
"abstract": " We present an amelioration of current known algorithms for optimal spectral\npartitioning problems. The idea is to use the advantage of a representation\nusing density functions while decreasing the computational time. This is done\nby restricting the computation to neighbourhoods of regions where the\nassociated densities are above a certain threshold. The algorithm extends and\nimproves known methods in the plane and on surfaces in dimension 3. It also\nmakes possible to make some of the first computations of volumic 3D spectral\npartitions on sufficiently large discretizations.\n",
"title": "Efficient algorithm for large spectral partitions"
}
| null | null | null | null | true | null |
16926
| null |
Default
| null | null |
null |
{
"abstract": " Seven of the nine known Mars Trojan asteroids belong to an orbital cluster\nnamed after its largest member 5261 Eureka. Eureka is likely the progenitor of\nthe whole cluster, which formed at least 1 Gyr ago. It was suggested that the\nthermal YORP effect spun-up Eureka resulting with fragments being ejected by\nthe rotational-fission mechanism. Eureka's spectrum exhibits a broad and deep\nabsorption band around 1 {\\mu}m, indicating an olivine-rich composition. Here\nwe show evidence that the Trojan Eureka cluster progenitor could have\noriginated as impact debris excavated from the Martian mantle. We present new\nnear-infrared observations of two Trojans (311999 2007 NS2 and 385250 2001\nDH47) and find that both exhibit an olivine-rich reflectance spectrum similar\nto Eureka's. These measurements confirm that the progenitor of the cluster has\nan achondritic composition. Olivine-rich reflectance spectra are rare amongst\nasteroids but are seen around the largest basins on Mars. They are also\nconsistent with some Martian meteorites (e.g. Chassigny), and with the material\ncomprising much of the Martian mantle. Using numerical simulations, we show\nthat the Mars Trojans are more likely to be impact ejecta from Mars than\ncaptured olivine-rich asteroids transported from the main belt. This result\nlinks directly specific asteroids to debris from the forming planets.\n",
"title": "A Martian Origin for the Mars Trojan Asteroids"
}
| null | null |
[
"Physics"
] | null | true | null |
16927
| null |
Validated
| null | null |
null |
{
"abstract": " The abundance of metals in galaxies is a key parameter which permits to\ndistinguish between different galaxy formation and evolution models. Most of\nthe metallicity determinations are based on optical line ratios. However, the\noptical spectral range is subject to dust extinction and, for high-z objects (z\n> 3), some of the lines used in optical metallicity diagnostics are shifted to\nwavelengths not accessible to ground based observatories. For this reason, we\nexplore metallicity diagnostics using far-infrared (IR) line ratios which can\nprovide a suitable alternative in such situations. To investigate these far-IR\nline ratios, we modeled the emission of a starburst with the photoionization\ncode CLOUDY. The most sensitive far-IR ratios to measure metallicities are the\n[OIII]52$\\mu$m and 88$\\mu$m to [NIII]57$\\mu$m ratios. We show that this ratio\nproduces robust metallicities in the presence of an AGN and is insensitive to\nchanges in the age of the ionizing stellar. Another metallicity sensitive ratio\nis the [OIII]88$\\mu$m/[NII]122$\\mu$m ratio, although it depends on the\nionization parameter. We propose various mid- and far-IR line ratios to break\nthis dependency. Finally, we apply these far-IR diagnostics to a sample of 19\nlocal ultraluminous IR galaxies (ULIRGs) observed with Herschel and Spitzer. We\nfind that the gas-phase metallicity in these local ULIRGs is in the range 0.7 <\nZ_gas/Z_sun < 1.5, which corresponds to 8.5 < 12 + log (O/H) < 8.9. The\ninferred metallicities agree well with previous estimates for local ULIRGs and\nthis confirms that they lie below the local mass-metallicity relation.\n",
"title": "Far-infrared metallicity diagnostics: Application to local ultraluminous infrared galaxies"
}
| null | null |
[
"Physics"
] | null | true | null |
16928
| null |
Validated
| null | null |
null |
{
"abstract": " We show that quantum communication by means of collapse of the wave function\nis possible. In this study, quantum communication does not mean quantum\nteleportation or quantum cryptography, but transmission of information itself.\nBecause of consistency with special relativity, the possibility of the quantum\ncommunication leads to another conclusion that the collapse of the wave\nfunction must propagate at the speed of light or slower.\nWe show this requirement is consistent with nonlocality in quantum mechanics.\nWe also demonstrate that the Einstein-Podolsky-Rosen experiment does not\ndisprove our conclusion.\n",
"title": "Quantum communication by means of collapse of the wave function"
}
| null | null | null | null | true | null |
16929
| null |
Default
| null | null |
null |
{
"abstract": " Terramechanics plays a critical role in the areas of ground vehicles and\nground mobile robots since understanding and estimating the variables\ninfluencing the vehicle-terrain interaction may mean the success or the failure\nof an entire mission. This research applies state-of-the-art algorithms in deep\nlearning to two key problems: estimating wheel slip and classifying the terrain\nbeing traversed by a ground robot. Three data sets collected by ground robotic\nplatforms (MIT single-wheel testbed, MSL Curiosity rover, and tracked robot\nFitorobot) are employed in order to compare the performance of traditional\nmachine learning methods (i.e. Support Vector Machine (SVM) and Multi-layer\nPerceptron (MLP)) against Deep Neural Networks (DNNs) and Convolutional Neural\nNetworks (CNNs). This work also shows the impact that certain tuning parameters\nand the network architecture (MLP, DNN and CNN) play on the performance of\nthose methods. This paper also contributes a deep discussion with the lessons\nlearned in the implementation of DNNs and CNNs and how these methods can be\nextended to solve other problems.\n",
"title": "DeepTerramechanics: Terrain Classification and Slip Estimation for Ground Robots via Deep Learning"
}
| null | null | null | null | true | null |
16930
| null |
Default
| null | null |
null |
{
"abstract": " We provide novel characterizations of multivariate normality that incorporate\nboth the characteristic function and the moment generating function, and we\nemploy these results to construct a class of affine invariant, consistent and\neasy-to-use goodness-of-fit tests for normality. The test statistics are\nsuitably weighted $L^2$-statistics, and we provide their asymptotic behavior\nboth for i.i.d. observations as well as in the context of testing that the\ninnovation distribution of a multivariate GARCH model is Gaussian. We also\nstudy the finite-sample behavior of the new tests and compare the new criteria\nwith alternative existing tests.\n",
"title": "Characterizations of multinormality and corresponding tests of fit, including for Garch models"
}
| null | null |
[
"Mathematics",
"Statistics"
] | null | true | null |
16931
| null |
Validated
| null | null |
null |
{
"abstract": " We consider multi-task regression models where the observations are assumed\nto be a linear combination of several latent node functions and weight\nfunctions, which are both drawn from Gaussian process priors. Driven by the\nproblem of developing scalable methods for forecasting distributed solar and\nother renewable power generation, we propose coupled priors over groups of\n(node or weight) processes to exploit spatial dependence between functions. We\nestimate forecast models for solar power at multiple distributed sites and\nground wind speed at multiple proximate weather stations. Our results show that\nour approach maintains or improves point-prediction accuracy relative to\ncompeting solar benchmarks and improves over wind forecast benchmark models on\nall measures. Our approach consistently dominates the equivalent model without\ncoupled priors, achieving faster gains in forecast accuracy. At the same time\nour approach provides better quantification of predictive uncertainties.\n",
"title": "Grouped Gaussian Processes for Solar Power Prediction"
}
| null | null | null | null | true | null |
16932
| null |
Default
| null | null |
null |
{
"abstract": " Since social interactions have been shown to lead to symmetric clusters, we\npropose here that symmetries play a key role in epidemic modeling. Mathematical\nmodels on d-ary tree graphs were recently shown to be particularly effective\nfor modeling epidemics in simple networks [Seibold & Callender, 2016]. To\naccount for symmetric relations, we generalize this to a new type of networks\nmodeled on d-cliqued tree graphs, which are obtained by adding edges to regular\nd-trees to form d-cliques. This setting gives a more realistic model for\nepidemic outbreaks originating, for example, within a family or classroom and\nwhich could reach a population by transmission via children in schools.\nSpecifically, we quantify how an infection starting in a clique (e.g. family)\ncan reach other cliques through the body of the graph (e.g. public places).\nMoreover, we propose and study the notion of a safe zone, a subset that has a\nnegligible probability of infection.\n",
"title": "Modeling epidemics on d-cliqued graphs"
}
| null | null | null | null | true | null |
16933
| null |
Default
| null | null |
null |
{
"abstract": " Cohomological and K-theoretic stable bases originated from the study of\nquantum cohomology and quantum K-theory. Restriction formula for cohomological\nstable bases played an important role in computing the quantum connection of\ncotangent bundle of partial flag varieties. In this paper we study the\nK-theoretic stable bases of cotangent bundles of flag varieties. We describe\nthese bases in terms of the action of the affine Hecke algebra and the twisted\ngroup algebra of Kostant-Kumar. Using this algebraic description and the method\nof root polynomials, we give a restriction formula of the stable bases. We\napply it to obtain the restriction formula for partial flag varieties. We also\nbuild a relation between the stable basis and the Casselman basis in the\nprincipal series representations of the Langlands dual group. As an\napplication, we give a closed formula for the transition matrix between\nCasselman basis and the characteristic functions.\n",
"title": "On the K-theory stable bases of the Springer resolution"
}
| null | null | null | null | true | null |
16934
| null |
Default
| null | null |
null |
{
"abstract": " The amount of information available to the mathematics teacher is so enormous\nthat the selection of desirable content is gradually becoming a huge task in\nitself. With respect to the inclusion of elements of history of mathematics in\nmathematics instruction, the era of Big Data introduces a high likelihood of\nRecency Bias, a hitherto unconnected challenge for stakeholders in mathematics\neducation. This tendency to choose recent information at the expense of\nrelevant older, composite, historical facts stands to defeat the aims and\nobjectives of the epistemological and cultural approach to mathematics\ninstructional delivery. This study is a didactic discourse with focus on this\nthreat to the history and pedagogy of mathematics, particularly as it affects\nmathematics education in Nigeria. The implications for mathematics curriculum\ndevelopers, teacher-training programmes, teacher lesson preparation, and\npublication of mathematics instructional materials were also deeply considered.\n",
