text
null | inputs
dict | prediction
null | prediction_agent
null | annotation
list | annotation_agent
null | multi_label
bool 1
class | explanation
null | id
stringlengths 1
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{
"abstract": " Linear and nonlinear optical properties of low dimensional nanostructures\nhave attracted a large interest in the scientific community as tools to probe\nthe strong confinement of the electrons and for possible applications in\noptoelectronic devices. In particular it has been shown that the linear optical\nresponse of carbon nanotubes [Science 308, 838 (2005)] and graphene nanoribbons\n[Nat. Comm. 5, 4253 (2014)] is dominated by bounded electron-hole pairs, the\nexcitons. The role of excitons in linear response has been widely studied, but\nstill little is known on their effect on nonlinear susceptibilities. Using a\nrecently developed methodology [Phys. Rev. B 88, 235113 (2013)] based on\nwell-established ab-initio many-body perturbation theory approaches, we find\nthat quasiparticle shifts and excitonic effects significantly modify the\nthird-harmonic generation in carbon nanotubes and graphene nanoribbons. For\nboth systems the net effect of many-body effects is to reduce the intensity of\nthe main peak in the independent particle spectrum and redistribute the\nspectral weight among several excitonic resonances.\n",
"title": "Excitonic effects in third harmonic generation: the case of carbon nanotubes and nanoribbons"
}
| null | null | null | null | true | null |
11401
| null |
Default
| null | null |
null |
{
"abstract": " We prove a general family of congruences for Bernoulli numbers whose index is\na polynomial function of a prime, modulo a power of that prime. Our family\ngeneralizes many known results, including the von Staudt--Clausen theorem and\nKummer's congruence.\n",
"title": "A general family of congruences for Bernoulli numbers"
}
| null | null | null | null | true | null |
11402
| null |
Default
| null | null |
null |
{
"abstract": " Completely positive and completely bounded mutlipliers on rigid\n$C^{\\ast}$-tensor categories were introduced by Popa and Vaes. Using these\nnotions, we define and study the Fourier-Stieltjes algebra, the Fourier algebra\nand the algebra of completely bounded multipliers of a rigid $C^{\\ast}$-tensor\ncategory. The rich structure that these algebras have in the setting of locally\ncompact groups is still present in the setting of rigid $C^{\\ast}$-tensor\ncategories. We also prove that Leptin's characterization of amenability still\nholds in this setting, and we collect some natural observations on property\n(T).\n",
"title": "The Fourier algebra of a rigid $C^{\\ast}$-tensor category"
}
| null | null | null | null | true | null |
11403
| null |
Default
| null | null |
null |
{
"abstract": " In this paper we investigate the number of integer points lying in dilations\nof lattice path matroid polytopes. We give a characterization of such points as\npolygonal paths in the diagram of the lattice path matroid. Furthermore, we\nprove that lattice path matroid polytopes are affinely equivalent to a family\nof distributive polytopes. As applications we obtain two new infinite families\nof matroids verifying a conjecture of De Loera et.~al. and present an explicit\nformula of the Ehrhart polynomial for one of them.\n",
"title": "On lattice path matroid polytopes: integer points and Ehrhart polynomial"
}
| null | null | null | null | true | null |
11404
| null |
Default
| null | null |
null |
{
"abstract": " Electronic and magnetic properties of DNA structures doped by simple and\ntransition d- and f-metal ions (Gd, La, Cu, Zn, Au) are reviewed. Both one- and\ntwo dimensional systems are considered. A particular attention is paid to\ngadolinium and copper doped DNA systems, their unusual magnetism being treated.\nThe problem of classical and quantum transport (including transfer of genetic\ninformation during replication and transcription) and electron localization in\nbiological systems is discussed.\n",
"title": "Quantum effects and magnetism in the spatially distributed DNA molecules"
}
| null | null | null | null | true | null |
11405
| null |
Default
| null | null |
null |
{
"abstract": " In this paper, a stochastic model with regime switching is developed for\nsolar photo-voltaic (PV) power in order to provide short-term probabilistic\nforecasts. The proposed model for solar PV power is physics inspired and\nexplicitly incorporates the stochasticity due to clouds using different\nparameters addressing the attenuation in power.Based on the statistical\nbehavior of parameters, a simple regime-switching process between the three\nclasses of sunny, overcast and partly cloudy is proposed. Then, probabilistic\nforecasts of solar PV power are obtained by identifying the present regime\nusing PV power measurements and assuming persistence in this regime. To\nillustrate the technique developed, a set of solar PV power data from a single\nrooftop installation in California is analyzed and the effectiveness of the\nmodel in fitting the data and in providing short-term point and probabilistic\nforecasts is verified. The proposed forecast method outperforms a variety of\nreference models that produce point and probabilistic forecasts and therefore\nportrays the merits of employing the proposed approach.\n",
"title": "A Stochastic Model for Short-Term Probabilistic Forecast of Solar Photo-Voltaic Power"
}
| null | null |
[
"Statistics"
] | null | true | null |
11406
| null |
Validated
| null | null |
null |
{
"abstract": " Employing ab initio calculations, we discuss chemical, mechanical, and\ndynamical stability of MoN-TaN solid solutions together with cubic-like MoN/TaN\nsuperlattices, as another materials design concept. Hexagonal-type structures\nbased on low-energy modifications of MoN and TaN are the most stable ones over\nthe whole composition range. Despite being metastable, disordered cubic\npolymorphs are energetically significantly preferred over their ordered\ncounterparts. An in-depth analysis of atomic environments in terms of bond\nlengths and angles reveals that the chemical disorder results in (partially)\nbroken symmetry, i.e., the disordered cubic structure relaxes towards a\nhexagonal NiAs-type phase, the ground state of MoN. Surprisingly, also the\nsuperlattice architecture is clearly favored over the ordered cubic solid\nsolution. We show that the bi-axial coherency stresses in superlattices break\nthe cubic symmetry beyond simple tetragonal distortions and lead to a new\ntetragonal $\\zeta$-phase (space group P4/nmm), which exhibits a more negative\nformation energy than the symmetry-stabilized cubic structures of MoN and TaN.\nUnlike cubic TaN, the $\\zeta\\text{-TaN}$ is elastically and vibrationally\nstable, while $\\zeta$-MoN is stabilized only by the superlattice structure. To\nmap compositional trends in elasticity, we establish mechanical stability of\nvarious Mo$_{1-x}$Ta$_x$N systems and find the closest high-symmetry\napproximants of the corresponding elastic tensors. According to the estimated\npolycrystalline moduli, the hexagonal polymorphs are predicted to be extremely\nhard, however, less ductile than the cubic phases and superlattices. The trends\nin stability based on energetics and elasticity are corroborated by density of\nelectronic states.\n",
"title": "Stability and elasticity of metastable solid solutions and superlattices in the MoN-TaN system: a first-principles study"
}
| null | null | null | null | true | null |
11407
| null |
Default
| null | null |
null |
{
"abstract": " Early and accurate identification of parkinsonian syndromes (PS) involving\npresynaptic degeneration from non-degenerative variants such as Scans Without\nEvidence of Dopaminergic Deficit (SWEDD) and tremor disorders, is important for\neffective patient management as the course, therapy and prognosis differ\nsubstantially between the two groups. In this study, we use Single Photon\nEmission Computed Tomography (SPECT) images from healthy normal, early PD and\nSWEDD subjects, as obtained from the Parkinson's Progression Markers Initiative\n(PPMI) database, and process them to compute shape- and surface fitting-based\nfeatures for the three groups. We use these features to develop and compare\nvarious classification models that can discriminate between scans showing\ndopaminergic deficit, as in PD, from scans without the deficit, as in healthy\nnormal or SWEDD. Along with it, we also compare these features with Striatal\nBinding Ratio (SBR)-based features, which are well-established and clinically\nused, by computing a feature importance score using Random forests technique.\nWe observe that the Support Vector Machine (SVM) classifier gave the best\nperformance with an accuracy of 97.29%. These features also showed higher\nimportance than the SBR-based features. We infer from the study that shape\nanalysis and surface fitting are useful and promising methods for extracting\ndiscriminatory features that can be used to develop diagnostic models that\nmight have the potential to help clinicians in the diagnostic process.\n",
"title": "High Accuracy Classification of Parkinson's Disease through Shape Analysis and Surface Fitting in $^{123}$I-Ioflupane SPECT Imaging"
}
| null | null | null | null | true | null |
11408
| null |
Default
| null | null |
null |
{
"abstract": " Structured Prediction Energy Networks (SPENs) are a simple, yet expressive\nfamily of structured prediction models (Belanger and McCallum, 2016). An energy\nfunction over candidate structured outputs is given by a deep network, and\npredictions are formed by gradient-based optimization. This paper presents\nend-to-end learning for SPENs, where the energy function is discriminatively\ntrained by back-propagating through gradient-based prediction. In our\nexperience, the approach is substantially more accurate than the structured SVM\nmethod of Belanger and McCallum (2016), as it allows us to use more\nsophisticated non-convex energies. We provide a collection of techniques for\nimproving the speed, accuracy, and memory requirements of end-to-end SPENs, and\ndemonstrate the power of our method on 7-Scenes image denoising and CoNLL-2005\nsemantic role labeling tasks. In both, inexact minimization of non-convex SPEN\nenergies is superior to baseline methods that use simplistic energy functions\nthat can be minimized exactly.\n",
"title": "End-to-End Learning for Structured Prediction Energy Networks"
}
| null | null | null | null | true | null |
11409
| null |
Default
| null | null |
null |
{
"abstract": " The self-action features of wave packets propagating in a two-dimensional\nsystem of equidistantly arranged fibers are studied analytically and\nnumerically on the basis of the discrete nonlinear Schrödinger equation.\nSelf-consistent equations for the characteristic scales of a Gaussian wave\npacket are derived on the basis of the variational approach, which are proved\nnumerically for powers $\\mathcal{P} < 10 \\mathcal{P}_\\text{cr}$ exceeding\nslightly the critical one for self-focusing. At higher powers, the wave beams\nbecome filamented, and their amplitude is limited due to nonlinear breaking of\nthe interaction between neighbor light-guides. This make impossible to collect\na powerful wave beam into the single light-guide. The variational analysis show\nthe possibility of adiabatic self-compression of soliton-like laser pulses in\nthe process of their three-dimensional self-focusing to the central\nlight-guide. However, the further increase of the field amplitude during\nself-compression leads to the longitudinal modulation instability development\nand formation of a set of light bullets in the central fiber. In the regime of\nhollow wave beams, filamentation instability becomes predominant. As a result,\nit becomes possible to form a set of light bullets in optical fibers located on\nthe ring.\n",
"title": "Self-compression of spatially limited laser pulses in a system of coupled light-guides"
}
| null | null | null | null | true | null |
11410
| null |
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| null | null |
null |
{
"abstract": " We propose a network independent, hand-held system to translate and\ndisambiguate foreign restaurant menu items in real-time. The system is based on\nthe use of a portable multimedia device, such as a smartphones or a PDA. An\naccurate and fast translation is obtained using a Machine Translation engine\nand a context-specific corpora to which we apply two pre-processing steps,\ncalled translation standardization and $n$-gram consolidation. The phrase-table\ngenerated is orders of magnitude lighter than the ones commonly used in market\napplications, thus making translations computationally less expensive, and\ndecreasing the battery usage. Translation ambiguities are mitigated using\nmultimedia information including images of dishes and ingredients, along with\ningredient lists. We implemented a prototype of our system on an iPod Touch\nSecond Generation for English speakers traveling in Spain. Our tests indicate\nthat our translation method yields higher accuracy than translation engines\nsuch as Google Translate, and does so almost instantaneously. The memory\nrequirements of the application, including the database of images, are also\nwell within the limits of the device. By combining it with a database of\nnutritional information, our proposed system can be used to help individuals\nwho follow a medical diet maintain this diet while traveling.\n",
"title": "A Hand-Held Multimedia Translation and Interpretation System with Application to Diet Management"
}
| null | null | null | null | true | null |
11411
| null |
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| null | null |
null |
{