"title": "Recency Bias in the Era of Big Data: The Need to Strengthen the Status of History of Mathematics in Nigerian Schools"
}
| null | null |
[
"Computer Science",
"Mathematics"
] | null | true | null |
16935
| null |
Validated
| null | null |
null |
{
"abstract": " This paper presents a convergence analysis of kernel-based quadrature rules\nin misspecified settings, focusing on deterministic quadrature in Sobolev\nspaces. In particular, we deal with misspecified settings where a test\nintegrand is less smooth than a Sobolev RKHS based on which a quadrature rule\nis constructed. We provide convergence guarantees based on two different\nassumptions on a quadrature rule: one on quadrature weights, and the other on\ndesign points. More precisely, we show that convergence rates can be derived\n(i) if the sum of absolute weights remains constant (or does not increase\nquickly), or (ii) if the minimum distance between design points does not\ndecrease very quickly. As a consequence of the latter result, we derive a rate\nof convergence for Bayesian quadrature in misspecified settings. We reveal a\ncondition on design points to make Bayesian quadrature robust to\nmisspecification, and show that, under this condition, it may adaptively\nachieve the optimal rate of convergence in the Sobolev space of a lesser order\n(i.e., of the unknown smoothness of a test integrand), under a slightly\nstronger regularity condition on the integrand.\n",
"title": "Convergence Analysis of Deterministic Kernel-Based Quadrature Rules in Misspecified Settings"
}
| null | null |
[
"Computer Science",
"Statistics"
] | null | true | null |
16936
| null |
Validated
| null | null |
null |
{
"abstract": " We combine the Bondal-Uehara method for producing exceptional collections on\ntoric varieties with a result of the first author and Favero to expand the set\nof varieties satisfying Orlov's Conjecture on derived dimension.\n",
"title": "The toric Frobenius morphism and a conjecture of Orlov"
}
| null | null | null | null | true | null |
16937
| null |
Default
| null | null |
null |
{
"abstract": " Due to the proliferation of online social networks (OSNs), users find\nthemselves participating in multiple OSNs. These users leave their activity\ntraces as they maintain friendships and interact with other users in these\nOSNs. In this work, we analyze how users maintain friendship in multiple OSNs\nby studying users who have accounts in both Twitter and Instagram.\nSpecifically, we study the similarity of a user's friendship and the evenness\nof friendship distribution in multiple OSNs. Our study shows that most users in\nTwitter and Instagram prefer to maintain different friendships in the two OSNs,\nkeeping only a small clique of common friends in across the OSNs. Based upon\nour empirical study, we conduct link prediction experiments to predict missing\nfriendship links in multiple OSNs using the neighborhood features, neighborhood\nfriendship maintenance features and cross-link features. Our link prediction\nexperiments shows that un- supervised methods can yield good accuracy in\npredicting links in one OSN using another OSN data and the link prediction\naccuracy can be further improved using supervised method with friendship\nmaintenance and others measures as features.\n",
"title": "Friendship Maintenance and Prediction in Multiple Social Networks"
}
| null | null | null | null | true | null |
16938
| null |
Default
| null | null |
null |
{
"abstract": " As deep learning advances, algorithms of music composition increase in\nperformance. However, most of the successful models are designed for specific\nmusical structures. Here, we present BachProp, an algorithmic composer that can\ngenerate music scores in many styles given sufficient training data. To adapt\nBachProp to a broad range of musical styles, we propose a novel representation\nof music and train a deep network to predict the note transition probabilities\nof a given music corpus. In this paper, new music scores generated by BachProp\nare compared with the original corpora as well as with different network\narchitectures and other related models. We show that BachProp captures\nimportant features of the original datasets better than other models and invite\nthe reader to a qualitative comparison on a large collection of generated\nsongs.\n",
"title": "Learning to Generate Music with BachProp"
}
| null | null |
[
"Computer Science"
] | null | true | null |
16939
| null |
Validated
| null | null |
null |
{
"abstract": " Suppose that $Y^n$ is obtained by observing a uniform Bernoulli random vector\n$X^n$ through a binary symmetric channel with crossover probability $\\alpha$.\nThe \"most informative Boolean function\" conjecture postulates that the maximal\nmutual information between $Y^n$ and any Boolean function $\\mathrm{b}(X^n)$ is\nattained by a dictator function. In this paper, we consider the \"complementary\"\ncase in which the Boolean function is replaced by\n$f:\\left\\{0,1\\right\\}^n\\to\\left\\{0,1\\right\\}^{n-1}$, namely, an $n-1$ bit\nquantizer, and show that $I(f(X^n);Y^n)\\leq (n-1)\\cdot\\left(1-h(\\alpha)\\right)$\nfor any such $f$. Thus, in this case, the optimal function is of the form\n$f(x^n)=(x_1,\\ldots,x_{n-1})$.\n",
"title": "How to Quantize $n$ Outputs of a Binary Symmetric Channel to $n-1$ Bits?"
}
| null | null | null | null | true | null |
16940
| null |
Default
| null | null |
null |
{
"abstract": " Supervised deep learning often suffers from the lack of sufficient training\ndata. Specifically in the context of monocular depth map prediction, it is\nbarely possible to determine dense ground truth depth images in realistic\ndynamic outdoor environments. When using LiDAR sensors, for instance, noise is\npresent in the distance measurements, the calibration between sensors cannot be\nperfect, and the measurements are typically much sparser than the camera\nimages. In this paper, we propose a novel approach to depth map prediction from\nmonocular images that learns in a semi-supervised way. While we use sparse\nground-truth depth for supervised learning, we also enforce our deep network to\nproduce photoconsistent dense depth maps in a stereo setup using a direct image\nalignment loss. In experiments we demonstrate superior performance in depth map\nprediction from single images compared to the state-of-the-art methods.\n",
"title": "Semi-Supervised Deep Learning for Monocular Depth Map Prediction"
}
| null | null | null | null | true | null |
16941
| null |
Default
| null | null |
null |
{
"abstract": " Clustering problems are well-studied in a variety of fields such as data\nscience, operations research, and computer science. Such problems include\nvariants of centre location problems, $k$-median, and $k$-means to name a few.\nIn some cases, not all data points need to be clustered; some may be discarded\nfor various reasons.\nWe study clustering problems with outliers. More specifically, we look at\nUncapacitated Facility Location (UFL), $k$-Median, and $k$-Means. In UFL with\noutliers, we have to open some centres, discard up to $z$ points of $\\cal X$\nand assign every other point to the nearest open centre, minimizing the total\nassignment cost plus centre opening costs. In $k$-Median and $k$-Means, we have\nto open up to $k$ centres but there are no opening costs. In $k$-Means, the\ncost of assigning $j$ to $i$ is $\\delta^2(j,i)$. We present several results.\nOur main focus is on cases where $\\delta$ is a doubling metric or is the\nshortest path metrics of graphs from a minor-closed family of graphs. For\nuniform-cost UFL with outliers on such metrics we show that a multiswap simple\nlocal search heuristic yields a PTAS. With a bit more work, we extend this to\nbicriteria approximations for the $k$-Median and $k$-Means problems in the same\nmetrics where, for any constant $\\epsilon > 0$, we can find a solution using\n$(1+\\epsilon)k$ centres whose cost is at most a $(1+\\epsilon)$-factor of the\noptimum and uses at most $z$ outliers. We also show that natural local search\nheuristics that do not violate the number of clusters and outliers for\n$k$-Median (or $k$-Means) will have unbounded gap even in Euclidean metrics.\nFurthermore, we show how our analysis can be extended to general metrics for\n$k$-Means with outliers to obtain a $(25+\\epsilon,1+\\epsilon)$ bicriteria.\n",
"title": "Approximation Schemes for Clustering with Outliers"
}
| null | null |
[
"Computer Science"
] | null | true | null |
16942
| null |
Validated
| null | null |
null |
{
"abstract": " The order preserving pattern matching (OPPM) problem is, given a pattern\nstring $p$ and a text string $t$, find all substrings of $t$ which have the\nsame relative orders as $p$. In this paper, we consider two variants of the\nOPPM problem where a set of text strings is given as a tree or a DAG. We show\nthat the OPPM problem for a single pattern $p$ of length $m$ and a text tree\n$T$ of size $N$ can be solved in $O(m+N)$ time if the characters of $p$ are\ndrawn from an integer alphabet of polynomial size. The time complexity becomes\n$O(m \\log m + N)$ if the pattern $p$ is over a general ordered alphabet. We\nthen show that the OPPM problem for a single pattern and a text DAG is\nNP-complete.\n",
"title": "Order preserving pattern matching on trees and DAGs"
}
| null | null | null | null | true | null |
16943
| null |
Default
| null | null |
null |
{
"abstract": " We present a simple categorical framework for the treatment of probabilistic\ntheories, with the aim of reconciling the fields of Categorical Quantum\nMechanics (CQM) and Operational Probabilistic Theories (OPTs). In recent years,\nboth CQM and OPTs have found successful application to a number of areas in\nquantum foundations and information theory: they present many similarities,\nboth in spirit and in formalism, but they remain separated by a number of\nsubtle yet important differences. We attempt to bridge this gap, by adopting a\nminimal number of operationally motivated axioms which provide clean\ncategorical foundations, in the style of CQM, for the treatment of the problems\nthat OPTs are concerned with.\n",
"title": "Categorical Probabilistic Theories"
}
| null | null |
[
"Mathematics"
] | null | true | null |
16944
| null |
Validated
| null | null |
null |
{
"abstract": " Let $K$ be a standard Hölder continuous Calderón--Zygmund kernel on\n$\\mathbb{R}^{\\mathbf{d}}$ whose truncations define $L^2$ bounded operators. We\nshow that the maximal operator obtained by modulating $K$ by polynomial phases\nof a fixed degree is bounded on $L^p(\\mathbb{R}^{\\mathbf{d}})$ for $1 < p <\n\\infty$. This extends Sjölin's multidimensional Carleson theorem and Lie's\npolynomial Carleson theorem.\n",
"title": "Maximal polynomial modulations of singular integrals"
}
| null | null | null | null | true | null |
16945
| null |
Default
| null | null |
null |
{
"abstract": " The effects of including the Hubbard on-site Coulombic correction to the\nstructural parameters and valence energy states of wurtzite ZnO was explored.\nDue to the changes in the structural parameters caused by correction of\nhybridization between Zn d states and O p states, suitable parameters of\nHubbard terms have to be determined for an accurate prediction of ZnO\nproperties. Using the LDA+${U}$ method by applying Hubbard corrections $U_p$ to\nZn 3d states and $U_p$ to O 2p states, the lattice constants were\nunderestimated for all tested Hubbard parameters. The combination of both $U_d$\nand $U_p$ correction terms managed to widen the band gap of wurtzite ZnO to the\nexperimental value. Pairs of $U_p$ and $U_p$ parameters with the correct\npositioning of d-band and accurate bandwidths were selected, in addition to\npredicting an accurate band gap value. Inspection of vibrational properties,\nhowever, revealed mismatches between the estimated gamma phonon frequencies and\nexperimental values. The selection of Hubbard terms based on electronic band\nproperties alone cannot ensure an accurate vibrational description in LDA+${U}$\ncalculation.\n",
"title": "Effects of Hubbard term correction on the structural parameters and electronic properties of wurtzite Zn"
}
| null | null | null | null | true | null |
16946
| null |
Default
| null | null |
null |
{
"abstract": " We present a construction of a multi-scale Gaussian beam parametrix for the\nDirichlet boundary value problem associated with the wave equation, and study\nits convergence rate to the true solution in the highly oscillatory regime. The\nconstruction elaborates on the wave-atom parametrix of Bao, Qian, Ying, and\nZhang and extends to a multi-scale setting the technique of Gaussian beam\npropagation from a boundary of Katchalov, Kurylev and Lassas.\n",
"title": "A multi-scale Gaussian beam parametrix for the wave equation: the Dirichlet boundary value problem"
}
| null | null | null | null | true | null |
16947
| null |
Default
| null | null |
null |
{
"abstract": " A functional risk curve gives the probability of an undesirable event as a\nfunction of the value of a critical parameter of a considered physical system.\nIn several applicative situations, this curve is built using phenomenological\nnumerical models which simulate complex physical phenomena. To avoid cpu-time\nexpensive numerical models, we propose to use Gaussian process regression to\nbuild functional risk curves. An algorithm is given to provide confidence\nbounds due to this approximation. Two methods of global sensitivity analysis of\nthe models' random input parameters on the functional risk curve are also\nstudied. In particular, the PLI sensitivity indices allow to understand the\neffect of misjudgment on the input parameters' probability density functions.\n",
"title": "Uncertainty and sensitivity analysis of functional risk curves based on Gaussian processes"
}
| null | null |
[
"Mathematics",
"Statistics"
] | null | true | null |
16948
| null |
Validated
| null | null |
null |
{
"abstract": " The paper provides global optimization algorithms for two particularly\ndifficult nonconvex problems raised by hybrid system identification: switching\nlinear regression and bounded-error estimation. While most works focus on local\noptimization heuristics without global optimality guarantees or with guarantees\nvalid only under restrictive conditions, the proposed approach always yields a\nsolution with a certificate of global optimality. This approach relies on a\nbranch-and-bound strategy for which we devise lower bounds that can be\nefficiently computed. In order to obtain scalable algorithms with respect to\nthe number of data, we directly optimize the model parameters in a continuous\noptimization setting without involving integer variables. Numerical experiments\nshow that the proposed algorithms offer a higher accuracy than convex\nrelaxations with a reasonable computational burden for hybrid system\nidentification. In addition, we discuss how bounded-error estimation is related\nto robust estimation in the presence of outliers and exact recovery under\nsparse noise, for which we also obtain promising numerical results.\n",
"title": "Global optimization for low-dimensional switching linear regression and bounded-error estimation"
}
| null | null | null | null | true | null |
16949
| null |
Default
| null | null |
null |
{
"abstract": " During visuomotor tasks, robots must compensate for temporal delays inherent\nin their sensorimotor processing systems. Delay compensation becomes crucial in\na dynamic environment where the visual input is constantly changing, e.g.,\nduring the interacting with a human demonstrator. For this purpose, the robot\nmust be equipped with a prediction mechanism for using the acquired perceptual\nexperience to estimate possible future motor commands. In this paper, we\npresent a novel neural network architecture that learns prototypical visuomotor\nrepresentations and provides reliable predictions on the basis of the visual\ninput. These predictions are used to compensate for the delayed motor behavior\nin an online manner. We investigate the performance of our method with a set of\nexperiments comprising a humanoid robot that has to learn and generate visually\nperceived arm motion trajectories. We evaluate the accuracy in terms of mean\nprediction error and analyze the response of the network to novel movement\ndemonstrations. Additionally, we report experiments with incomplete data\nsequences, showing the robustness of the proposed architecture in the case of a\nnoisy and faulty visual sensor.\n",
"title": "An Incremental Self-Organizing Architecture for Sensorimotor Learning and Prediction"
}
| null | null | null | null | true | null |
16950
| null |
Default
| null | null |
null |
{
"abstract": " In this article, we present a cut finite element method for two-phase\nNavier-Stokes flows. The main feature of the method is the formulation of a\nunified continuous interior penalty stabilisation approach for, on the one\nhand, stabilising advection and the pressure-velocity coupling and, on the\nother hand, stabilising the cut region. The accuracy of the algorithm is\nenhanced by the development of extended fictitious domains to guarantee a well\ndefined velocity from previous time steps in the current geometry. Finally, the\nrobustness of the moving-interface algorithm is further improved by the\nintroduction of a curvature smoothing technique that reduces spurious\nvelocities. The algorithm is shown to perform remarkably well for low capillary\nnumber flows, and is a first step towards flexible and robust CutFEM algorithms\nfor the simulation of microfluidic devices.\n",
"title": "A CutFEM method for two-phase flow problems"
}
| null | null | null | null | true | null |
16951
| null |
Default
| null | null |
null |
{
"abstract": " We explore the problem of learning under selective labels in the context of\nalgorithm-assisted decision making. Selective labels is a pervasive selection\nbias problem that arises when historical decision making blinds us to the true\noutcome for certain instances. Examples of this are common in many\napplications, ranging from predicting recidivism using pre-trial release data\nto diagnosing patients. In this paper we discuss why selective labels often\ncannot be effectively tackled by standard methods for adjusting for sample\nselection bias, even if there are no unobservables. We propose a data\naugmentation approach that can be used to either leverage expert consistency to\nmitigate the partial blindness that results from selective labels, or to\nempirically validate whether learning under such framework may lead to\nunreliable models prone to systemic discrimination.\n",
"title": "Learning under selective labels in the presence of expert consistency"
}
| null | null | null | null | true | null |
16952
| null |
Default
| null | null |
null |
{
"abstract": " We present a model for the evolution of supermassive protostars from their\nformation at $M_\\star \\simeq 0.1\\,\\text{M}_\\odot$ until their growth to\n$M_\\star \\simeq 10^5\\,\\text{M}_\\odot$. To calculate the initial properties of\nthe object in the optically thick regime we follow two approaches: based on\nidealized thermodynamic considerations, and on a more detailed one-zone model.\nBoth methods derive a similar value of $n_{\\rm F} \\simeq 2 \\times 10^{17}\n\\,\\text{cm}^{-3}$ for the density of the object when opacity becomes important,\ni.e. the opacity limit. The subsequent evolution of the growing protostar is\ndetermined by the accretion of gas onto the object and can be described by a\nmass-radius relation of the form $R_\\star \\propto M_\\star^{1/3}$ during the\nearly stages, and of the form $R_\\star \\propto M_\\star^{1/2}$ when internal\nluminosity becomes important. For the case of a supermassive protostar, this\nimplies that the radius of the star grows from $R_\\star \\simeq 0.65 \\,{\\rm AU}$\nto $R_\\star \\simeq 250 \\,{\\rm AU}$ during its evolution. Finally, we use this\nmodel to construct a sub-grid recipe for accreting sink particles in numerical\nsimulations. A prime ingredient thereof is a physically motivated prescription\nfor the accretion radius and the effective temperature of the growing protostar\nembedded inside it. From the latter, we can conclude that photo-ionization\nfeedback can be neglected until very late in the assembly process of the\nsupermassive object.\n",
"title": "Opacity limit for supermassive protostars"
}
| null | null | null | null | true | null |
16953
| null |
Default
| null | null |
null |
{
"abstract": " Prospection is an important part of how humans come up with new task plans,\nbut has not been explored in depth in robotics. Predicting multiple task-level\nis a challenging problem that involves capturing both task semantics and\ncontinuous variability over the state of the world. Ideally, we would combine\nthe ability of machine learning to leverage big data for learning the semantics\nof a task, while using techniques from task planning to reliably generalize to\nnew environment. In this work, we propose a method for learning a model\nencoding just such a representation for task planning. We learn a neural net\nthat encodes the $k$ most likely outcomes from high level actions from a given\nworld. Our approach creates comprehensible task plans that allow us to predict\nchanges to the environment many time steps into the future. We demonstrate this\napproach via application to a stacking task in a cluttered environment, where\nthe robot must select between different colored blocks while avoiding\nobstacles, in order to perform a task. We also show results on a simple\nnavigation task. Our algorithm generates realistic image and pose predictions\nat multiple points in a given task.\n",
"title": "Learning to Imagine Manipulation Goals for Robot Task Planning"
}
| null | null | null | null | true | null |
16954
| null |
Default
| null | null |
null |
{
"abstract": " The classical Weisfeiler-Lehman method WL[2] uses edge colors to produce a\npowerful graph invariant. It is at least as powerful in its ability to\ndistinguish non-isomorphic graphs as the most prominent algebraic graph\ninvariants. It determines not only the spectrum of a graph, and the angles\nbetween standard basis vectors and the eigenspaces, but even the angles between\nprojections of standard basis vectors into the eigenspaces. Here, we\ninvestigate the combinatorial power of WL[2]. For sufficiently large k, WL[k]\ndetermines all combinatorial properties of a graph. Many traditionally used\ncombinatorial invariants are determined by WL[k] for small k. We focus on two\nfundamental invariants, the num- ber of cycles Cp of length p, and the number\nof cliques Kp of size p. We show that WL[2] determines the number of cycles of\nlengths up to 6, but not those of length 8. Also, WL[2] does not determine the\nnumber of 4-cliques.\n",