"abstract": " In this paper, we show how controllers created using data driven designs,\nsuch as neural networks, can be used together with model based controllers in a\nway that combines the performance guarantees of the model based controllers\nwith the efficiency of the data driven controllers. The considered performance\nguarantees include both safety, in terms of avoiding designated unsafe parts of\nthe state space, and convergence, in terms of reaching a given beneficial part\nof the state space. Using the framework Behavior Trees, we are able to show how\nthis can be done on the top level, concerning just two controllers, as\ndescribed above, but also note that the same approach can be used in arbitrary\nsub-trees. The price for introducing the new controller is that the upper bound\non the time needed to reach the desired part of the state space increases. The\napproach is illustrated with an inverted pendulum example.\n",
"title": "Adding Neural Network Controllers to Behavior Trees without Destroying Performance Guarantees"
}
| null | null | null | null | true | null |
11412
| null |
Default
| null | null |
null |
{
"abstract": " A rectangular grid formed by liquid filaments on a partially wetting\nsubstrate evolves in a series of breakups leading to arrays of drops with\ndifferent shapes distributed in a rather regular bidimensional pattern. Our\nstudy is focused on the configuration produced when two long parallel filaments\nof silicone oil, which are placed upon a glass substrate previously coated with\na fluorinated solution, are crossed perpendicularly by another pair of long\nparallel filaments. A remarkable feature of this kind of grids is that there\nare two qualitatively different types of drops. While one set is formed at the\ncrossing points, the rest are consequence of the breakup of shorter filaments\nformed between the crossings. Here, we analyze the main geometric features of\nall types of drops, such as shape of the footprint and contact angle\ndistribution along the drop periphery. The formation of a series of short\nfilaments with similar geometric and physical properties allows us to have\nsimultaneously quasi identical experiments to study the subsequent breakups. We\ndevelop a simple hydrodynamic model to predict the number of drops that results\nfrom a filament of given initial length and width. This model is able to yield\nthe length intervals corresponding to a small number of drops and its\npredictions are successfully compared with the experimental data as well as\nwith numerical simulations of the full Navier--Stokes equation that provide a\ndetailed time evolution of the dewetting motion of the filament till the\nbreakup into drops. Finally, the prediction for finite filaments is contrasted\nwith the existing theories for infinite ones.\n",
"title": "Drop pattern resulting from the breakup of a bidimensional grid of liquid filaments"
}
| null | null | null | null | true | null |
11413
| null |
Default
| null | null |
null |
{
"abstract": " A Cyber-Physical System (CPS) is defined by its unique characteristics\ninvolving both the cyber and physical domains. Their hybrid nature introduces\nnew attack vectors, but also provides an opportunity to design new security\ndefenses. In this paper, we present a new domain-specific security mechanism,\nFIRED, that leverages physical properties such as inertia of the CPS to improve\nsecurity.\nFIRED is simple to describe and implement. It goes through two operations:\nReset and Diversify, as frequently as possible -- typically in the order of\nseconds or milliseconds. The combined effect of these operations is that\nattackers are unable to gain persistent control of the system. The CPS stays\nsafe and stable even under frequent resets because of the inertia present.\nFurther, resets simplify certain diversification mechanisms and makes them\nfeasible to implement in CPSs with limited computing resources.\nWe evaluate our idea on two real-world systems: an engine management unit of\na car and a flight controller of a quadcopter. Roughly speaking, these two\nsystems provide typical and extreme operational requirements for evaluating\nFIRED in terms of stability, algorithmic complexity, and safety requirements.\nWe show that FIRED provides robust security guarantees against hijacking\nattacks and persistent CPS threats. We find that our defense is suitable for\nemerging CPS such as commodity unmanned vehicles that are currently unregulated\nand cost sensitive.\n",
"title": "FIRED: Frequent Inertial Resets with Diversification for Emerging Commodity Cyber-Physical Systems"
}
| null | null | null | null | true | null |
11414
| null |
Default
| null | null |
null |
{
"abstract": " Chemotaxis is a ubiquitous biological phenomenon in which cells detect a\nspatial gradient of chemoattractant, and then move towards the source. Here we\npresent a position-dependent advection-diffusion model that quantitatively\ndescribes the statistical features of the chemotactic motion of the social\namoeba {\\it Dictyostelium discoideum} in a linear gradient of cAMP (cyclic\nadenosine monophosphate). We fit the model to experimental trajectories that\nare recorded in a microfluidic setup with stationary cAMP gradients and extract\nthe diffusion and drift coefficients in the gradient direction. Our analysis\nshows that for the majority of gradients, both coefficients decrease in time\nand become negative as the cells crawl up the gradient. The extracted model\nparameters also show that besides the expected drift in the direction of\nchemoattractant gradient, we observe a nonlinear dependency of the\ncorresponding variance in time, which can be explained by the model.\nFurthermore, the results of the model show that the non-linear term in the mean\nsquared displacement of the cell trajectories can dominate the linear term on\nlarge time scales.\n",
"title": "Modelling of Dictyostelium Discoideum Movement in Linear Gradient of Chemoattractant"
}
| null | null | null | null | true | null |
11415
| null |
Default
| null | null |
null |
{
"abstract": " Jupiter's banded appearance may appear unchanging to the casual observer, but\ncloser inspection reveals a dynamic, ever-changing system of belts and zones\nwith distinct cycles of activity. Identification of these long-term cycles\nrequires access to datasets spanning multiple jovian years, but explaining them\nrequires multi-spectral characterization of the thermal, chemical, and aerosol\nchanges associated with visible color variations. The Earth-based support\ncampaign for Juno's exploration of Jupiter has already characterized two\nupheaval events in the equatorial and temperate belts that are part of\nlong-term jovian cycles, whose underlying sources could be revealed by Juno's\nexploration of Jupiter's deep atmosphere.\n",
"title": "Cycles of Activity in the Jovian Atmosphere"
}
| null | null | null | null | true | null |
11416
| null |
Default
| null | null |
null |
{
"abstract": " This paper offers a methodological contribution at the intersection of\nmachine learning and operations research. Namely, we propose a methodology to\nquickly predict tactical solutions to a given operational problem. In this\ncontext, the tactical solution is less detailed than the operational one but it\nhas to be computed in very short time and under imperfect information. The\nproblem is of importance in various applications where tactical and operational\nplanning problems are interrelated and information about the operational\nproblem is revealed over time. This is for instance the case in certain\ncapacity planning and demand management systems.\nWe formulate the problem as a two-stage optimal prediction stochastic program\nwhose solution we predict with a supervised machine learning algorithm. The\ntraining data set consists of a large number of deterministic (second stage)\nproblems generated by controlled probabilistic sampling. The labels are\ncomputed based on solutions to the deterministic problems (solved independently\nand offline) employing appropriate aggregation and subselection methods to\naddress uncertainty. Results on our motivating application in load planning for\nrail transportation show that deep learning algorithms produce highly accurate\npredictions in very short computing time (milliseconds or less). The prediction\naccuracy is comparable to solutions computed by sample average approximation of\nthe stochastic program.\n",
"title": "Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information"
}
| null | null | null | null | true | null |
11417
| null |
Default
| null | null |
null |
{
"abstract": " This paper is an axiomatic study of consistent approval-based multi-winner\nrules, i.e., voting rules that select a fixed-size group of candidates based on\napproval ballots. We introduce the class of counting rules, provide an\naxiomatic characterization of this class and, in particular, show that counting\nrules are consistent. Building upon this result, we axiomatically characterize\nthree important consistent multi-winner rules: Proportional Approval Voting,\nMulti-Winner Approval Voting and the Approval Chamberlin-Courant rule. Our\nresults demonstrate the variety of multi-winner rules and illustrate three\ndifferent, orthogonal principles that multi-winner voting rules may represent:\nindividual excellence, diversity, and proportionality.\n",
"title": "Consistent Approval-Based Multi-Winner Rules"
}
| null | null |
[
"Computer Science"
] | null | true | null |
11418
| null |
Validated
| null | null |
null |
{
"abstract": " We address the problem of predicting the solvation free energy and\nequilibrium solvent density profile in fews minutes from the molecular density\nfunctional theory beyond the usual hypernetted-chain approximation. We\nintroduce a bridge functional of a coarse-grained, weighted solvent density. In\nfew minutes at most, for solutes of sizes ranging from small compounds to large\nproteins, we produce (i) an estimation of the free energy of solvation within 1\nkcal/mol of the experimental data for the hydrophobic solutes presented here,\nand (ii) the solvent distribution around the solute. Contrary to previous\npropositions, this bridge functional is thermodynamically consistent in that it\nproduces the correct liquid-vapor coexistence and the experimental surface\ntension. We show this consistency to be of crucial importance for water at room\ntemperature and pressure. This bridge functional is designed to be simple,\nlocal, and thus numerically efficient. Finally, we illustrate this new level of\nmolecular theory of solutions with the study of the hydration shell of a\nprotein.\n",
"title": "Bridge functional for the molecular density functional theory with consistent pressure and surface tension and its importance for solvation in water"
}
| null | null | null | null | true | null |
11419
| null |
Default
| null | null |
null |
{
"abstract": " In this paper we study sharp generalizations of $\\dot{F}_p^{0,q}$ multiplier\ntheorem of Mikhlin-Hörmander type. The class of multipliers that we consider\ninvolves Herz spaces $K_u^{s,t}$. Plancherel's theorem proves\n$\\widehat{L_s^2}=K_2^{s,2}$ and we study the optimal triple $(u,t,s)$ for which\n$\\sup_{k\\in\\mathbb{Z}}{\\big\\Vert \\big(\nm(2^k\\cdot)\\varphi\\big)^{\\vee}\\big\\Vert_{K_u^{s,t}}}<\\infty$ implies\n$\\dot{F}_p^{0,q}$ boundedness of multiplier operator $T_m$ where $\\varphi$ is a\ncutoff function. Our result also covers the $BMO$-type space\n$\\dot{F}_{\\infty}^{0,q}$.\n",
"title": "Fourier multiplier theorems for Triebel-Lizorkin spaces"
}
| null | null | null | null | true | null |
11420
| null |
Default
| null | null |
null |
{
"abstract": " After the diagnosis of a disease, one major objective is to predict\ncumulative probabilities of events such as clinical relapse or death from the\nindividual information collected up to a prediction time, including usually\nbiomarker repeated measurements. Several competing estimators have been\nproposed to calculate these individual dynamic predictions, mainly from two\napproaches: joint modelling and landmarking. These approaches differ by the\ninformation used, the model assumptions and the complexity of the computational\nprocedures. It is essential to properly validate the estimators derived from\njoint models and landmark models, quantify their variability and compare them\nin order to provide key elements for the development and use of individual\ndynamic predictions in clinical follow-up of patients. Motivated by the\nprediction of two competing causes of progression of prostate cancer from the\nhistory of prostate-specific antigen, we conducted an in-depth simulation study\nto validate and compare the dynamic predictions derived from these two methods.\nSpecifically, we formally defined the quantity to estimate and its estimators,\nproposed techniques to assess the uncertainty around predictions and validated\nthem. We also compared the individual dynamic predictions derived from joint\nmodels and landmark models in terms of prediction error, discriminatory power,\nefficiency and robustness to model assumptions. We show that these prediction\ntools should be handled with care, in particular by properly specifying models\nand estimators.\n",
"title": "Individual dynamic predictions using landmarking and joint modelling: validation of estimators and robustness assessment"
}
| null | null | null | null | true | null |
11421
| null |
Default
| null | null |
null |
{
"abstract": " Let $X$ be a smooth projective manifold with $\\dim_\\mathbb{C} X=n$. We show\nthat if a line bundle $L$ is $(n-1)$-ample, then it is $(n-1)$-positive. This\nis a partial converse to the Andreotti-Grauert theorem. As an application, we\nshow that a projective manifold $X$ is uniruled if and only if there exists a\nHermitian metric $\\omega$ on $X$ such that its Ricci curvature\n$\\mathrm{Ric}(\\omega)$ has at least one positive eigenvalue everywhere.\n",
"title": "A partial converse to the Andreotti-Grauert theorem"
}
| null | null | null | null | true | null |
11422
| null |
Default
| null | null |
null |
{
"abstract": " The ordered L1$_0$ FeNi phase (tetrataenite) is recently considered as a\npromising candidate for the rare-earth free permanent magnets applications. In\nthis work we calculate several characteristics of the L1$_0$ FeNi, where most\nof the results come form the fully relativistic full potential FPLO method with\nthe generalized gradient approximation (GGA). A special attention deserves the\nsummary of the magnetocrystalline anisotropy energies (MAE's), the full\npotential calculations of the anisotropy constant $K_3$, and the combined\nanalysis of the Fermi surface and three-dimensional $\\mathbf{k}$-resolved MAE.\nOther calculated parameters presented in this article are the magnetic moments\n$m_{s}$ and $m_{l}$, magnetostrictive coefficient $\\lambda_{001}$, bulk modulus\nB$_0$, and lattice parameters. The MAE's summary shows rather big discrepancies\nbetween the experimental MAE's from literature and also between the calculated\nMAE's.\n",
"title": "Ab initio study of magnetocrystalline anisotropy, magnetostriction, and Fermi surface of L10 FeNi (tetrataenite)"
}
| null | null |
[
"Physics"
] | null | true | null |
11423
| null |
Validated
| null | null |
null |
{
"abstract": " An orientation-preserving branched covering $f: S^2 \\to S^2$ is a nearly\nEuclidean Thurston (NET) map if each critical point is simple and its\npostcritical set has exactly four points. Inspired by classical, non-dynamical\nnotions such as Hurwitz equivalence of branched covers of surfaces, we develop\ninvariants for such maps. We then apply these notions to the classification and\nenumeration of NET maps. As an application, we obtain a complete classification\nof the dynamic critical orbit portraits of NET maps.\n",
"title": "Modular groups, Hurwitz classes and dynamic portraits of NET maps"
}
| null | null | null | null | true | null |
11424
| null |
Default
| null | null |
null |
{
"abstract": " This article introduces planar shape signatures derived from homology nerves,\nwhich are intersecting 1-cycles in a collection of homology groups endowed with\na proximal relator (set of nearness relations) that includes a descriptive\nproximity. A 1-cycle is a closed, connected path with a zero boundary in a\nsimplicial complex covering a finite, bounded planar shape. The signature of a\nshape sh A (denoted by sig(sh A)) is a feature vector that describes sh A. A\nsignature sig(sh A) is derived from the geometry, homology nerves, Betti\nnumber, and descriptive CW topology on the shape sh A. Several main results are\ngiven, namely, (a) every finite, bounded planar shape has a signature derived\nfrom the homology group on the shape, (b) a homology group equipped with a\nproximal relator defines a descriptive Leader uniform topology and (c) a\ndescription of a homology nerve and union of the descriptions of the 1-cycles\nin the nerve have same homotopy type.\n",
"title": "Proximal Planar Shape Signatures. Homology Nerves and Descriptive Proximity"
}
| null | null | null | null | true | null |
11425
| null |
Default
| null | null |
null |
{
"abstract": " The paper investigates the asymptotic behavior of a 2D overhead crane with\ninput delays in the boundary control. A linear boundary control is proposed.\nThe main feature of such a control lies in the facts that it solely depends on\nthe velocity but under the presence of time-delays. We end-up with a\nclosed-loop system where no displacement term is involved. It is shown that the\nproblem is well-posed in the sense of semigroups theory. LaSalle's invariance\nprinciple is invoked in order to establish the asymptotic convergence for the\nsolutions of the system to a stationary position which depends on the initial\ndata. Using a resolvent method it is proved that the convergence is indeed\npolynomial.\n",
"title": "Asymptotic analysis of a 2D overhead crane with input delays in the boundary control"
}
| null | null | null | null | true | null |
11426
| null |
Default
| null | null |
null |
{
"abstract": " The interplay of almost degenerate levels in quantum dots and molecular\njunctions with possibly different couplings to the reservoirs has lead to many\nobservable phenomena, such as the Fano effect, transmission phase slips and the\nSU(4) Kondo effect. Here we predict a dramatic repeated disappearance and\nreemergence of the SU(4) and anomalous SU(2) Kondo effects with increasing gate\nvoltage. This phenomenon is attributed to the level occupation switching which\nhas been previously invoked to explain the universal transmission phase slips\nin the conductance through a quantum dot. We use analytical arguments and\nnumerical renormalization group calculations to explain the observations and\ndiscuss their experimental relevance and dependence on the physical parameters.\n",
"title": "Abrupt disappearance and reemergence of the SU(2) and SU(4) Kondo effects due to population inversion"
}
| null | null | null | null | true | null |
11427
| null |
Default
| null | null |
null |
{
"abstract": " As we know, some global optimization problems cannot be solved using analytic\nmethods, so numeric/algorithmic approaches are used to find near to the optimal\nsolutions for them. A stochastic global optimization algorithm (SGoal) is an\niterative algorithm that generates a new population (a set of candidate\nsolutions) from a previous population using stochastic operations. Although\nsome research works have formalized SGoals using Markov kernels, such\nformalization is not general and sometimes is blurred. In this paper, we\npropose a comprehensive and systematic formal approach for studying SGoals.\nFirst, we present the required theory of probability (\\sigma-algebras,\nmeasurable functions, kernel, markov chain, products, convergence and so on)\nand prove that some algorithmic functions like swapping and projection can be\nrepresented by kernels. Then, we introduce the notion of join-kernel as a way\nof characterizing the combination of stochastic methods. Next, we define the\noptimization space, a formal structure (a set with a \\sigma-algebra that\ncontains strict \\epsilon-optimal states) for studying SGoals, and we develop\nkernels, like sort and permutation, on such structure. Finally, we present some\npopular SGoals in terms of the developed theory, we introduce sufficient\nconditions for convergence of a SGoal, and we prove convergence of some popular\nSGoals.\n",
"title": "Stochastic Global Optimization Algorithms: A Systematic Formal Approach"
}
| null | null | null | null | true | null |
11428
| null |
Default
| null | null |
null |
{
"abstract": " Dictionaries are collections of vectors used for representations of elements\nin Euclidean spaces. While recent research on optimal dictionaries is focussed\non providing sparse (i.e., $\\ell_0$-optimal,) representations, here we consider\nthe problem of finding optimal dictionaries such that representations of\nsamples of a random vector are optimal in an $\\ell_2$-sense. For us, optimality\nof representation is equivalent to minimization of the average $\\ell_2$-norm of\nthe coefficients used to represent the random vector, with the lengths of the\ndictionary vectors being specified a priori. With the help of recent results on\nrank-$1$ decompositions of symmetric positive semidefinite matrices and the\ntheory of majorization, we provide a complete characterization of\n$\\ell_2$-optimal dictionaries. Our results are accompanied by polynomial time\nalgorithms that construct $\\ell_2$-optimal dictionaries from given data.\n",
"title": "A complete characterization of optimal dictionaries for least squares representation"
}
| null | null |
[
"Computer Science",
"Statistics"
] | null | true | null |
11429
| null |
Validated
| null | null |
null |
{
"abstract": " In recent years Variation Autoencoders have become one of the most popular\nunsupervised learning of complicated distributions.Variational Autoencoder\n(VAE) provides more efficient reconstructive performance over a traditional\nautoencoder. Variational auto enocders make better approximaiton than MCMC. The\nVAE defines a generative process in terms of ancestral sampling through a\ncascade of hidden stochastic layers. They are a directed graphic models.\nVariational autoencoder is trained to maximise the variational lower bound.\nHere we are trying maximise the likelihood and also at the same time we are\ntrying to make a good approximation of the data. Its basically trading of the\ndata log-likelihood and the KL divergence from the true posterior. This paper\ndescribes the scenario in which we wish to find a point-estimate to the\nparameters $\\theta$ of some parametric model in which we generate each\nobservations by first sampling a local latent variable and then sampling the\nassociated observation. Here we use least square loss function with\nregularization in the the reconstruction of the image, the least square loss\nfunction was found to give better reconstructed images and had a faster\ntraining time.\n",
"title": "Least Square Variational Bayesian Autoencoder with Regularization"
}
| null | null |
[
"Computer Science",
"Statistics"
] | null | true | null |
11430
| null |
Validated
| null | null |
null |
{
"abstract": " A complex projective manifold is rationally connected, resp. rationally\nsimply connected, if finite subsets are connected by a rational curve, resp.\nthe spaces parameterizing these connecting rational curves are themselves\nrationally connected. We prove that a projective scheme over a global function\nfield with vanishing \"elementary obstruction\" has a rational point if it\ndeforms to a rationally simply connected variety in characteristic 0. This\ngives new, uniform proofs over these fields of the Period-Index Theorem, the\nquasi-split case of Serre's \"Conjecture II\", and Lang's $C_2$ property.\n",
"title": "Rational points of rationally simply connected varieties over global function fields"
}
| null | null | null | null | true | null |
11431
| null |
Default
| null | null |
null |
{
"abstract": " We define a Koszul sign map encoding the Koszul sign convention. A\ncohomological interpretation is given.\n",
"title": "A Koszul sign map"
}
| null | null | null | null | true | null |
11432
| null |
Default
| null | null |
null |
{
"abstract": " Space-borne low-to medium-resolution (R~10^2-10^3) transmission spectroscopy\nof atmospheres detect the broadest spectral features (alkali doublets,\nmolecular bands, scattering), while high-resolution (R~10^5), ground-based\nobservations probe the sharpest features (cores of the alkali lines, molecular\nlines).The two techniques differ by:(1) The LSF of ground-based observations is\n10^3 times narrower than for space-borne observations;(2)Space-borne\ntransmission spectra probe up to the base of thermosphere, while ground-based\nobservations can reach pressures down to 10^(-11);(3)Space-borne observations\ndirectly yield the transit depth of the planet, while ground-based observations\nmeasure differences in the radius of the planet at different wavelengths.It is\nchallenging to combine both techniques.We develop a method to compare\ntheoretical models with observations at different resolutions.We introduce\nPyETA, a line-by-line 1D radiative transfer code to compute transmission\nspectra at R~10^6 (0.01 A) over a broad wavelength range.An hybrid forward\nmodeling/retrieval optimization scheme is devised to deal with the large\ncomputational resources required by modeling a broad wavelength range (0.3-2\n$\\mu$m) at high resolution.We apply our technique to HD189733b.Here, HST\nobservations reveal a flattened spectrum due to scattering by aerosols, while\nhigh-resolution ground-based HARPS observations reveal the sharp cores of\nsodium lines.We reconcile these results by building models that reproduce\nsimultaneously both data sets, from the troposphere to the thermosphere. We\nconfirm:(1)the presence of scattering by tropospheric aerosols;(2)that the\nsodium core feature is of thermospheric origin.Accounting for aerosols, the\nsodium cores indicate T up to 10000K in the thermosphere.The precise value of\nthe thermospheric temperature is degenerate with the abundance of sodium and\naltitude of the aerosol deck.\n",
"title": "Combining low- to high-resolution transit spectroscopy of HD 189733b. Linking the troposphere and the thermosphere of a hot gas giant"
}
| null | null | null | null | true | null |
11433
| null |
Default
| null | null |
null |
{
"abstract": " We introduce the concept of multiplicatively closed subsets of a commutative\nring $R$ which split an $R$-module $M$ and study factorization properties of\nelements of $M$ with respect to such a set. Also we demonstrate how one can\nutilize this concept to investigate factorization properties of $R$ and deduce\nsome Nagata type theorems relating factorization properties of $R$ to those of\nits localizations, when $R$ is an integral domain.\n",
"title": "Factorizations in Modules and Splitting Multiplicatively Closed Subsets"
}
| null | null | null | null | true | null |
11434
| null |
Default
| null | null |
null |
{
"abstract": " The concept of a hybrid readout of a time projection chamber is presented. It\ncombines a GEM-based amplification and a pad-based anode plane with a pixel\nchip as readout electronics. This way, a high granularity enabling to identify\nelectron clusters from the primary ionisation is achieved as well as\nflexibility and large anode coverage. The benefits of this high granularity, in\nparticular for dE/dx measurements, are outlined and the current software and\nhardware development status towards a proof-of-principle is given.\n",
"title": "ROPPERI - A TPC readout with GEMs, pads and Timepix"
}
| null | null | null | null | true | null |
11435
| null |
Default
| null | null |
null |
{
"abstract": " We introduce and describe the results of a novel shared task on bandit\nlearning for machine translation. The task was organized jointly by Amazon and\nHeidelberg University for the first time at the Second Conference on Machine\nTranslation (WMT 2017). The goal of the task is to encourage research on\nlearning machine translation from weak user feedback instead of human\nreferences or post-edits. On each of a sequence of rounds, a machine\ntranslation system is required to propose a translation for an input, and\nreceives a real-valued estimate of the quality of the proposed translation for\nlearning. This paper describes the shared task's learning and evaluation setup,\nusing services hosted on Amazon Web Services (AWS), the data and evaluation\nmetrics, and the results of various machine translation architectures and\nlearning protocols.\n",
"title": "A Shared Task on Bandit Learning for Machine Translation"