"title": "On the Combinatorial Power of the Weisfeiler-Lehman Algorithm"
}
| null | null | null | null | true | null |
16955
| null |
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| null | null |
null |
{
"abstract": " We explore the properties of byte-level recurrent language models. When given\nsufficient amounts of capacity, training data, and compute time, the\nrepresentations learned by these models include disentangled features\ncorresponding to high-level concepts. Specifically, we find a single unit which\nperforms sentiment analysis. These representations, learned in an unsupervised\nmanner, achieve state of the art on the binary subset of the Stanford Sentiment\nTreebank. They are also very data efficient. When using only a handful of\nlabeled examples, our approach matches the performance of strong baselines\ntrained on full datasets. We also demonstrate the sentiment unit has a direct\ninfluence on the generative process of the model. Simply fixing its value to be\npositive or negative generates samples with the corresponding positive or\nnegative sentiment.\n",
"title": "Learning to Generate Reviews and Discovering Sentiment"
}
| null | null | null | null | true | null |
16956
| null |
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| null | null |
null |
{
"abstract": " Combining material informatics and high-throughput electronic structure\ncalculations offers the possibility of a rapid characterization of complex\nmagnetic materials. Here we demonstrate that datasets of electronic properties\ncalculated at the ab initio level can be effectively used to identify and\nunderstand physical trends in magnetic materials, thus opening new avenues for\naccelerated materials discovery. Following a data-centric approach, we utilize\na database of Heusler alloys calculated at the density functional theory level\nto identify the ideal ions neighbouring Fe in the $X_2$Fe$Z$ Heusler prototype.\nThe hybridization of Fe with the nearest neighbour $X$ ion is found to cause\nredistribution of the on-site Fe charge and a net increase of its magnetic\nmoment proportional to the valence of $X$. Thus, late transition metals are\nideal Fe neighbours for producing high-moment Fe-based Heusler magnets. At the\nsame time a thermodynamic stability analysis is found to restrict $Z$ to main\ngroup elements. Machine learning regressors, trained to predict magnetic moment\nand volume of Heusler alloys, are used to determine the magnetization for all\nmaterials belonging to the proposed prototype. We find that Co$_2$Fe$Z$ alloys,\nand in particular Co$_2$FeSi, maximize the magnetization, which reaches values\nup to 1.2T. This is in good agreement with both ab initio and experimental\ndata. Furthermore, we identify the Cu$_2$Fe$Z$ family to be a cost-effective\nmaterials class, offering a magnetization of approximately 0.65T.\n",
"title": "Designing magnetism in Fe-based Heusler alloys: a machine learning approach"
}
| null | null | null | null | true | null |
16957
| null |
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| null | null |
null |
{
"abstract": " We study a non-local variant of a diffuse interface model proposed by\nHawkins--Darrud et al. (2012) for tumour growth in the presence of a chemical\nspecies acting as nutrient. The system consists of a Cahn--Hilliard equation\ncoupled to a reaction-diffusion equation. For non-degenerate mobilities and\nsmooth potentials, we derive well-posedness results, which are the non-local\nanalogue of those obtained in Frigeri et al. (European J. Appl. Math. 2015).\nFurthermore, we establish existence of weak solutions for the case of\ndegenerate mobilities and singular potentials, which serves to confine the\norder parameter to its physically relevant interval. Due to the non-local\nnature of the equations, under additional assumptions continuous dependence on\ninitial data can also be shown.\n",
"title": "On a diffuse interface model for tumour growth with non-local interactions and degenerate mobilities"
}
| null | null | null | null | true | null |
16958
| null |
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| null | null |
null |
{
"abstract": " Although gradient descent (GD) almost always escapes saddle points\nasymptotically [Lee et al., 2016], this paper shows that even with fairly\nnatural random initialization schemes and non-pathological functions, GD can be\nsignificantly slowed down by saddle points, taking exponential time to escape.\nOn the other hand, gradient descent with perturbations [Ge et al., 2015, Jin et\nal., 2017] is not slowed down by saddle points - it can find an approximate\nlocal minimizer in polynomial time. This result implies that GD is inherently\nslower than perturbed GD, and justifies the importance of adding perturbations\nfor efficient non-convex optimization. While our focus is theoretical, we also\npresent experiments that illustrate our theoretical findings.\n",
"title": "Gradient Descent Can Take Exponential Time to Escape Saddle Points"
}
| null | null | null | null | true | null |
16959
| null |
Default
| null | null |
null |
{
"abstract": " Let $L$ be the $n$-th order linear differential operator $Ly = \\phi_0y^{(n)}\n+ \\phi_1y^{(n-1)} + \\cdots + \\phi_ny$ with variable coefficients. A\nrepresentation is given for $n$ linearly independent solutions of $Ly=\\lambda r\ny$ as power series in $\\lambda$, generalizing the SPPS (spectral parameter\npower series) solution which has been previously developed for $n=2$. The\ncoefficient functions in these series are obtained by recursively iterating a\nsimple integration process, begining with a solution system for $\\lambda=0$. It\nis shown how to obtain such an initializing system working upwards from\nequations of lower order. The values of the successive derivatives of the power\nseries solutions at the basepoint of integration are given, which provides a\ntechnique for numerical solution of $n$-th order initial value problems and\nspectral problems.\n",
"title": "Spectral parameter power series for arbitrary order linear differential equations"
}
| null | null |
[
"Mathematics"
] | null | true | null |
16960
| null |
Validated
| null | null |
null |
{
"abstract": " The traditional humanism of the twentieth century, inspired by the culture of\nthe book, systematically distanced itself from the new society of digital\ninformation; the Internet and tools of information processing revolutionized\nthe world, society during this period developed certain adaptive\ncharacteristics based on coexistence (Human - Machine), this transformation\nsets based on the impact of three technology segments: devices, applications\nand infrastructure of social communication, which are involved in various\nphysical, behavioural and cognitive changes of the human being; and the\nemergence of new models of influence and social control through the new\nubiquitous communication; however in this new process of conviviality new\nmodels like the \"collaborative thinking\" and \"InfoSharing\" develop; managing\nsocial information under three Human ontological dimensions (h) - Information\n(i) - Machine (m), which is the basis of a new physical-cyber ecosystem, where\nthey coexist and develop new social units called \"virtual communities \". This\nnew communication infrastructure and social management of information given\ndiscovered areas of vulnerability \"social perspective of risk\", impacting all\nsocial units through massive impact vector (i); The virtual environment \"H + i\n+ M\"; and its components, as well as the life cycle management of social\ninformation allows us to understand the path of integration \"Techno - Social\"\nand setting a new contribution to cybernetics, within the convergence of\ntechnology with society and the new challenges of coexistence, aimed at a new\nholistic and not pragmatic vision, as the human component (h) in the virtual\nenvironment is the precursor of the future and needs to be studied not as an\napplication, but as the hub of a new society.\n",
"title": "Antropologia de la Informatica Social: Teoria de la Convergencia Tecno-Social"
}
| null | null | null | null | true | null |
16961
| null |
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| null | null |
null |
{
"abstract": " Loss functions with a large number of saddle points are one of the main\nobstacles to training many modern machine learning models. Gradient descent\n(GD) is a fundamental algorithm for machine learning and converges to a saddle\npoint for certain initial data. We call the region formed by these initial\nvalues the \"attraction region.\" For quadratic functions, GD converges to a\nsaddle point if the initial data is in a subspace of up to n-1 dimensions. In\nthis paper, we prove that a small modification of the recently proposed\nLaplacian smoothing gradient descent (LSGD) [Osher, et al., arXiv:1806.06317]\ncontributes to avoiding saddle points without sacrificing the convergence rate\nof GD. In particular, we show that the dimension of the LSGD's attraction\nregion is at most floor((n-1)/2) for a class of quadratic functions which is\nsignificantly smaller than GD's (n-1)-dimensional attraction region.\n",
"title": "A Deterministic Approach to Avoid Saddle Points"
}
| null | null |
[
"Computer Science",
"Statistics"
] | null | true | null |
16962
| null |
Validated
| null | null |
null |
{
"abstract": " This paper addresses the automatic generation of a typographic font from a\nsubset of characters. Specifically, we use a subset of a typographic font to\nextrapolate additional characters. Consequently, we obtain a complete font\ncontaining a number of characters sufficient for daily use. The automated\ngeneration of Japanese fonts is in high demand because a Japanese font requires\nover 1,000 characters. Unfortunately, professional typographers create most\nfonts, resulting in significant financial and time investments for font\ngeneration. The proposed method can be a great aid for font creation because\ndesigners do not need to create the majority of the characters for a new font.\nThe proposed method uses strokes from given samples for font generation. The\nstrokes, from which we construct characters, are extracted by exploiting a\ncharacter skeleton dataset. This study makes three main contributions: a novel\nmethod of extracting strokes from characters, which is applicable to both\nstandard fonts and their variations; a fully automated approach for\nconstructing characters; and a selection method for sample characters. We\ndemonstrate our proposed method by generating 2,965 characters in 47 fonts.\nObjective and subjective evaluations verify that the generated characters are\nsimilar to handmade characters.\n",
"title": "Automatic Generation of Typographic Font from a Small Font Subset"
}
| null | null | null | null | true | null |
16963
| null |
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| null | null |
null |
{