}
| null | null |
[
"Computer Science",
"Statistics"
] | null | true | null |
11436
| null |
Validated
| null | null |
null |
{
"abstract": " Learning with Reproducing Kernel Hilbert Spaces (RKHS) has been widely used\nin many scientific disciplines. Because a RKHS can be very flexible, it is\ncommon to impose a regularization term in the optimization to prevent\noverfitting. Standard RKHS learning employs the squared norm penalty of the\nlearning function. Despite its success, many challenges remain. In particular,\none cannot directly use the squared norm penalty for variable selection or data\nextraction. Therefore, when there exists noise predictors, or the underlying\nfunction has a sparse representation in the dual space, the performance of\nstandard RKHS learning can be suboptimal. In the literature,work has been\nproposed on how to perform variable selection in RKHS learning, and a data\nsparsity constraint was considered for data extraction. However, how to learn\nin a RKHS with both variable selection and data extraction simultaneously\nremains unclear. In this paper, we propose a unified RKHS learning method,\nnamely, DOuble Sparsity Kernel (DOSK) learning, to overcome this challenge. An\nefficient algorithm is provided to solve the corresponding optimization\nproblem. We prove that under certain conditions, our new method can\nasymptotically achieve variable selection consistency. Simulated and real data\nresults demonstrate that DOSK is highly competitive among existing approaches\nfor RKHS learning.\n",
"title": "Double Sparsity Kernel Learning with Automatic Variable Selection and Data Extraction"
}
| null | null | null | null | true | null |
11437
| null |
Default
| null | null |
null |
{
"abstract": " We investigate the dynamics of a coupled waveguide system with competing\nlinear and nonlinear loss-gain profiles which can facilitate power saturation.\nWe show the usefulness of the model in achieving unidirectional beam\npropagation. In this regard, the considered type of coupled waveguide system\nhas two drawbacks, (i) difficulty in achieving perfect isolation of light in a\nwaveguide and (ii) existence of blow-up type behavior for certain input power\nsituations. We here show a nonlinear $\\cal{PT}$ symmetric coupling that helps\nto overcome these two drawbacks. Such a nonlinear coupling has close connection\nwith the phenomenon of stimulated Raman scattering. In particular, we have\nelucidated the role of this nonlinear coupling using an integrable $\\cal{PT}$\nsymmetric situation. In particular, using the integrals of motion, we have\nreduced this coupled waveguide problem to the problem of dynamics of a particle\nin a potential. With the latter picture, we have clearly illustrated the role\nof the considered nonlinear coupling. The above $\\cal{PT}$ symmetric case\ncorresponds to a limiting form of a general equation describing the phenomenon\nof stimulated Raman scattering. We also point out the ability to transport\nlight unidirectionally even in this general case.\n",
"title": "Controlling of blow-up responses by a nonlinear $\\cal{PT}$ symmetric coupling"
}
| null | null | null | null | true | null |
11438
| null |
Default
| null | null |
null |
{
"abstract": " Text extraction is an important problem in image processing with applications\nfrom optical character recognition to autonomous driving. Most of the\ntraditional text segmentation algorithms consider separating text from a simple\nbackground (which usually has a different color from texts). In this work we\nconsider separating texts from a textured background, that has similar color to\ntexts. We look at this problem from a signal decomposition perspective, and\nconsider a more realistic scenario where signal components are overlaid on top\nof each other (instead of adding together). When the signals are overlaid, to\nseparate signal components, we need to find a binary mask which shows the\nsupport of each component. Because directly solving the binary mask is\nintractable, we relax this problem to the approximated continuous problem, and\nsolve it by alternating optimization method. We show that the proposed\nalgorithm achieves significantly better results than other recent works on\nseveral challenging images.\n",
"title": "Text Extraction From Texture Images Using Masked Signal Decomposition"
}
| null | null | null | null | true | null |
11439
| null |
Default
| null | null |
null |
{
"abstract": " Long-lead forecasting for spatio-temporal systems can often entail complex\nnonlinear dynamics that are difficult to specify it a priori. Current\nstatistical methodologies for modeling these processes are often highly\nparameterized and thus, challenging to implement from a computational\nperspective. One potential parsimonious solution to this problem is a method\nfrom the dynamical systems and engineering literature referred to as an echo\nstate network (ESN). ESN models use so-called {\\it reservoir computing} to\nefficiently compute recurrent neural network (RNN) forecasts. Moreover,\nso-called \"deep\" models have recently been shown to be successful at predicting\nhigh-dimensional complex nonlinear processes, particularly those with multiple\nspatial and temporal scales of variability (such as we often find in\nspatio-temporal environmental data). Here we introduce a deep ensemble ESN\n(D-EESN) model. We present two versions of this model for spatio-temporal\nprocesses that both produce forecasts and associated measures of uncertainty.\nThe first approach utilizes a bootstrap ensemble framework and the second is\ndeveloped within a hierarchical Bayesian framework (BD-EESN). This more general\nhierarchical Bayesian framework naturally accommodates non-Gaussian data types\nand multiple levels of uncertainties. The methodology is first applied to a\ndata set simulated from a novel non-Gaussian multiscale Lorenz-96 dynamical\nsystem simulation model and then to a long-lead United States (U.S.) soil\nmoisture forecasting application.\n",
"title": "Deep Echo State Networks with Uncertainty Quantification for Spatio-Temporal Forecasting"
}
| null | null | null | null | true | null |
11440
| null |
Default
| null | null |
null |
{
"abstract": " In imaging modalities recording diffraction data, the original image can be\nreconstructed assuming known phases. When phases are unknown, oversampling and\na constraint on the support region in the original object can be used to solve\na non-convex optimization problem.\nSuch schemes are ill-suited to find the optimum solution for sparse data,\nsince the recorded image does not correspond exactly to the original wave\nfunction. We construct a convex optimization problem using a relaxed support\nconstraint and a maximum-likelihood treatment of the recorded data as a sample\nfrom the underlying wave function. We also stress the need to use relevant\nwindowing techniques to account for the sampled pattern being finite.\nOn simulated data, we demonstrate the benefits of our approach in terms of\nvisual quality and an improvement in the crystallographic R-factor from .4 to\n.1 for highly noisy data.\n",
"title": "Using Convex Optimization of Autocorrelation with Constrained Support and Windowing for Improved Phase Retrieval Accuracy"
}
| null | null | null | null | true | null |
11441
| null |
Default
| null | null |
null |
{
"abstract": " We consider the problem of the annual mean temperature prediction. The years\ntaken into account and the corresponding annual mean temperatures are denoted\nby $0,\\ldots, n$ and $t_0$, $\\ldots$, $t_n$, respectively. We propose to\npredict the temperature $t_{n+1}$ using the data $t_0$, $\\ldots$, $t_n$. For\neach $0\\leq l\\leq n$ and each parametrization $\\Theta^{(l)}$ of the Euclidean\nspace $\\mathbb{R}^{l+1}$ we construct a list of weights for the data\n$\\{t_0,\\ldots, t_l\\}$ based on the rows of $\\Theta^{(l)}$ which are correlated\nwith the constant trend. Using these weights we define a list of predictors of\n$t_{l+1}$ from the data $t_0$, $\\ldots$, $t_l$. We analyse how the\nparametrization affects the prediction, and provide three optimality criteria\nfor the selection of weights and parametrization. We illustrate our results for\nthe annual mean temperature of France and Morocco.\n",
"title": "Parametrizations, weights, and optimal prediction: Part 1"
}
| null | null | null | null | true | null |
11442
| null |
Default
| null | null |
null |
{
"abstract": " The issue of how time reversible microscopic dynamics gives rise to\nmacroscopic irreversible processes has been a recurrent issue in Physics since\nthe time of Boltzmann whose ideas shaped, and essentially resolved, such an\napparent contradiction. Following Boltzmann's spirit and ideas, but employing\nGibbs's approach, we advance the view that macroscopic irreversibility of\nHamiltonian systems of many degrees of freedom can be also seen as a result of\nthe symplectic non-squeezing theorem.\n",
"title": "Time irreversibility from symplectic non-squeezing"
}
| null | null | null | null | true | null |
11443
| null |
Default
| null | null |
null |
{
"abstract": " Motivated by relatively few delay-optimal scheduling results, in comparison\nto results on throughput optimality, we investigate an input-queued switch\nscheduling problem in which the objective is to minimize a linear function of\nthe queue-length vector. Theoretical properties of variants of the well-known\nMaxWeight scheduling algorithm are established within this context, which\nincludes showing that these algorithms exhibit optimal heavy-traffic\nqueue-length scaling. For the case of $2 \\times 2$ input-queued switches, we\nderive an optimal scheduling policy and establish its theoretical properties,\ndemonstrating fundamental differences with the variants of MaxWeight\nscheduling. Our theoretical results are expected to be of interest more broadly\nthan input-queued switches. Computational experiments demonstrate and quantify\nthe benefits of our optimal scheduling policy.\n",
"title": "On Optimal Weighted-Delay Scheduling in Input-Queued Switches"
}
| null | null | null | null | true | null |
11444
| null |
Default
| null | null |
null |
{
"abstract": " This is a survey on recent developments on the Hausdorff dimension of\nprojections and intersections for general subsets of Euclidean spaces, with an\nemphasis on estimates of the Hausdorff dimension of exceptional sets and on\nrestricted projection families. We shall also discuss relations between\nprojections and Hausdorff dimension of Besicovitch sets.\n",
"title": "Hausdorff dimension, projections, intersections, and Besicovitch sets"
}
| null | null | null | null | true | null |
11445
| null |
Default
| null | null |
null |
{
"abstract": " We consider the theoretical properties of a model which encompasses\nbi-partite matching under transferable utility on the one hand, and hedonic\npricing on the other. This framework is intimately connected to tripartite\nmatching problems (known as multi-marginal optimal transport problems in the\nmathematical literature). We exploit this relationship in two ways; first, we\nshow that a known structural result from multi-marginal optimal transport can\nbe used to establish an upper bound on the dimension of the support of stable\nmatchings. Next, assuming the distribution of agents on one side of the market\nis continuous, we identify a condition on their preferences that ensures purity\nand uniqueness of the stable matching; this condition is a variant of a known\ncondition in the mathematical literature, which guarantees analogous properties\nin the multi-marginal optimal transport problem. We exhibit several examples of\nsurplus functions for which our condition is satisfied, as well as some for\nwhich it fails.\n",
"title": "Interpolating between matching and hedonic pricing models"
}
| null | null |
[
"Mathematics"
] | null | true | null |
11446
| null |
Validated
| null | null |
null |
{
"abstract": " The coupled evolution of the magnetic field and the flow at the Earth's core\nmantle boundary is modeled within the 1900.0-2014.0 time period. To constraint\nthe dynamical behavior of the system with a core field model deriving from\ndirect measurements of the Earth's magnetic field we used an Ensemble Kalman\nfilter algorithm. By simulating an ensemble of possible states, access to the\ncomplete statistical properties of the considered fields is available.\nFurthermore, the method enables to provide predictions and to assess their\nreliability. In this study, we could highlight the cohabitation of two distinct\nflow regimes. One associated with the large scale part of the eccentric gyre,\nwhich evolves slowly in time and posses a very long memory of its past, and a\nfaster one associated with the small scale velocity field. We show that the\nlatter can exhibit rapid variations in localized areas. The combination of the\ntwo regimes can predict quite well the decadal variations in length of day, but\nit can also explain the discrepancies between the physically predicted and the\nobserved trend in these variations. Hindcast tests demonstrate that the model\nis well balanced and that it can provide accurate short term predictions of a\nmean state and its associated uncertainties. However, magnetic field\npredictions are limited by the high randomization rate of the different\nvelocity field scales, and after approximately 2000 years of forecast, no\nreliable information on the core field can be recovered.\n",
"title": "Modeling and predicting the short term evolution of the Geomagnetic field"
}
| null | null | null | null | true | null |
11447
| null |
Default
| null | null |
null |
{
"abstract": " In this paper, we present a novel approach for broadcasting information based\non a Bluetooth Low Energy (BLE) ibeacon technology. We propose a dynamic method\nthat uses a combination of Wi-Fi and BLE technology where every technology\nplays a part in a user discovery and broadcasting process. In such system, a\nspecific ibeacon device broadcasts the information when a user is in proximity.\nUsing experiments, we conduct a scenario where the system discovers users,\ndisseminates information, and later we use collected data to examine the system\nperformance and capability. The results show that our proposed approach has a\npromising potential to become a powerful tool in the discovery and broadcasting\nconcept that can be easily implemented and used in business environments.\n",
"title": "Analysing the Potential of BLE to Support Dynamic Broadcasting Scenarios"
}
| null | null | null | null | true | null |
11448
| null |
Default
| null | null |
null |
{
"abstract": " Several studies have shown that stellar activity features, such as occulted\nand non-occulted starspots, can affect the measurement of transit parameters\nbiasing studies of transit timing variations and transmission spectra. We\npresent $\\texttt{PyTranSpot}$, which we designed to model multiband transit\nlight curves showing starspot anomalies, inferring both transit and spot\nparameters. The code follows a pixellation approach to model the star with its\ncorresponding limb darkening, spots, and transiting planet on a two dimensional\nCartesian coordinate grid. We combine $\\texttt{PyTranSpot}$ with an MCMC\nframework to study and derive exoplanet transmission spectra, which provides\nstatistically robust values for the physical properties and uncertainties of a\ntransiting star-planet system. We validate $\\texttt{PyTranSpot}$'s performance\nby analyzing eleven synthetic light curves of four different star-planet\nsystems and 20 transit light curves of the well-studied WASP-41b system. We\nalso investigate the impact of starspots on transit parameters and derive\nwavelength dependent transit depth values for WASP-41b covering a range of\n6200-9200 $\\AA$, indicating a flat transmission spectrum.\n",
"title": "$\\texttt{PyTranSpot}$ - A tool for multiband light curve modeling of planetary transits and stellar spots"
}
| null | null | null | null | true | null |
11449
| null |
Default
| null | null |
null |
{
"abstract": " The distributions of species lifetimes and species in space are related,\nsince species with good local survival chances have more time to colonize new\nhabitats and species inhabiting large areas have higher chances to survive\nlocal disturbances. Yet, both distributions have been discussed in mostly\nseparate communities. Here, we study both patterns simultaneously using a\nspatially explicit, evolutionary community assembly approach. We present and\ninvestigate a metacommunity model, consisting of a grid of patches, where each\npatch contains a local food web. Species survival depends on predation and\ncompetition interactions, which in turn depend on species body masses as the\nkey traits. The system evolves due to the migration of species to neighboring\npatches, the addition of new species as modifications of existing species, and\nlocal extinction events. The structure of each local food web thus emerges in a\nself-organized manner as the highly non-trivial outcome of the relative time\nscales of these processes. Our model generates a large variety of complex,\nmulti-trophic networks and therefore serves as a powerful tool to investigate\necosystems on long temporal and large spatial scales. We find that the observed\nlifetime distributions and species-area relations resemble power laws over\nappropriately chosen parameter ranges and thus agree qualitatively with\nempirical findings. Moreover, we observe strong finite-size effects, and a\ndependence of the relationships on the trophic level of the species. By\ncomparing our results to simple neutral models found in the literature, we\nidentify the features that are responsible for the values of the exponents.\n",
"title": "Interplay of spatial dynamics and local adaptation shapes species lifetime distributions and species-area relationships"
}
| null | null | null | null | true | null |
11450
| null |
Default
| null | null |
null |
{
"abstract": " The problem of routing in graphs using node-disjoint paths has received a lot\nof attention and a polylogarithmic approximation algorithm with constant\ncongestion is known for undirected graphs [Chuzhoy and Li 2016] and [Chekuri\nand Ene 2013]. However, the problem is hard to approximate within polynomial\nfactors on directed graphs, for any constant congestion [Chuzhoy, Kim and Li\n2016].\nRecently, [Chekuri, Ene and Pilipczuk 2016] have obtained a polylogarithmic\napproximation with constant congestion on directed planar graphs, for the\nspecial case of symmetric demands. We extend their result by obtaining a\npolylogarithmic approximation with constant congestion on arbitrary directed\nminor-free graphs, for the case of symmetric demands.\n",
"title": "Routing Symmetric Demands in Directed Minor-Free Graphs with Constant Congestion"
}
| null | null |
[
"Computer Science"
] | null | true | null |
11451
| null |
Validated
| null | null |
null |
{
"abstract": " Gaussian random fields are popular models for spatially varying\nuncertainties, arising for instance in geotechnical engineering, hydrology or\nimage processing. A Gaussian random field is fully characterised by its mean\nfunction and covariance operator. In more complex models these can also be\npartially unknown. In this case we need to handle a family of Gaussian random\nfields indexed with hyperparameters. Sampling for a fixed configuration of\nhyperparameters is already very expensive due to the nonlocal nature of many\nclassical covariance operators. Sampling from multiple configurations increases\nthe total computational cost severely. In this report we employ parameterised\nKarhunen-Loève expansions for sampling. To reduce the cost we construct a\nreduced basis surrogate built from snapshots of Karhunen-Loève eigenvectors.\nIn particular, we consider Matérn-type covariance operators with unknown\ncorrelation length and standard deviation. We suggest a linearisation of the\ncovariance function and describe the associated online-offline decomposition.\nIn numerical experiments we investigate the approximation error of the reduced\neigenpairs. As an application we consider forward uncertainty propagation and\nBayesian inversion with an elliptic partial differential equation where the\nlogarithm of the diffusion coefficient is a parameterised Gaussian random\nfield. In the Bayesian inverse problem we employ Markov chain Monte Carlo on\nthe reduced space to generate samples from the posterior measure. All numerical\nexperiments are conducted in 2D physical space, with non-separable covariance\noperators, and finite element grids with $\\sim 10^4$ degrees of freedom.\n",
"title": "Fast sampling of parameterised Gaussian random fields"
}
| null | null | null | null | true | null |
11452
| null |
Default
| null | null |
null |
{
"abstract": " We show how the discovery of robust scalable numerical solvers for arbitrary\nbounded linear operators can be automated as a Game Theory problem by\nreformulating the process of computing with partial information and limited\nresources as that of playing underlying hierarchies of adversarial information\ngames. When the solution space is a Banach space $B$ endowed with a quadratic\nnorm $\\|\\cdot\\|$, the optimal measure (mixed strategy) for such games (e.g. the\nadversarial recovery of $u\\in B$, given partial measurements $[\\phi_i, u]$ with\n$\\phi_i\\in B^*$, using relative error in $\\|\\cdot\\|$-norm as a loss) is a\ncentered Gaussian field $\\xi$ solely determined by the norm $\\|\\cdot\\|$, whose\nconditioning (on measurements) produces optimal bets. When measurements are\nhierarchical, the process of conditioning this Gaussian field produces a\nhierarchy of elementary bets (gamblets). These gamblets generalize the notion\nof Wavelets and Wannier functions in the sense that they are adapted to the\nnorm $\\|\\cdot\\|$ and induce a multi-resolution decomposition of $B$ that is\nadapted to the eigensubspaces of the operator defining the norm $\\|\\cdot\\|$.\nWhen the operator is localized, we show that the resulting gamblets are\nlocalized both in space and frequency and introduce the Fast Gamblet Transform\n(FGT) with rigorous accuracy and (near-linear) complexity estimates. As the FFT\ncan be used to solve and diagonalize arbitrary PDEs with constant coefficients,\nthe FGT can be used to decompose a wide range of continuous linear operators\n(including arbitrary continuous linear bijections from $H^s_0$ to $H^{-s}$ or\nto $L^2$) into a sequence of independent linear systems with uniformly bounded\ncondition numbers and leads to $\\mathcal{O}(N \\operatorname{polylog} N)$\nsolvers and eigenspace adapted Multiresolution Analysis (resulting in near\nlinear complexity approximation of all eigensubspaces).\n",
"title": "Universal Scalable Robust Solvers from Computational Information Games and fast eigenspace adapted Multiresolution Analysis"
}
| null | null | null | null | true | null |
11453
| null |
Default
| null | null |
null |
{
"abstract": " According to the principle of polyrepresentation, retrieval accuracy may\nimprove through the combination of multiple and diverse information object\nrepresentations about e.g. the context of the user, the information sought, or\nthe retrieval system. Recently, the principle of polyrepresentation was\nmathematically expressed using subjective logic, where the potential\nsuitability of each representation for improving retrieval performance was\nformalised through degrees of belief and uncertainty. No experimental evidence\nor practical application has so far validated this model. We extend the work of\nLioma et al. (2010), by providing a practical application and analysis of the\nmodel. We show how to map the abstract notions of belief and uncertainty to\nreal-life evidence drawn from a retrieval dataset. We also show how to estimate\ntwo different types of polyrepresentation assuming either (a) independence or\n(b) dependence between the information objects that are combined. We focus on\nthe polyrepresentation of different types of context relating to user\ninformation needs (i.e. work task, user background knowledge, ideal answer) and\nshow that the subjective logic model can predict their optimal combination\nprior and independently to the retrieval process.\n",
"title": "Preliminary Experiments using Subjective Logic for the Polyrepresentation of Information Needs"
}
| null | null | null | null | true | null |
11454
| null |
Default
| null | null |
null |
{
"abstract": " Although Bayesian inference is an immensely popular paradigm among a large\nsegment of scientists including statisticians, most of the applications\nconsider the objective priors and need critical investigations (Efron, 2013,\nScience). And although it has several optimal properties, one major drawback of\nBayesian inference is the lack of robustness against data contamination and\nmodel misspecification, which becomes pernicious in the use of objective\npriors. This paper presents the general formulation of a Bayes pseudo-posterior\ndistribution yielding robust inference. Exponential convergence results related\nto the new pseudo-posterior and the corresponding Bayes estimators are\nestablished under the general parametric set-up and illustrations are provided\nfor the independent stationary models and the independent non-homogenous\nmodels. For the first case, the discrete priors and the corresponding maximum\nposterior estimators are discussed with additional details. We further apply\nthis new pseudo-posterior to propose robust versions of the Bayes predictive\ndensity estimators and the expected Bayes estimator for the fixed-design\n(normal) linear regression models; their properties are illustrated both\ntheoretically as well as empirically.\n",
"title": "General Robust Bayes Pseudo-Posterior: Exponential Convergence results with Applications"
}
| null | null | null | null | true | null |
11455
| null |
Default
| null | null |
null |
{
"abstract": " Despite the outstanding achievements of modern cosmology, the classical\ndispute on the precise value of $H_0$, which is the first ever parameter of\nmodern cosmology and one of the prime parameters in the field, still goes on\nand on after over half a century of measurements. Recently the dispute came to\nthe spotlight with renewed strength owing to the significant tension (at\n$>3\\sigma$ c.l.) between the latest Planck determination obtained from the CMB\nanisotropies and the local (distance ladder) measurement from the Hubble Space\nTelescope (HST), based on Cepheids. In this work, we investigate the impact of\nthe running vacuum model (RVM) and related models on such a controversy. For\nthe RVM, the vacuum energy density $\\rho_{\\Lambda}$ carries a mild dependence\non the cosmic expansion rate, i.e. $\\rho_{\\Lambda}(H)$, which allows to\nameliorate the fit quality to the overall $SNIa+BAO+H(z)+LSS+CMB$ cosmological\nobservations as compared to the concordance $\\Lambda$CDM model. By letting the\nRVM to deviate from the vacuum option, the equation of state $w=-1$ continues\nto be favored by the overall fit. Vacuum dynamics also predicts the following:\ni) the CMB range of values for $H_0$ is more favored than the local ones, and\nii) smaller values for $\\sigma_8(0)$. As a result, a better account for the LSS\nstructure formation data is achieved as compared to the $\\Lambda$CDM, which is\nbased on a rigid (i.e. non-dynamical) $\\Lambda$ term.\n",
"title": "The $H_0$ tension in light of vacuum dynamics in the Universe"
}
| null | null | null | null | true | null |
11456
| null |
Default
| null | null |
null |
{
"abstract": " In this paper, we investigate zeros of difference polynomials of the form\n$f(z)^nH(z, f)-s(z)$, where $f(z)$ is a meromorphic function, $H(z, f)$ is a\ndifference polynomial of $f(z)$ and $s(z)$ is a small function. We first obtain\nsome inequalities for the relationship of the zero counting function of\n$f(z)^nH(z, f)-s(z)$ and the characteristic function and pole counting function\nof $f(z)$. Based on these inequalities, we establish some difference analogues\nof a classical result of Hayman for meromorphic functions. Some special cases\nare also investigated. These results improve previous findings.\n",
"title": "On certain type of difference polynomials of meromorphic functions"
}
| null | null | null | null | true | null |
11457
| null |