"abstract": " The article deals with the connection between the second postulate of Euclid\nand non-Euclidean geometry. It is shown that the violation of the second\npostulate of Euclid inevitably leads to hyperbolic geometry. This eliminates\nmisunderstandings about the sums of some divergent series. The connection\nbetween hyperbolic geometry and relativistic computations is noted.\n",
"title": "The Second Postulate of Euclid and the Hyperbolic Geometry"
}
| null | null | null | null | true | null |
16964
| null |
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| null | null |
null |
{
"abstract": " Modern mobile and embedded platforms see a large number of ephemeral tasks\ndriven by background activities. In order to execute such a task, the OS kernel\nwakes up the platform beforehand and puts it back to sleep afterwards. In doing\nso, the kernel operates various IO devices and orchestrates their power state\ntransitions. Such kernel execution phases are lengthy, having high energy cost,\nand yet difficult to optimize. We advocate for relieving the CPU from these\nkernel phases by executing them on a low-power, microcontroller-like core,\ndubbed peripheral core, hence leaving the CPU off. Yet, for a peripheral core\nto execute phases in a complex commodity kernel (e.g. Linux), existing\napproaches either incur high engineering effort or high runtime overhead. We\ntake a radical approach with a new executor model called transkernel. Running\non a peripheral core, a transkernel executes the binary of the commodity kernel\nthrough cross-ISA, dynamic binary translation (DBT). The transkernel translates\nstateful kernel code while emulating a small set of stateless kernel services;\nit sets a narrow, stable binary interface for emulated services; it specializes\nfor kernel's beaten paths; it exploits ISA similarities for low DBT cost. With\na concrete implementation on a heterogeneous ARM SoC, we demonstrate the\nfeasibility and benefit of transkernel. Our result contributes a new OS\nstructure that combines cross-ISA DBT and emulation for harnessing a\nheterogeneous SoC. Our result demonstrates that while cross-ISA DBT is\ntypically used under the assumption of efficiency loss, it can be used for\nefficiency gain, even atop off-the-shelf hardware.\n",
"title": "Transkernel: An Executor for Commodity Kernels on Peripheral Cores"
}
| null | null | null | null | true | null |
16965
| null |
Default
| null | null |
null |
{
"abstract": " We show that there is no iterated identity satisfied by all finite groups.\nFor $w$ being a non-trivial word of length $l$, we show that there exists a\nfinite group $G$ of cardinality at most $\\exp(l^C)$ which does not satisfy the\niterated identity $w$. The proof uses the approach of Borisov and Sapir, who\nused dynamics of polynomial mappings for the proof of non residual finiteness\nof some groups.\n",
"title": "No iterated identities satisfied by all finite groups"
}
| null | null |
[
"Mathematics"
] | null | true | null |
16966
| null |
Validated
| null | null |
null |
{
"abstract": " The goal of this tutorial is to introduce key models, algorithms, and open\nquestions related to the use of optimization methods for solving problems\narising in machine learning. It is written with an INFORMS audience in mind,\nspecifically those readers who are familiar with the basics of optimization\nalgorithms, but less familiar with machine learning. We begin by deriving a\nformulation of a supervised learning problem and show how it leads to various\noptimization problems, depending on the context and underlying assumptions. We\nthen discuss some of the distinctive features of these optimization problems,\nfocusing on the examples of logistic regression and the training of deep neural\nnetworks. The latter half of the tutorial focuses on optimization algorithms,\nfirst for convex logistic regression, for which we discuss the use of\nfirst-order methods, the stochastic gradient method, variance reducing\nstochastic methods, and second-order methods. Finally, we discuss how these\napproaches can be employed to the training of deep neural networks, emphasizing\nthe difficulties that arise from the complex, nonconvex structure of these\nmodels.\n",
"title": "Optimization Methods for Supervised Machine Learning: From Linear Models to Deep Learning"
}
| null | null | null | null | true | null |
16967
| null |
Default
| null | null |
null |
{
"abstract": " Partial Least Squares (PLS) methods have been heavily exploited to analyse\nthe association between two blocs of data. These powerful approaches can be\napplied to data sets where the number of variables is greater than the number\nof observations and in presence of high collinearity between variables.\nDifferent sparse versions of PLS have been developed to integrate multiple data\nsets while simultaneously selecting the contributing variables. Sparse\nmodelling is a key factor in obtaining better estimators and identifying\nassociations between multiple data sets. The cornerstone of the sparsity\nversion of PLS methods is the link between the SVD of a matrix (constructed\nfrom deflated versions of the original matrices of data) and least squares\nminimisation in linear regression. We present here an accurate description of\nthe most popular PLS methods, alongside their mathematical proofs. A unified\nalgorithm is proposed to perform all four types of PLS including their\nregularised versions. Various approaches to decrease the computation time are\noffered, and we show how the whole procedure can be scalable to big data sets.\n",
"title": "A Unified Parallel Algorithm for Regularized Group PLS Scalable to Big Data"
}
| null | null | null | null | true | null |
16968
| null |
Default
| null | null |
null |
{
"abstract": " We study the asymptotic behaviour of the solutions of the fifth Painlevé\nequation as the independent variable approaches zero and infinity in the space\nof initial values. We show that the limit set of each solution is compact and\nconnected and, moreover, that any solution with the essential singularity at\nzero has an infinite number of poles and zeroes, and any solution with the\nessential singularity at infinity has infinite number of poles and takes value\n$1$ infinitely many times.\n",
"title": "Asymptotic behaviour of the fifth Painlevé transcendents in the space of initial values"
}
| null | null |
[
"Physics",
"Mathematics"
] | null | true | null |
16969
| null |
Validated
| null | null |
null |
{
"abstract": " Internet-wide scans are a common active measurement approach to study the\nInternet, e.g., studying security properties or protocol adoption. They involve\nprobing large address ranges (IPv4 or parts of IPv6) for specific ports or\nprotocols. Besides their primary use for probing (e.g., studying protocol\nadoption), we show that - at the same time - they provide valuable insights\ninto the Internet control plane informed by ICMP responses to these probes - a\ncurrently unexplored secondary use. We collect one week of ICMP responses\n(637.50M messages) to several Internet-wide ZMap scans covering multiple TCP\nand UDP ports as well as DNS-based scans covering > 50% of the domain name\nspace. This perspective enables us to study the Internet's control plane as a\nby-product of Internet measurements. We receive ICMP messages from ~171M\ndifferent IPs in roughly 53K different autonomous systems. Additionally, we\nuncover multiple control plane problems, e.g., we detect a plethora of outdated\nand misconfigured routers and uncover the presence of large-scale persistent\nrouting loops in IPv4.\n",
"title": "Hidden Treasures - Recycling Large-Scale Internet Measurements to Study the Internet's Control Plane"
}
| null | null |
[
"Computer Science"
] | null | true | null |
16970
| null |
Validated
| null | null |
null |
{
"abstract": " We present a method for metric optimization in the Large Deformation\nDiffeomorphic Metric Mapping (LDDMM) framework, by treating the induced\nRiemannian metric on the space of diffeomorphisms as a kernel in a machine\nlearning context. For simplicity, we choose the kernel Fischer Linear\nDiscriminant Analysis (KLDA) as the framework. Optimizing the kernel parameters\nin an Expectation-Maximization framework, we define model fidelity via the\nhinge loss of the decision function. The resulting algorithm optimizes the\nparameters of the LDDMM norm-inducing differential operator as a solution to a\ngroup-wise registration and classification problem. In practice, this may lead\nto a biology-aware registration, focusing its attention on the predictive task\nat hand such as identifying the effects of disease. We first tested our\nalgorithm on a synthetic dataset, showing that our parameter selection improves\nregistration quality and classification accuracy. We then tested the algorithm\non 3D subcortical shapes from the Schizophrenia cohort Schizconnect. Our\nSchizpohrenia-Control predictive model showed significant improvement in ROC\nAUC compared to baseline parameters.\n",
"title": "Image Registration and Predictive Modeling: Learning the Metric on the Space of Diffeomorphisms"
}
| null | null | null | null | true | null |
16971
| null |
Default
| null | null |
null |
{
"abstract": " We suggested an algorithm for searching the recursion operators for nonlinear\nintegrable equations. It was observed that the recursion operator $R$ can be\nrepresented as a ratio of the form $R=L_1^{-1}L_2$ where the linear\ndifferential operators $L_1$ and $L_2$ are chosen in such a way that the\nordinary differential equation $(L_2-\\lambda L_1)U=0$ is consistent with the\nlinearization of the given nonlinear integrable equation for any value of the\nparameter $\\lambda\\in \\textbf{C}$. For constructing the operator $L_1$ we use\nthe concept of the invariant manifold which is a generalization of the\nsymmetry. Then for searching $L_2$ we take an auxiliary linear equation\nconnected with the linearized equation by the Darboux transformation.\nConnection of the invariant manifold with the Lax pairs and the\nDubrovin-Weierstrass equations is discussed.\n",
"title": "On a direct algorithm for constructing recursion operators and Lax pairs for integrable models"
}
| null | null | null | null | true | null |
16972
| null |
Default
| null | null |
null |
{
"abstract": " To the best of our knowledge, this paper presents the first large-scale study\nthat tests whether network categories (e.g., social networks vs. web graphs)\nare distinguishable from one another (using both categories of real-world\nnetworks and synthetic graphs). A classification accuracy of $94.2\\%$ was\nachieved using a random forest classifier with both real and synthetic\nnetworks. This work makes two important findings. First, real-world networks\nfrom various domains have distinct structural properties that allow us to\npredict with high accuracy the category of an arbitrary network. Second,\nclassifying synthetic networks is trivial as our models can easily distinguish\nbetween synthetic graphs and the real-world networks they are supposed to\nmodel.\n",
"title": "Network Classification and Categorization"
}
| null | null |
[
"Computer Science",
"Statistics"
] | null | true | null |
16973
| null |
Validated
| null | null |
null |
{