Default
| null | null |
null |
{
"abstract": " If spreadsheets are not erroneous then who, or what, is? Research has found\nthat end-users are. If end-users are erroneous then why are they? Research has\nfound that responsibility lies with human beings' fast and slow thinking modes\nand the inappropriate way they use them. If we are aware of this peculiarity of\nhuman thinking, then why do we not teach students how to train their brains?\nThis is the main problem, this is the weakest link in the process: teaching. We\nhave to make teachers realize that end-users are erroneous because of the\nerroneous teaching approaches to end-user computing. The proportion of fast and\nslow thinking modes is not constant, and teachers are mistaken when they apply\nthe same proportion in both the teaching and end-user roles. Teachers should\nbelieve in the incremental nature of science and have high self-efficacy to\nmake students understand and appreciate science. This is not currently the case\nin ICT and CS, and it is high time fundamental changes were introduced.\n",
"title": "Teaching methods are erroneous: approaches which lead to erroneous end-user computing"
}
| null | null | null | null | true | null |
11458
| null |
Default
| null | null |
null |
{
"abstract": " We investigate modulational instability (MI) in asymmetric dual-core\nnonlinear directional couplers incorporating the effects of the differences in\neffective mode areas and group velocity dispersions, as well as phase- and\ngroup-velocity mismatches. Using coupled-mode equations for this system, we\nidentify MI conditions from the linearization with respect to small\nperturbations. First, we compare the MI spectra of the asymmetric system and\nits symmetric counterpart in the case of the anomalous group-velocity\ndispersion (GVD). In particular, it is demonstrated that the increase of the\ninter-core linear-coupling coefficient leads to a reduction of the MI gain\nspectrum in the asymmetric coupler. The analysis is extended for the asymmetric\nsystem in the normal-GVD regime, where the coupling induces and controls the\nMI, as well as for the system with opposite GVD signs in the two cores.\nFollowing the analytical consideration of the MI, numerical simulations are\ncarried out to explore nonlinear development of the MI, revealing the\ngeneration of periodic chains of localized peaks with growing amplitudes, which\nmay transform into arrays of solitons.\n",
"title": "Modulational Instability in Linearly Coupled Asymmetric Dual-Core Fibers"
}
| null | null | null | null | true | null |
11459
| null |
Default
| null | null |
null |
{
"abstract": " Discovering automatically the semantic structure of tagged visual data (e.g.\nweb videos and images) is important for visual data analysis and\ninterpretation, enabling the machine intelligence for effectively processing\nthe fast-growing amount of multi-media data. However, this is non-trivial due\nto the need for jointly learning underlying correlations between heterogeneous\nvisual and tag data. The task is made more challenging by inherently sparse and\nincomplete tags. In this work, we develop a method for modelling the inherent\nvisual data concept structures based on a novel Hierarchical-Multi-Label Random\nForest model capable of correlating structured visual and tag information so as\nto more accurately interpret the visual semantics, e.g. disclosing meaningful\nvisual groups with similar high-level concepts, and recovering missing tags for\nindividual visual data samples. Specifically, our model exploits hierarchically\nstructured tags of different semantic abstractness and multiple tag statistical\ncorrelations in addition to modelling visual and tag interactions. As a result,\nour model is able to discover more accurate semantic correlation between\ntextual tags and visual features, and finally providing favourable visual\nsemantics interpretation even with highly sparse and incomplete tags. We\ndemonstrate the advantages of our proposed approach in two fundamental\napplications, visual data clustering and missing tag completion, on\nbenchmarking video (i.e. TRECVID MED 2011) and image (i.e. NUS-WIDE) datasets.\n",
"title": "Discovering Visual Concept Structure with Sparse and Incomplete Tags"
}
| null | null | null | null | true | null |
11460
| null |
Default
| null | null |
null |
{
"abstract": " We present a new method of generating mixture models for data with\ncategorical attributes. The keys to this approach are an entropy-based density\nmetric in categorical space and annealing of high-entropy/low-density\ncomponents from an initial state with many components. Pruning of low-density\ncomponents using the entropy-based density allows GALILEO to consistently find\nhigh-quality clusters and the same optimal number of clusters. GALILEO has\nshown promising results on a range of test datasets commonly used for\ncategorical clustering benchmarks. We demonstrate that the scaling of GALILEO\nis linear in the number of records in the dataset, making this method suitable\nfor very large categorical datasets.\n",
"title": "GALILEO: A Generalized Low-Entropy Mixture Model"
}
| null | null | null | null | true | null |
11461
| null |
Default
| null | null |
null |
{
"abstract": " Let $\\Omega\\subset\\mathbb R^n$ be a Lipschitz domain. Given $1\\leq p<k\\leq n$\nand any $u\\in W^{2,p}(\\Omega)$ belonging to the little Hölder class\n$c^{1,\\alpha}$, we construct a sequence $u_j$ in the same space with\n$\\operatorname{rank}D^2u_j<k$ almost everywhere such that $u_j\\to u$ in\n$C^{1,\\alpha}$ and weakly in $W^{2,p}$. This result is in strong contrast with\nknown regularity behavior of functions in $W^{2,p}$, $p\\geq k$, satisfying the\nsame rank inequality.\n",
"title": "Approximation by mappings with singular Hessian minors"
}
| null | null | null | null | true | null |
11462
| null |
Default
| null | null |
null |
{
"abstract": " In this paper we address cardinality estimation problem which is an important\nsubproblem in query optimization. Query optimization is a part of every\nrelational DBMS responsible for finding the best way of the execution for the\ngiven query. These ways are called plans. The execution time of different plans\nmay differ by several orders, so query optimizer has a great influence on the\nwhole DBMS performance. We consider cost-based query optimization approach as\nthe most popular one. It was observed that cost-based optimization quality\ndepends much on cardinality estimation quality. Cardinality of the plan node is\nthe number of tuples returned by it.\nIn the paper we propose a novel cardinality estimation approach with the use\nof machine learning methods. The main point of the approach is using query\nexecution statistics of the previously executed queries to improve cardinality\nestimations. We called this approach adaptive cardinality estimation to reflect\nthis point. The approach is general, flexible, and easy to implement. The\nexperimental evaluation shows that this approach significantly increases the\nquality of cardinality estimation, and therefore increases the DBMS performance\nfor some queries by several times or even by several dozens of times.\n",
"title": "Adaptive Cardinality Estimation"
}
| null | null | null | null | true | null |
11463
| null |
Default
| null | null |
null |
{
"abstract": " We consider a non-stationary sequential stochastic optimization problem, in\nwhich the underlying cost functions change over time under a variation budget\nconstraint. We propose an $L_{p,q}$-variation functional to quantify the\nchange, which yields less variation for dynamic function sequences whose\nchanges are constrained to short time periods or small subsets of input domain.\nUnder the $L_{p,q}$-variation constraint, we derive both upper and matching\nlower regret bounds for smooth and strongly convex function sequences, which\ngeneralize previous results in Besbes et al. (2015). Furthermore, we provide an\nupper bound for general convex function sequences with noisy gradient feedback,\nwhich matches the optimal rate as $p\\to\\infty$. Our results reveal some\nsurprising phenomena under this general variation functional, such as the curse\nof dimensionality of the function domain. The key technical novelties in our\nanalysis include affinity lemmas that characterize the distance of the\nminimizers of two convex functions with bounded Lp difference, and a cubic\nspline based construction that attains matching lower bounds.\n",
"title": "Non-stationary Stochastic Optimization under $L_{p,q}$-Variation Measures"
}
| null | null |
[
"Computer Science",
"Statistics"
] | null | true | null |
11464
| null |
Validated
| null | null |
null |
{
"abstract": " We report a study on spin conductance in ultra-thin films of Yttrium Iron\nGarnet (YIG), where spin transport is provided by propagating spin waves, that\nare generated and detected by direct and inverse spin Hall effects in two Pt\nwires deposited on top. While at low current the spin conductance is dominated\nby transport of thermal magnons, at high current, the spin conductance is\ndominated by low-damping non-equilibrium magnons thermalized near the spectral\nbottom by magnon-magnon interaction, with consequent a sensitivity to the\napplied magnetic field and a longer decay length. This picture is supported by\nmicrofocus Brillouin Light Scattering spectroscopy.\n",
"title": "Spin conductance of YIG thin films driven from thermal to subthermal magnons regime by large spin-orbit torque"
}
| null | null | null | null | true | null |
11465
| null |
Default
| null | null |
null |
{
"abstract": " In this work, we focus on on the approach by noncommutative formal power\nseries to study the combinatorial aspects of the renormalization at the\nsingularities in $\\{0,1,+\\infty\\}$ of the solutions of nonlinear differential\nequations involved in quantum electrodynamics.\n",
"title": "Mathematical renormalization in quantum electrodynamics via noncommutative generating series"
}
| null | null | null | null | true | null |
11466
| null |
Default
| null | null |
null |
{
"abstract": " Deep generative networks provide a powerful tool for modeling complex data in\na wide range of applications. In inverse problems that use these networks as\ngenerative priors on data, one must often perform inference of the inputs of\nthe networks from the outputs. Inference is also required for sampling during\nstochastic training on these generative models. This paper considers inference\nin a deep stochastic neural network where the parameters (e.g., weights, biases\nand activation functions) are known and the problem is to estimate the values\nof the input and hidden units from the output. While several approximate\nalgorithms have been proposed for this task, there are few analytic tools that\ncan provide rigorous guarantees in the reconstruction error. This work presents\na novel and computationally tractable output-to-input inference method called\nMulti-Layer Vector Approximate Message Passing (ML-VAMP). The proposed\nalgorithm, derived from expectation propagation, extends earlier AMP methods\nthat are known to achieve the replica predictions for optimality in simple\nlinear inverse problems. Our main contribution shows that the mean-squared\nerror (MSE) of ML-VAMP can be exactly predicted in a certain large system limit\n(LSL) where the numbers of layers is fixed and weight matrices are random and\northogonally-invariant with dimensions that grow to infinity. ML-VAMP is thus a\nprincipled method for output-to-input inference in deep networks with a\nrigorous and precise performance achievability result in high dimensions.\n",
"title": "Inference in Deep Networks in High Dimensions"
}
| null | null | null | null | true | null |
11467
| null |
Default
| null | null |
null |
{
"abstract": " Deep neural networks with their large number of parameters are highly\nflexible learning systems. The high flexibility in such networks brings with\nsome serious problems such as overfitting, and regularization is used to\naddress this problem. A currently popular and effective regularization\ntechnique for controlling the overfitting is dropout. Often, large data\ncollections required for neural networks contain sensitive information such as\nthe medical histories of patients, and the privacy of the training data should\nbe protected. In this paper, we modify the recently proposed variational\ndropout technique which provided an elegant Bayesian interpretation to dropout,\nand show that the intrinsic noise in the variational dropout can be exploited\nto obtain a degree of differential privacy. The iterative nature of training\nneural networks presents a challenge for privacy-preserving estimation since\nmultiple iterations increase the amount of noise added. We overcome this by\nusing a relaxed notion of differential privacy, called concentrated\ndifferential privacy, which provides tighter estimates on the overall privacy\nloss. We demonstrate the accuracy of our privacy-preserving variational dropout\nalgorithm on benchmark datasets.\n",
"title": "Differentially Private Variational Dropout"
}
| null | null | null | null | true | null |
11468
| null |
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| null | null |
null |
{
"abstract": " Persistent currents in Bose condensates with a scalar order parameter are\nstabilized by the topology of the order parameter manifold. In condensates with\nmulticomponent order parameters it is topologically possible for supercurrents\nto `unwind' without leaving the manifold. We study the energetics of this\nprocess in the case of ferromagnetic condensates using a long wavelength energy\nfunctional that includes both the superfluid and spin stiffnesses. Exploiting\nanalogies to an elastic rod and rigid body motion, we show that the current\ncarrying state in a 1D ring geometry transitions between a spin helix in the\nenergy minima and a soliton-like configuration at the maxima. The relevance to\nrecent experiments in ultracold atoms is briefly discussed.\n",
"title": "Persistent Currents in Ferromagnetic Condensates"
}
| null | null | null | null | true | null |
11469
| null |
Default
| null | null |
null |
{
"abstract": " Generality is one of the main advantages of heuristic algorithms, as such,\nmultiple parameters are exposed to the user with the objective of allowing them\nto shape the algorithms to their specific needs. Parameter selection,\ntherefore, becomes an intrinsic problem of every heuristic algorithm. Selecting\ngood parameter values relies not only on knowledge related to the problem at\nhand, but to the algorithms themselves. This research explores the usage of\nself-organized criticality to reduce user interaction in the process of\nselecting suitable parameters for particle swarm optimization (PSO) heuristics.\nA particle swarm variant (named Adaptive PSO) with self-organized criticality\nis developed and benchmarked against the standard PSO. Criticality is observed\nin the dynamic behaviour of this swarm and excellent results are observed in\nthe long run. In contrast with the standard PSO, the Adaptive PSO does not\nstagnate at any point in time, balancing the concepts of exploration and\nexploitation better. A software platform for experimenting with particle\nswarms, called PSO Laboratory, is also developed. This software is used to test\nthe standard PSO as well as all other PSO variants developed in the process of\ncreating the Adaptive PSO. As the software is intended to be of aid to future\nand related research, special attention has been put in the development of a\nfriendly graphical user interface. Particle swarms are executed in real time,\nallowing users to experiment by changing parameters on-the-fly.\n",
"title": "Parameter Adaptation and Criticality in Particle Swarm Optimization"
}
| null | null | null | null | true | null |
11470
| null |
Default
| null | null |
null |
{
"abstract": " Model Predictive Control (MPC) is the principal control technique used in\nindustrial applications. Although it offers distinguishable qualities that make\nit ideal for industrial applications, it can be questioned its robustness\nregarding model uncertainties and external noises. In this paper we propose a\nrobust MPC controller that merges the simplicity in the design of MPC with\nadded robustness. In particular, our control system stems from the idea of\nadding robustness in the prediction phase of the algorithm through a specific\nrobust Kalman filter recently introduced. Notably, the overall result is an\nalgorithm very similar to classic MPC but that also provides the user with the\npossibility to tune the robustness of the control. To test the ability of the\ncontroller to deal with errors in modeling, we consider a servomechanism system\ncharacterized by nonlinear dynamics.\n",
"title": "Model Predictive Control meets robust Kalman filtering"
}
| null | null | null | null | true | null |
11471
| null |
Default
| null | null |
null |
{
"abstract": " With a majority of 'Yes' votes in the Constitutional Referendum of 2017,\nTurkey continues its transition from democracy to autocracy. By the will of the\nTurkish people, this referendum transferred practically all executive power to\npresident Erdogan. However, the referendum was confronted with a substantial\nnumber of allegations of electoral misconducts and irregularities, ranging from\nstate coercion of 'No' supporters to the controversial validity of unstamped\nballots. In this note we report the results of an election forensic analysis of\nthe 2017 referendum to clarify to what extent these voting irregularities were\npresent and if they were able to influence the outcome of the referendum. We\nspecifically apply novel statistical forensics tests to further identify the\nspecific nature of electoral malpractices. In particular, we test whether the\ndata contains fingerprints for ballot-stuffing (submission of multiple ballots\nper person during the vote) and voter rigging (coercion and intimidation of\nvoters). Additionally, we perform tests to identify numerical anomalies in the\nelection results. We find systematic and highly significant support for the\npresence of both, ballot-stuffing and voter rigging. In 6% of stations we find\nsigns for ballot-stuffing with an error (probability of ballot-stuffing not\nhappening) of 0.15% (3 sigma event). The influence of these vote distortions\nwere large enough to tip the overall balance from 'No' to a majority of 'Yes'\nvotes.\n",
"title": "Election forensic analysis of the Turkish Constitutional Referendum 2017"
}
| null | null | null | null | true | null |
11472
| null |
Default
| null | null |
null |
{
"abstract": " This paper discusses the efficient Bayesian estimation of a multivariate\nfactor stochastic volatility (Factor MSV) model with leverage. We propose a\nnovel approach to construct the sampling schemes that converges to the\nposterior distribution of the latent volatilities and the parameters of\ninterest of the Factor MSV model based on recent advances in Particle Markov\nchain Monte Carlo (PMCMC). As opposed to the approach of Chib et al. (2006} and\nOmori et al. (2007}, our approach does not require approximating the joint\ndistribution of outcome and volatility innovations by a mixture of bivariate\nnormal distributions. To sample the free elements of the loading matrix we\nemploy the interweaving method used in Kastner et al. (2017} in the Particle\nMetropolis within Gibbs (PMwG) step. The proposed method is illustrated\nempirically using a simulated dataset and a sample of daily US stock returns.\n",
"title": "Efficient Bayesian inference for multivariate factor stochastic volatility models with leverage"
}
| null | null | null | null | true | null |
11473
| null |
Default
| null | null |
null |
{
"abstract": " Colorado conducted risk-limiting tabulation audits (RLAs) across the state in\n2017, including both ballot-level comparison audits and ballot-polling audits.\nThose audits only covered contests restricted to a single county; methods to\nefficiently audit contests that cross county boundaries and combine ballot\npolling and ballot-level comparisons have not been available.\nColorado's current audit software (RLATool) needs to be improved to audit\nthese contests that cross county lines and to audit small contests efficiently.\nThis paper addresses these needs. It presents extremely simple but\ninefficient methods, more efficient methods that combine ballot polling and\nballot-level comparisons using stratified samples, and methods that combine\nballot-level comparison and variable-size batch comparison audits in a way that\ndoes not require stratified sampling.\nWe conclude with some recommendations, and illustrate our recommended method\nusing examples that compare them to existing approaches. Exemplar open-source\ncode and interactive Jupyter notebooks are provided that implement the methods\nand allow further exploration.\n",
"title": "Next Steps for the Colorado Risk-Limiting Audit (CORLA) Program"
}
| null | null |
[
"Statistics"
] | null | true | null |
11474
| null |
Validated
| null | null |
null |
{
"abstract": " With Hubble Space Telescope Fine Guidance Sensor astrometry and previously\npublished radial velocity measures we explore the exoplanetary system HD\n202206. Our modeling results in a parallax, $\\pi_{abs} = 21.96\\pm0.12$\nmilliseconds of arc, a mass for HD 202206 B of M$_B = 0.089^{ +0.007}_{-0.006}$\nMsun, and a mass for HD 202206 c of M$_c = 17.9 ^{ +2.9}_{-1.8}$ MJup. HD\n202206 is a nearly face-on G+M binary orbited by a brown dwarf. The system\narchitecture we determine supports past assertions that stability requires a\n5:1 mean motion resonance (we find a period ratio, $P_c/P_B = 4.92\\pm0.04$) and\ncoplanarity (we find a mutual inclination, Phi = 6 \\arcdeg \\pm 2 \\arcdeg).\n",
"title": "HD 202206 : A Circumbinary Brown Dwarf System"
}
| null | null | null | null | true | null |
11475
| null |
Default
| null | null |
null |
{
"abstract": " There is a paradox in the model of social dynamics determined by voting in a\nstochastic environment (the ViSE model) called \"pit of losses.\" It consists in\nthe fact that a series of democratic decisions may systematically lead the\nsociety to the states unacceptable for all the voters. The paper examines how\nthis paradox can be neutralized by the presence in society of a group that\nvotes for its benefit and can regulate the threshold of its claims. We obtain\nand analyze analytical results characterizing the welfare of the whole society,\nthe group, and the other participants as functions of the said claims\nthreshold.\n",
"title": "On Optimal Group Claims at Voting in a Stochastic Environment"
}
| null | null |
[
"Computer Science",
"Mathematics"
] | null | true | null |
11476
| null |
Validated
| null | null |
null |
{
"abstract": " The Discrete Truncated Wigner Approximation (DTWA) is a semi-classical phase\nspace method useful for the exploration of Many-body quantum dynamics. In this\nwork we investigate Many-Body Localization (MBL) and thermalization using DTWA\nand compare its performance to exact numerical solutions. By taking as a\nbenchmark case a 1D random field Heisenberg spin chain with short range\ninteractions, and by comparing to numerically exact techniques, we show that\nDTWA is able to reproduce dynamical signatures that characterize both the\nthermal and the MBL phases. It exhibits the best quantitative agreement at\nshort times deep in each of the phases and larger mismatches close to the phase\ntransition. The DTWA captures the logarithmic growth of entanglement in the MBL\nphase, even though a pure classical mean-field analysis would lead to no\ndynamics at all. Our results suggest the DTWA can become a useful method to\ninvestigate MBL and thermalization in experimentally relevant settings\nintractable with exact numerical techniques, such as systems with long range\ninteractions and/or systems in higher dimensions.\n",
"title": "Exploring many body localization and thermalization using semiclassical method"
}
| null | null | null | null | true | null |
11477
| null |
Default
| null | null |
null |
{
"abstract": " In this paper, we investigate the parametric weight knapsack problem, in\nwhich the item weights are affine functions of the form $w_i(\\lambda) = a_i +\n\\lambda \\cdot b_i$ for $i \\in \\{1,\\ldots,n\\}$ depending on a real-valued\nparameter $\\lambda$. The aim is to provide a solution for all values of the\nparameter. It is well-known that any exact algorithm for the problem may need\nto output an exponential number of knapsack solutions. We present the first\nfully polynomial-time approximation scheme (FPTAS) for the problem that, for\nany desired precision $\\varepsilon \\in (0,1)$, computes\n$(1-\\varepsilon)$-approximate solutions for all values of the parameter. Our\nFPTAS is based on two different approaches and achieves a running time of\n$\\mathcal{O}(n^3/\\varepsilon^2 \\cdot \\min\\{ \\log^2 P, n^2 \\} \\cdot \\min\\{\\log\nM, n \\log (n/\\varepsilon) / \\log(n \\log (n/\\varepsilon) )\\})$ where $P$ is an\nupper bound on the optimal profit and $M := \\max\\{W, n \\cdot \\max\\{a_i,b_i: i\n\\in \\{1,\\ldots,n\\}\\}\\}$ for a knapsack with capacity $W$.\n",
"title": "An FPTAS for the Knapsack Problem with Parametric Weights"
}
| null | null | null | null | true | null |
11478
| null |
Default
| null | null |
null |
{
"abstract": " Markov processes are well understood in the case when they take place in the\nwhole Euclidean space. However, the situation becomes much more complicated if\na Markov process is restricted to a domain with a boundary, and then a\nsatisfactory theory only exists for processes with continuous trajectories.\nThis research, into non-classical boundary value problems, is motivated by the\nstudy of stochastic processes, restricted to a domain, that can have\ndiscontinuous trajectories.\nTo make this general problem more tractable, we consider a particular\noperator, $\\mathcal{A}$, which is chosen to be the generator of a certain\nstable Lévy process restricted to the positive half-line. We are able to\nrepresent $\\mathcal{A}$ as a (hyper-) singular integral and, using this\nrepresentation, deduce simple conditions for its boundedness, between Bessel\npotential spaces. Moreover, from energy estimates, we prove that, under certain\nconditions, $\\mathcal{A}$ has a trivial kernel.\nA central feature of this research is our use of Mellin operators to deal\nwith the leading singular terms that combine, and cancel, at the boundary.\nIndeed, after considerable analysis, the problem is reformulated in the context\nof an algebra of multiplication, Wiener-Hopf and Mellin operators, acting on a\nLebesgue space. The resulting generalised symbol is examined and, it turns out,\nthat a certain transcendental equation, involving gamma and trigonometric\nfunctions with complex arguments, plays a pivotal role. Following detailed\nconsideration of this transcendental equation, we are able to determine when\nour operator is Fredholm and, in that case, calculate its index. Finally,\ncombining information on the kernel with the Fredholm index, we establish\nprecise conditions for the invertibility of $\\mathcal{A}$.\n",
"title": "Mellin and Wiener-Hopf operators in a non-classical boundary value problem describing a Lévy process"
}
| null | null | null | null | true | null |
11479
| null |
Default
| null | null |
null |
{
"abstract": " We present the analysis results of an eclipsing cataclysmic variable (CV)\nV729 Sgr, based on our observations and AAVSO data. Some outburst parameters\nwere determined such as outburst amplitude ($A_{n}$) and recurrence time\n($T_{n}$), and then the relationship between $A_{n}$ and $T_{n}$ is discussed.\nA cursory examination for the long-term light curves reveals that there are\nsmall-amplitude outbursts and dips present, which is similar to the behaviors\nseen in some nova-like CVs (NLs). More detailed inspection suggests that the\noutbursts in V729 Sgr may be Type A (outside-in) with a rise time $\\sim1.76$ d.\nFurther analysis also shows that V729 Sgr is an intermediate between dwarf nova\nand NLs, and we constrain its mass transfer rate to $1.59\\times10^{-9} <\n\\dot{M}_{2} < 5.8\\times10^{-9}M_{\\odot}yr^{-1}$ by combining the theory for Z\nCam type stars with observations. Moreover, the rapid oscillations in V729 Sgr\nwere detected and analyzed for the first time. Our results indicate that the\noscillation at $\\sim 25.5$ s is a true DNO, being associated with the accretion\nevents. The classification of the oscillations at $\\sim 136$ and $154$ s as\nlpDNOs is based on the relation between $P_{lpDNOs}$ and $P_{DNOs}$. Meanwhile,\nthe QPOs at the period of hundreds of seconds are also detected.\n",