"abstract": " Many complex systems in biology, physics, and engineering include a large\nnumber of state-variables, and measuring the full state of the system is often\nimpossible. Typically, a set of sensors is used to measure part of the\nstate-variables. A system is called observable if these measurements allow to\nreconstruct the entire state of the system. When the system is not observable,\nan important and practical problem is how to add a \\emph{minimal} number of\nsensors so that the system becomes observable. This minimal observability\nproblem is practically useful and theoretically interesting, as it pinpoints\nthe most informative nodes in the system. We consider the minimal observability\nproblem for an important special class of Boolean networks, called conjunctive\nBoolean networks (CBNs). Using a graph-theoretic approach, we provide a\nnecessary and sufficient condition for observability of a CBN with $n$\nstate-variables, and an efficient~$O(n^2)$-time algorithm for solving the\nminimal observability problem. We demonstrate the usefulness of these results\nby studying the properties of a class of random CBNs.\n",
"title": "A Polynomial-Time Algorithm for Solving the Minimal Observability Problem in Conjunctive Boolean Networks"
}
| null | null | null | null | true | null |
16974
| null |
Default
| null | null |
null |
{
"abstract": " A second derivative-based moment method is proposed for describing the\nthickness and shape of the region where viscous forces are dominant in\nturbulent boundary layer flows. Rather than the fixed location sublayer model\npresently employed, the new method defines thickness and shape parameters that\nare experimentally accessible without differentiation. It is shown\ntheoretically that one of the new length parameters used as a scaling parameter\nis also a similarity parameter for the velocity profile. In fact, we show that\nthis new length scale parameter removes one of the theoretical inconsistencies\npresent in the traditional Prandtl Plus scalings. Furthermore, the new length\nparameter and the Prandtl Plus scaling parameters perform identically when\noperating on experimental datasets. This means that many of the past successes\nascribed to the Prandtl Plus scaling also apply to the new parameter set but\nwithout one of the theoretical inconsistencies. Examples are offered to show\nhow the new description method is useful in exploring the actual physics of the\nboundary layer.\n",
"title": "The Description and Scaling Behavior for the Inner Region of the Boundary Layer for 2-D Wall-bounded Flows"
}
| null | null |
[
"Physics"
] | null | true | null |
16975
| null |
Validated
| null | null |
null |
{
"abstract": " A sequence in a $C^*$-algebra $A$ is called completely Sidon if its span in\n$A$ is completely isomorphic to the operator space version of the space\n$\\ell_1$ (i.e. $\\ell_1$ equipped with its maximal operator space structure).\nThe latter can also be described as the span of the free unitary generators in\nthe (full) $C^*$-algebra of the free group $\\F_\\infty$ with countably\ninfinitely many generators. Our main result is a generalization to this context\nof Drury's classical theorem stating that Sidon sets are stable under finite\nunions. In the particular case when $A=C^*(G)$ the (maximal) $C^*$-algebra of a\ndiscrete group $G$, we recover the non-commutative (operator space) version of\nDrury's theorem that we recently proved. We also give several non-commutative\ngeneralizations of our recent work on uniformly bounded orthonormal systems to\nthe case of von Neumann algebras equipped with normal faithful tracial states.\n",
"title": "Completely Sidon sets in $C^*$-algebras (New title)"
}
| null | null | null | null | true | null |
16976
| null |
Default
| null | null |
null |
{
"abstract": " A conflict-free k-coloring of a graph assigns one of k different colors to\nsome of the vertices such that, for every vertex v, there is a color that is\nassigned to exactly one vertex among v and v's neighbors. Such colorings have\napplications in wireless networking, robotics, and geometry, and are\nwell-studied in graph theory. Here we study the natural problem of the\nconflict-free chromatic number chi_CF(G) (the smallest k for which\nconflict-free k-colorings exist). We provide results both for closed\nneighborhoods N[v], for which a vertex v is a member of its neighborhood, and\nfor open neighborhoods N(v), for which vertex v is not a member of its\nneighborhood.\nFor closed neighborhoods, we prove the conflict-free variant of the famous\nHadwiger Conjecture: If an arbitrary graph G does not contain K_{k+1} as a\nminor, then chi_CF(G) <= k. For planar graphs, we obtain a tight worst-case\nbound: three colors are sometimes necessary and always sufficient. We also give\na complete characterization of the computational complexity of conflict-free\ncoloring. Deciding whether chi_CF(G)<= 1 is NP-complete for planar graphs G,\nbut polynomial for outerplanar graphs. Furthermore, deciding whether\nchi_CF(G)<= 2 is NP-complete for planar graphs G, but always true for\nouterplanar graphs. For the bicriteria problem of minimizing the number of\ncolored vertices subject to a given bound k on the number of colors, we give a\nfull algorithmic characterization in terms of complexity and approximation for\nouterplanar and planar graphs.\nFor open neighborhoods, we show that every planar bipartite graph has a\nconflict-free coloring with at most four colors; on the other hand, we prove\nthat for k in {1,2,3}, it is NP-complete to decide whether a planar bipartite\ngraph has a conflict-free k-coloring. Moreover, we establish that any general}\nplanar graph has a conflict-free coloring with at most eight colors.\n",
"title": "Conflict-Free Coloring of Planar Graphs"
}
| null | null | null | null | true | null |
16977
| null |
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| null | null |
null |
{
"abstract": " We study the problem of utility maximization from terminal wealth in which an\nagent optimally builds her portfolio by investing in a bond and a risky asset.\nThe asset price dynamics follow a diffusion process with regime-switching\ncoefficients modeled by a continuous-time finite-state Markov chain. We\nconsider an investor with a Constant Relative Risk Aversion (CRRA) utility\nfunction. We deduce the associated Hamilton-Jacobi-Bellman equation to\nconstruct the solution and the optimal trading strategy and verify optimality\nby showing that the value function is the unique constrained viscosity solution\nof the HJB equation. By means of a Laplace transform method, we show how to\nexplicitly compute the value function and illustrate the method with the two-\nand three-states cases. This method is interesting in its own right and can be\nadapted in other applications involving hybrid systems and using other types of\ntransforms with basic properties similar to the Laplace transform.\n",
"title": "Explicit solutions to utility maximization problems in a regime-switching market model via Laplace transforms"
}
| null | null | null | null | true | null |
16978
| null |
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| null | null |
null |
{
"abstract": " Globular clusters (GCs) are amongst the oldest objects in the Galaxy and play\na pivotal role in deciphering its early history. We present the first\nspectroscopic study of the GC ESO452-SC11 using the AAOmega spectrograph at\nmedium resolution. Given the sparsity of this object and high degree of\nforeground contamination due to its location toward the bulge, few details are\nknown for this cluster: there is no consensus of its age, metallicity, or its\nassociation with the disk or bulge. We identify 5 members based on radial\nvelocity, metallicity, and position within the GC. Using spectral synthesis,\naccurate abundances of Fe and several $\\alpha$-, Fe-peak, neutron-capture\nelements (Si,Ca,Ti,Cr,Co,Ni,Sr,Eu) were measured. Two of the 5 cluster\ncandidates are likely non-members, as they have deviant Fe abundances and\n[$\\alpha$/Fe] ratios. The mean radial velocity is 19$\\pm$2 km s$^{-1}$ with a\nlow dispersion of 2.8$\\pm$3.4 km s$^{-1}$, in line with its low mass. The mean\nFe-abundance from spectral fitting is $-0.88\\pm0.03$, with a spread driven by\nobservational errors. The $\\alpha$-elements of the GC candidates are marginally\nlower than expected for the bulge at similar metallicities. As spectra of\nhundreds of stars were collected in a 2 degree field around ESO452-SC11,\ndetailed abundances in the surrounding field were measured. Most non-members\nhave higher [$\\alpha$/Fe] ratios, typical of the nearby bulge population. Stars\nwith measured Fe-peak abundances show a large scatter around Solar values,\nthough with large uncertainties. Our study provides the first systematic\nmeasurement of Sr in a Galactic bulge GC. The Eu and Sr abundances of the GC\ncandidates are consistent with a disk or bulge association. Our calculations\nplace ESO452 on an elliptical orbit in the central 3 kpc of the bulge. We find\nno evidence of extratidal stars in our data. (Abridged)\n",
"title": "Spectroscopic study of the elusive globular cluster ESO452-SC11 and its surroundings"
}
| null | null |
[
"Physics"
] | null | true | null |
16979
| null |
Validated
| null | null |
null |
{
"abstract": " Given an infinity-category C, one can naturally construct an\ninfinity-category Fam(C) of families of objects in C indexed by\ninfinity-groupoids. An ordinary categorical version of this construction was\nused by Borceux and Janelidze in the study of generalized covering maps in\ncategorical Galois theory. In this paper, we develop the homotopy theory of\nsuch \"parametrized families\" as generalization of the classical homotopy theory\nof spaces. In particular, we study homotopy-theoretical constructions that\narise from the fundamental infinity-groupoids of families in an\ninfinity-category. In the same spirit, we show that Fam(C) admits a\nGrothendieck topology which generalizes the canonical/epimorphism topology on\nthe infinity-topos of infinity-groupoids in the sense of Carchedi.\n",
"title": "The Fundamental Infinity-Groupoid of a Parametrized Family"
}
| null | null | null | null | true | null |
16980
| null |
Default
| null | null |
null |
{
"abstract": " Training 3D object detectors for autonomous driving has been limited to small\ndatasets due to the effort required to generate annotations. Reducing both task\ncomplexity and the amount of task switching done by annotators is key to\nreducing the effort and time required to generate 3D bounding box annotations.\nThis paper introduces a novel ground truth generation method that combines\nhuman supervision with pretrained neural networks to generate per-instance 3D\npoint cloud segmentation, 3D bounding boxes, and class annotations. The\nannotators provide object anchor clicks which behave as a seed to generate\ninstance segmentation results in 3D. The points belonging to each instance are\nthen used to regress object centroids, bounding box dimensions, and object\norientation. Our proposed annotation scheme requires 30x lower human annotation\ntime. We use the KITTI 3D object detection dataset to evaluate the efficiency\nand the quality of our annotation scheme. We also test the the proposed scheme\non previously unseen data from the Autonomoose self-driving vehicle to\ndemonstrate generalization capabilities of the network.\n",
"title": "Leveraging Pre-Trained 3D Object Detection Models For Fast Ground Truth Generation"
}
| null | null |
[
"Statistics"
] | null | true | null |
16981
| null |
Validated
| null | null |
null |
{
"abstract": " We study the changes of opinions about vaccination together with the\nevolution of a disease. In our model we consider a multiplex network consisting\nof two layers. One of the layers corresponds to a social network where people\nshare their opinions and influence others opinions. The social model that rules\nthe dynamic is the M-model, which takes into account two different processes\nthat occurs in a society: persuasion and compromise. This two processes are\nrelated through a parameter $r$, $r<1$ describes a moderate and committed\nsociety, for $r>1$ the society tends to have extremist opinions, while $r=1$\nrepresents a neutral society. This social network may be of real or virtual\ncontacts. On the other hand, the second layer corresponds to a network of\nphysical contacts where the disease spreading is described by the SIR-Model. In\nthis model the individuals may be in one of the following four states:\nSusceptible ($S$), Infected($I$), Recovered ($R$) or Vaccinated ($V$). A\nSusceptible individual can: i) get vaccinated, if his opinion in the other\nlayer is totally in favor of the vaccine, ii) get infected, with probability\n$\\beta$ if he is in contact with an infected neighbor. Those $I$ individuals\nrecover after a certain period $t_r=6$. Vaccinated individuals have an\nextremist positive opinion that does not change. We consider that the vaccine\nhas a certain effectiveness $\\omega$ and as a consequence vaccinated nodes can\nbe infected with probability $\\beta (1 - \\omega)$ if they are in contact with\nan infected neighbor. In this case, if the infection process is successful, the\nnew infected individual changes his opinion from extremist positive to totally\nagainst the vaccine. We find that depending on the trend in the opinion of the\nsociety, which depends on $r$, different behaviors in the spread of the\nepidemic occurs. An epidemic threshold was found.\n",
"title": "Epidemic spreading in multiplex networks influenced by opinion exchanges on vaccination"
}
| null | null | null | null | true | null |
16982
| null |
Default
| null | null |
null |
{
"abstract": " CASP is an extension of ASP that allows for numerical constraints to be added\nin the rules. PDDL+ is an extension of the PDDL standard language of automated\nplanning for modeling mixed discrete-continuous dynamics.\nIn this paper, we present CASP solutions for dealing with PDDL+ problems,\ni.e., encoding from PDDL+ to CASP, and extensions to the algorithm of the EZCSP\nCASP solver in order to solve CASP programs arising from PDDL+ domains. An\nexperimental analysis, performed on well-known linear and non-linear variants\nof PDDL+ domains, involving various configurations of the EZCSP solver, other\nCASP solvers, and PDDL+ planners, shows the viability of our solution.\n",
"title": "CASP Solutions for Planning in Hybrid Domains"
}
| null | null |
[
"Computer Science"
] | null | true | null |
16983
| null |
Validated
| null | null |
null |
{
"abstract": " We study production of primordial black holes (PBHs) during an early\nmatter-dominated phase. As a source of perturbations, we consider either the\ninflaton field with a running spectral index or a spectator field that has a\nblue spectrum and thus provides a significant contribution to the PBH\nproduction at small scales. First, we identify the region of the parameter\nspace where a significant fraction of the observed dark matter can be produced,\ntaking into account all current PBH constraints. Then, we present constraints\non the amplitude and spectral index of the spectator field as a function of the\nreheating temperature. We also derive constraints on the running of the\ninflaton spectral index, ${\\rm d}n/{\\rm d}{\\rm ln}k \\lesssim -0.002$, which are\ncomparable to those from the Planck satellite for a scenario where the\nspectator field is absent.\n",
"title": "Primordial black holes from inflaton and spectator field perturbations in a matter-dominated era"
}
| null | null | null | null | true | null |
16984
| null |
Default
| null | null |
null |
{
"abstract": " Typical reinforcement learning (RL) agents learn to complete tasks specified\nby reward functions tailored to their domain. As such, the policies they learn\ndo not generalize even to similar domains. To address this issue, we develop a\nframework through which a deep RL agent learns to generalize policies from\nsmaller, simpler domains to more complex ones using a recurrent attention\nmechanism. The task is presented to the agent as an image and an instruction\nspecifying the goal. This meta-controller guides the agent towards its goal by\ndesigning a sequence of smaller subtasks on the part of the state space within\nthe attention, effectively decomposing it. As a baseline, we consider a setup\nwithout attention as well. Our experiments show that the meta-controller learns\nto create subgoals within the attention.\n",
"title": "State Space Decomposition and Subgoal Creation for Transfer in Deep Reinforcement Learning"
}
| null | null |
[
"Computer Science",
"Statistics"
] | null | true | null |
16985
| null |
Validated
| null | null |
null |
{
"abstract": " Deep generative models have recently shown great promise in imitation\nlearning for motor control. Given enough data, even supervised approaches can\ndo one-shot imitation learning; however, they are vulnerable to cascading\nfailures when the agent trajectory diverges from the demonstrations. Compared\nto purely supervised methods, Generative Adversarial Imitation Learning (GAIL)\ncan learn more robust controllers from fewer demonstrations, but is inherently\nmode-seeking and more difficult to train. In this paper, we show how to combine\nthe favourable aspects of these two approaches. The base of our model is a new\ntype of variational autoencoder on demonstration trajectories that learns\nsemantic policy embeddings. We show that these embeddings can be learned on a 9\nDoF Jaco robot arm in reaching tasks, and then smoothly interpolated with a\nresulting smooth interpolation of reaching behavior. Leveraging these policy\nrepresentations, we develop a new version of GAIL that (1) is much more robust\nthan the purely-supervised controller, especially with few demonstrations, and\n(2) avoids mode collapse, capturing many diverse behaviors when GAIL on its own\ndoes not. We demonstrate our approach on learning diverse gaits from\ndemonstration on a 2D biped and a 62 DoF 3D humanoid in the MuJoCo physics\nenvironment.\n",
"title": "Robust Imitation of Diverse Behaviors"
}
| null | null | null | null | true | null |
16986
| null |
Default
| null | null |
null |
{
"abstract": " In this paper we discuss the possible usage of the compressive sampling based\nwavelet analysis for the efficient measurement and for the early detection of\none dimensional (1D) vibrational rogue waves. We study the construction of the\ntriangular (V-shaped) wavelet spectra using compressive samples of rogue waves\nthat can be modeled as Peregrine and Akhmediev-Peregrine solitons. We show that\ntriangular wavelet spectra can be sensed by compressive measurements at the\nearly stages of the development of vibrational rogue waves. Our results may\nlead to development of the efficient vibrational rogue wave measurement and\nearly sensing systems with reduced memory requirements which use the\ncompressive sampling algorithms. In typical solid mechanics applications,\ncompressed measurements can be acquired by randomly positioning single sensor\nand multisensors.\n",
"title": "Efficient Measurement of the Vibrational Rogue Waves by Compressive Sampling Based Wavelet Analysis"
}
| null | null | null | null | true | null |
16987
| null |
Default
| null | null |
null |
{
"abstract": " This paper presents SceneCut, a novel approach to jointly discover previously\nunseen objects and non-object surfaces using a single RGB-D image. SceneCut's\njoint reasoning over scene semantics and geometry allows a robot to detect and\nsegment object instances in complex scenes where modern deep learning-based\nmethods either fail to separate object instances, or fail to detect objects\nthat were not seen during training. SceneCut automatically decomposes a scene\ninto meaningful regions which either represent objects or scene surfaces. The\ndecomposition is qualified by an unified energy function over objectness and\ngeometric fitting. We show how this energy function can be optimized\nefficiently by utilizing hierarchical segmentation trees. Moreover, we leverage\na pre-trained convolutional oriented boundary network to predict accurate\nboundaries from images, which are used to construct high-quality region\nhierarchies. We evaluate SceneCut on several different indoor environments, and\nthe results show that SceneCut significantly outperforms all the existing\nmethods.\n",
"title": "SceneCut: Joint Geometric and Object Segmentation for Indoor Scenes"
}
| null | null | null | null | true | null |
16988
| null |
Default
| null | null |
null |
{
"abstract": " In this paper, we show that different body parts do not play equally\nimportant roles in recognizing a human action in video data. We investigate to\nwhat extent a body part plays a role in recognition of different actions and\nhence propose a generic method of assigning weights to different body points.\nThe approach is inspired by the strong evidence in the applied perception\ncommunity that humans perform recognition in a foveated manner, that is they\nrecognize events or objects by only focusing on visually significant aspects.\nAn important contribution of our method is that the computation of the weights\nassigned to body parts is invariant to viewing directions and camera parameters\nin the input data. We have performed extensive experiments to validate the\nproposed approach and demonstrate its significance. In particular, results show\nthat considerable improvement in performance is gained by taking into account\nthe relative importance of different body parts as defined by our approach.\n",
"title": "An Invariant Model of the Significance of Different Body Parts in Recognizing Different Actions"
}
| null | null | null | null | true | null |
16989
| null |
Default
| null | null |
null |
{
"abstract": " The objective of this work is to take advantage of deep neural networks in\norder to make next day crime count predictions in a fine-grain city partition.\nWe make predictions using Chicago and Portland crime data, which is augmented\nwith additional datasets covering weather, census data, and public\ntransportation. The crime counts are broken into 10 bins and our model predicts\nthe most likely bin for a each spatial region at a daily level. We train this\ndata using increasingly complex neural network structures, including variations\nthat are suited to the spatial and temporal aspects of the crime prediction\nproblem. With our best model we are able to predict the correct bin for overall\ncrime count with 75.6% and 65.3% accuracy for Chicago and Portland,\nrespectively. The results show the efficacy of neural networks for the\nprediction problem and the value of using external datasets in addition to\nstandard crime data.\n",
"title": "Forecasting Crime with Deep Learning"
}
| null | null | null | null | true | null |
16990
| null |
Default
| null | null |
null |
{
"abstract": " About 6 years ago, semitoric systems were classified by Pelayo & Vu Ngoc by\nmeans of five invariants. Standard examples are the coupled spin oscillator on\n$\\mathbb{S}^2 \\times \\mathbb{R}^2$ and coupled angular momenta on $\\mathbb{S}^2\n\\times \\mathbb{S}^2$, both having exactly one focus-focus singularity. But so\nfar there were no explicit examples of systems with more than one focus-focus\nsingularity which are semitoric in the sense of that classification. This paper\nintroduces a 6-parameter family of integrable systems on $\\mathbb{S}^2 \\times\n\\mathbb{S}^2$ and proves that, for certain ranges of the parameters, it is a\ncompact semitoric system with precisely two focus-focus singularities. Since\nthe twisting index (one of the semitoric invariants) is related to the\nrelationship between different focus-focus points, this paper provides systems\nfor the future study of the twisting index.\n",
"title": "A family of compact semitoric systems with two focus-focus singularities"
}
| null | null | null | null | true | null |
16991
| null |
Default
| null | null |
null |
{
"abstract": " In this paper we study \\emph{threefolds isogenous to a product of mixed type}\ni.e. quotients of a product of three compact Riemann surfaces $C_i$ of genus at\nleast two by the action of a finite group $G$, which is free, but not diagonal.\nIn particular, we are interested in the systematic construction and\nclassification of these varieties. Our main result is the full classification\nof threefolds isogenous to a product of mixed type with $\\chi(\\mathcal O_X)=-1$\nassuming that any automorphism in $G$, which restricts to the trivial element\nin $Aut(C_i)$ for some $C_i$, is the identity on the product. Since the\nholomorphic Euler-Poincaré-characteristic of a smooth threefold of general\ntype with ample canonical class is always negative, these examples lie on the\nboundary, in the sense of threefold geography. To achieve our result we use\ntechniques from computational group theory. Indeed, we develop a MAGMA\nalgorithm to classify these threefolds for any given value of $\\chi(\\mathcal\nO_X)$.\n",
"title": "Mixed Threefolds Isogenous to a Product"
}
| null | null | null | null | true | null |
16992
| null |
Default
| null | null |
null |
{
"abstract": " We observe standard transfer learning can improve prediction accuracies of\ntarget tasks at the cost of lowering their prediction fairness -- a phenomenon\nwe named discriminatory transfer. We examine prediction fairness of a standard\nhypothesis transfer algorithm and a standard multi-task learning algorithm, and\nshow they both suffer discriminatory transfer on the real-world Communities and\nCrime data set. The presented case study introduces an interaction between\nfairness and transfer learning, as an extension of existing fairness studies\nthat focus on single task learning.\n",
"title": "Discriminatory Transfer"
}
| null | null | null | null | true | null |
16993
| null |
Default
| null | null |
null |
{
"abstract": " Fast carrier cooling is important for high power graphene based devices.\nStrongly Coupled Optical Phonons (SCOPs) play a major role in the relaxation of\nphotoexcited carriers in graphene. Heterostructures of graphene and hexagonal\nboron nitride (hBN) have shown exceptional mobility and high saturation\ncurrent, which makes them ideal for applications, but the effect of the hBN\nsubstrate on carrier cooling mechanisms is not understood. We track the cooling\nof hot photo-excited carriers in graphene-hBN heterostructures using ultrafast\npump-probe spectroscopy. We find that the carriers cool down four times faster\nin the case of graphene on hBN than on a silicon oxide substrate thus\novercoming the hot phonon (HP) bottleneck that plagues cooling in graphene\ndevices.\n",
"title": "Ultrafast relaxation of hot phonons in Graphene-hBN Heterostructures"
}
| null | null | null | null | true | null |
16994
| null |
Default
| null | null |
null |
{
"abstract": " Pattern matching is a powerful tool which is part of many functional\nprogramming languages as well as computer algebra systems such as Mathematica.\nAmong the existing systems, Mathematica offers the most expressive pattern\nmatching. Unfortunately, no open source alternative has comparable pattern\nmatching capabilities. Notably, these features include support for associative\nand/or commutative function symbols and sequence variables. While those\nfeatures have individually been subject of previous research, their\ncomprehensive combination has not yet been investigated. Furthermore, in many\napplications, a fixed set of patterns is matched repeatedly against different\nsubjects. This many-to-one matching can be sped up by exploiting similarities\nbetween patterns. Discrimination nets are the state-of-the-art solution for\nmany-to-one matching. In this thesis, a generalized discrimination net which\nsupports the full feature set is presented. All algorithms have been\nimplemented as an open-source library for Python. In experiments on real world\nexamples, significant speedups of many-to-one over one-to-one matching have\nbeen observed.\n",
"title": "Non-linear Associative-Commutative Many-to-One Pattern Matching with Sequence Variables"
}
| null | null | null | null | true | null |
16995
| null |
Default
| null | null |
null |
{
"abstract": " The beams at the ILC produce electron positron pairs due to beam-beam\ninteractions. This note presents for the first time a study of these processes\nin a detailed simulation, which shows that these pair background particles\nappear at angles that extend to the inner layers of the detector. The full data\nset of pairs produced in one bunch crossing was used to calculate the helix\ntracks, which the particles form in the solenoid field of the SiD detector. The\nresults suggest to further study the reduction of the beam pipe radius and\ntherefore to either add another SiD vertex detector layer, or reduce the radius\nof the existing vertex detector layers, without increasing the detector\noccupancy significantly. This has to go along with additional studies whether\nthe improvement in physics reconstruction methods, like c-tagging, is worth the\nincreased background level at smaller radii.\n",
"title": "Pair Background Envelopes in the SiD Detector"
}
| null | null | null | null | true | null |
16996
| null |
Default
| null | null |
null |
{
"abstract": " The Hamming graph $H(d,n)$ is the Cartesian product of $d$ complete graphs on\n$n$ vertices. Let $m=d(n-1)$ be the degree and $V = n^d$ be the number of\nvertices of $H(d,n)$. Let $p_c^{(d)}$ be the critical point for bond\npercolation on $H(d,n)$. We show that, for $d \\in \\mathbb N$ fixed and $n \\to\n\\infty$,\n\\begin{equation*}\np_c^{(d)}= \\dfrac{1}{m} + \\dfrac{2d^2-1}{2(d-1)^2}\\dfrac{1}{m^2}\n+ O(m^{-3}) + O(m^{-1}V^{-1/3}),\n\\end{equation*} which extends the asymptotics found in\n\\cite{BorChaHofSlaSpe05b} by one order. The term $O(m^{-1}V^{-1/3})$ is the\nwidth of the critical window. For $d=4,5,6$ we have $m^{-3} =\nO(m^{-1}V^{-1/3})$, and so the above formula represents the full asymptotic\nexpansion of $p_c^{(d)}$. In \\cite{FedHofHolHul16a} \\st{we show that} this\nformula is a crucial ingredient in the study of critical bond percolation on\n$H(d,n)$ for $d=2,3,4$. The proof uses a lace expansion for the upper bound and\na novel comparison with a branching random walk for the lower bound. The proof\nof the lower bound also yields a refined asymptotics for the susceptibility of\na subcritical Erdős-Rényi random graph.\n",
"title": "Expansion of percolation critical points for Hamming graphs"
}
| null | null |
[
"Mathematics"
] | null | true | null |
16997
| null |
Validated
| null | null |
null |
{
"abstract": " Android, the #1 mobile app framework, enforces the single-GUI-thread model,\nin which a single UI thread manages GUI rendering and event dispatching. Due to\nthis model, it is vital to avoid blocking the UI thread for responsiveness. One\ncommon practice is to offload long-running tasks into async threads. To achieve\nthis, Android provides various async programming constructs, and leaves\ndevelopers themselves to obey the rules implied by the model. However, as our\nstudy reveals, more than 25% apps violate these rules and introduce\nhard-to-detect, fail-stop errors, which we term as aysnc programming errors\n(APEs). To this end, this paper introduces APEChecker, a technique to\nautomatically and efficiently manifest APEs. The key idea is to characterize\nAPEs as specific fault patterns, and synergistically combine static analysis\nand dynamic UI exploration to detect and verify such errors. Among the 40\nreal-world Android apps, APEChecker unveils and processes 61 APEs, of which 51\nare confirmed (83.6% hit rate). Specifically, APEChecker detects 3X more APEs\nthan the state-of-art testing tools (Monkey, Sapienz and Stoat), and reduces\ntesting time from half an hour to a few minutes. On a specific type of APEs,\nAPEChecker confirms 5X more errors than the data race detection tool,\nEventRacer, with very few false alarms.\n",
"title": "Efficiently Manifesting Asynchronous Programming Errors in Android Apps"
}
| null | null | null | null | true | null |
16998
| null |
Default
| null | null |
null |
{
"abstract": " Among the many anticipated roles for robots in the future is that of being a\nhuman teammate. Aside from all the technological hurdles that have to be\novercome with respect to hardware and control to make robots fit to work with\nhumans, the added complication here is that humans have many conscious and\nsubconscious expectations of their teammates - indeed, we argue that teaming is\nmostly a cognitive rather than physical coordination activity. This introduces\nnew challenges for the AI and robotics community and requires fundamental\nchanges to the traditional approach to the design of autonomy. With this in\nmind, we propose an update to the classical view of the intelligent agent\narchitecture, highlighting the requirements for mental modeling of the human in\nthe deliberative process of the autonomous agent. In this article, we outline\nbriefly the recent efforts of ours, and others in the community, towards\ndeveloping cognitive teammates along these guidelines.\n",
"title": "AI Challenges in Human-Robot Cognitive Teaming"
}
| null | null | null | null | true | null |
16999
| null |
Default
| null | null |
null |
{
"abstract": " For certain quasi-split reductive groups $G$ over a general field $F$, we\nconstruct an automorphism $\\iota_G$ of $G$ over $F$, well-defined as an element\nof ${\\rm Aut}(G)(F)/jG(F)$ where $j:G(F) \\rightarrow {\\rm Aut}(G)(F)$ is the\ninner-conjugation action of $G(F)$ on $G$. The automorphism $\\iota_G$\ngeneralizes (although only for quasi-split groups) an involution due to\nMoeglin-Vigneras-Waldspurger in [MVW] for classical groups which takes any\nirreducible admissible representation $\\pi$ of $G(F)$ for $G$ a classical group\nand $F$ a local field, to its contragredient $\\pi^\\vee$. The paper also\nformulates a conjecture on the contragredient of an irreducible admissible\nrepresentation of $G(F)$ for $G$ a reductive algebraic group over a local field\n$F$ in terms of the (enhanced) Langlands parameter of the representation.\n",
"title": "Generalizing the MVW involution, and the contragredient"
}
| null | null |
[
"Mathematics"
] | null | true | null |
17000
| null |
Validated
| null | null |
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