"title": "Long-term photometric behavior of the eclipsing cataclysmic variable V729 Sgr"
}
| null | null | null | null | true | null |
11480
| null |
Default
| null | null |
null |
{
"abstract": " Calibration of individual based models (IBMs), successful in modeling complex\necological dynamical systems, is often performed only ad-hoc. Bayesian\ninference can be used for both parameter estimation and uncertainty\nquantification, but its successful application to realistic scenarios has been\nhindered by the complex stochastic nature of IBMs. Computationally expensive\ntechniques such as Particle Filter (PF) provide marginal likelihood estimates,\nwhere multiple model simulations (particles) are required to get a sample from\nthe state distribution conditional on the observed data. Particle ensembles are\nre-sampled at each data observation time, requiring particle destruction and\nreplication, which lead to an increase in algorithmic complexity. We present\nSPUX, a Python implementation of parallel Particle Markov Chain Monte Carlo\n(PMCMC) algorithm, which mitigates high computational costs by distributing\nparticles over multiple computational units. Adaptive load re-balancing\ntechniques are used to mitigate computational work imbalances introduced by\nre-sampling. Framework performance is investigated and significant speed-ups\nare observed for a simple predator-prey IBM model.\n",
"title": "SPUX: Scalable Particle Markov Chain Monte Carlo for uncertainty quantification in stochastic ecological models"
}
| null | null | null | null | true | null |
11481
| null |
Default
| null | null |
null |
{
"abstract": " Health insurance companies in Brazil have their data about claims organized\nhaving the view only for providers. In this way, they loose the physician view\nand how they share patients. Partnership between physicians can view as a\nfruitful work in most of the cases but sometimes this could be a problem for\nhealth insurance companies and patients, for example a recommendation to visit\nanother physician only because they work in same clinic. The focus of the work\nis to better understand physicians activities and how these activities are\nrepresented in the data. Our approach considers three aspects: the\nrelationships among physicians, the relationships between physicians and\npatients, and the relationships between physicians and health providers. We\npresent the results of an analysis of a claims database (detailing 18 months of\nactivity) from a large health insurance company in Brazil. The main\ncontribution presented in this paper is a set of models to represent: mutual\nreferral between physicians, patient retention, and physician centrality in the\nhealth insurance network. Our results show the proposed models based on social\nnetwork frameworks, extracted surprising insights about physicians from real\nhealth insurance claims data.\n",
"title": "A Social Network Analysis Framework for Modeling Health Insurance Claims Data"
}
| null | null | null | null | true | null |
11482
| null |
Default
| null | null |
null |
{
"abstract": " In 2009, Corteel, Savelief and Vuletić generalized the concept of\noverpartitions to a new object called plane overpartitions. In recent work, the\nauthor considered a restricted form of plane overpartitions called $k$-rowed\nplane overpartions and proved a method to obtain congruences for these and\nother types of combinatorial generating functions. In this paper, we prove\nseveral restricted and unrestricted plane overpartition congruences modulo $4$\nand $8$ using other techniques.\n",
"title": "Congruences for Restricted Plane Overpartitions Modulo 4 and 8"
}
| null | null | null | null | true | null |
11483
| null |
Default
| null | null |
null |
{
"abstract": " Data driven research on Android has gained a great momentum these years. The\nabundance of data facilitates knowledge learning, however, also increases the\ndifficulty of data preprocessing. Therefore, it is non-trivial to prepare a\ndemanding and accurate set of data for research. In this work, we put forward\nAndroVault, a framework for the Android research composing of data collection,\nknowledge representation and knowledge extraction. It has started with a\nlong-running web crawler for data collection (both apps and description) since\n2013, which guarantees the timeliness of data; With static analysis and dynamic\nanalysis of the collected data, we compute a variety of attributes to\ncharacterize Android apps. After that, we employ a knowledge graph to connect\nall these apps by computing their correlation in terms of attributes; Last, we\nleverage multiple technologies such as logical inference, machine learning, and\ncorrelation analysis to extract facts (more accurate and demanding, either high\nlevel or not, data) that are beneficial for a specific research problem. With\nthe produced data of high quality, we have successfully conducted many research\nworks including malware detection, code generation, and Android testing. We\nwould like to release our data to the research community in an authenticated\nmanner, and encourage them to conduct productive research.\n",
"title": "AndroVault: Constructing Knowledge Graph from Millions of Android Apps for Automated Analysis"
}
| null | null | null | null | true | null |
11484
| null |
Default
| null | null |
null |
{
"abstract": " Humans are increasingly stressing ecosystems via habitat destruction, climate\nchange and global population movements leading to the widespread loss of\nbiodiversity and the disruption of key ecological services. Ecosystems\ncharacterized primarily by mutualistic relationships between species such as\nplant-pollinator interactions may be particularly vulnerable to such\nperturbations because the loss of biodiversity can cause extinction cascades\nthat can compromise the entire network. Here, we develop a general restoration\nstrategy based on network-science for degraded ecosystems. Specifically, we\nshow that network topology can be used to identify the optimal sequence of\nspecies reintroductions needed to maximize biodiversity gains following partial\nand full ecosystem collapse. This restoration strategy generalizes across\ntopologically-disparate and geographically-distributed ecosystems.\nAdditionally, we find that although higher connectance and diversity promote\npersistence in pristine ecosystems, these attributes reduce the effectiveness\nof restoration efforts in degraded networks. Hence, focusing on restoring the\nfactors that promote persistence in pristine ecosystems may yield suboptimal\nrecovery strategies for degraded ecosystems. Overall, our results have\nimportant insights for designing effective ecosystem restoration strategies to\npreserve biodiversity and ensure the delivery of critical natural services that\nfuel economic development, food security and human health around the globe\n",
"title": "Universal and generalizable restoration strategies for degraded ecological networks"
}
| null | null | null | null | true | null |
11485
| null |
Default
| null | null |
null |
{
"abstract": " Recent experiments show that both natural and artificial microswimmers in\nnarrow channel-like geometries will self-organise to form steady, directed\nflows. This suggests that networks of flowing active matter could function as\nnovel autonomous microfluidic devices. However, little is known about how\ninformation propagates through these far-from-equilibrium systems. Through a\nmathematical analogy with spin-ice vertex models, we investigate here the\ninput-output characteristics of generic incompressible active flow networks\n(AFNs). Our analysis shows that information transport through an AFN is\ninherently different from conventional pressure or voltage driven networks.\nActive flows on hexagonal arrays preserve input information over longer\ndistances than their passive counterparts and are highly sensitive to bulk\ntopological defects, whose presence can be inferred from marginal input-output\ndistributions alone. This sensitivity further allows controlled permutations on\nparallel inputs, revealing an unexpected link between active matter and group\ntheory that can guide new microfluidic mixing strategies facilitated by active\nmatter and aid the design of generic autonomous information transport networks.\n",
"title": "Information transmission and signal permutation in active flow networks"
}
| null | null | null | null | true | null |
11486
| null |
Default
| null | null |
null |
{
"abstract": " When analyzing empirical data, we often find that global linear models\noverestimate the number of parameters required. In such cases, we may ask\nwhether the data lies on or near a manifold or a set of manifolds (a so-called\nmulti-manifold) of lower dimension than the ambient space. This question can be\nphrased as a (multi-) manifold hypothesis. The identification of such intrinsic\nmultiscale features is a cornerstone of data analysis and representation and\nhas given rise to a large body of work on manifold learning. In this work, we\nreview key results on multi-scale data analysis and intrinsic dimension\nfollowed by the introduction of a heuristic, multiscale framework for testing\nthe multi-manifold hypothesis. Our method implements a hypothesis test on a set\nof spline-interpolated manifolds constructed from variance-based intrinsic\ndimensions. The workflow is suitable for empirical data analysis as we\ndemonstrate on two use cases.\n",
"title": "Heuristic Framework for Multi-Scale Testing of the Multi-Manifold Hypothesis"
}
| null | null |
[
"Statistics"
] | null | true | null |
11487
| null |
Validated
| null | null |
null |
{
"abstract": " Technological developments call for increasing perception and action\ncapabilities of robots. Among other skills, vision systems that can adapt to\nany possible change in the working conditions are needed. Since these\nconditions are unpredictable, we need benchmarks which allow to assess the\ngeneralization and robustness capabilities of our visual recognition\nalgorithms. In this work we focus on robotic kitting in unconstrained\nscenarios. As a first contribution, we present a new visual dataset for the\nkitting task. Differently from standard object recognition datasets, we provide\nimages of the same objects acquired under various conditions where camera,\nillumination and background are changed. This novel dataset allows for testing\nthe robustness of robot visual recognition algorithms to a series of different\ndomain shifts both in isolation and unified. Our second contribution is a novel\nonline adaptation algorithm for deep models, based on batch-normalization\nlayers, which allows to continuously adapt a model to the current working\nconditions. Differently from standard domain adaptation algorithms, it does not\nrequire any image from the target domain at training time. We benchmark the\nperformance of the algorithm on the proposed dataset, showing its capability to\nfill the gap between the performances of a standard architecture and its\ncounterpart adapted offline to the given target domain.\n",
"title": "Kitting in the Wild through Online Domain Adaptation"
}
| null | null | null | null | true | null |
11488
| null |
Default
| null | null |
null |
{
"abstract": " The pulse-recloser uses pulse testing technology to verify that the line is\nclear of faults before initiating a reclose operation, which significantly\nreduces stress on the system components (e.g. substation transformers) and\nvoltage sags on adjacent feeders. Online event analysis of pulse-reclosers are\nessential to increases the overall utility of the devices, especially when\nthere are numerous devices installed throughout the distribution system. In\nthis paper, field data recorded from several devices were analyzed to identify\nspecific activity and fault locations. An algorithm is developed to screen the\ndata to identify the status of each pole and to tag time windows with a\npossible pulse event. In the next step, selected time windows are further\nanalyzed and classified using a sparse representation technique by solving an\nl1-regularized least-square problem. This classification is obtained by\ncomparing the pulse signature with the reference dictionary to find a set that\nmost closely matches the pulse features. This work also sheds additional light\non the possibility of fault classification based on the pulse signature. Field\ndata collected from a distribution system are used to verify the effectiveness\nand reliability of the proposed method.\n",
"title": "Event Analysis of Pulse-reclosers in Distribution Systems Through Sparse Representation"
}
| null | null | null | null | true | null |
11489
| null |
Default
| null | null |
null |
{
"abstract": " The network alignment problem asks for the best correspondence between two\ngiven graphs, so that the largest possible number of edges are matched. This\nproblem appears in many scientific problems (like the study of protein-protein\ninteractions) and it is very closely related to the quadratic assignment\nproblem which has graph isomorphism, traveling salesman and minimum bisection\nproblems as particular cases. The graph matching problem is NP-hard in general.\nHowever, under some restrictive models for the graphs, algorithms can\napproximate the alignment efficiently. In that spirit the recent work by Feizi\nand collaborators introduce EigenAlign, a fast spectral method with convergence\nguarantees for Erdős-Renyí graphs. In this work we propose the algorithm\nProjected Power Alignment, which is a projected power iteration version of\nEigenAlign. We numerically show it improves the recovery rates of EigenAlign\nand we describe the theory that may be used to provide performance guarantees\nfor Projected Power Alignment.\n",
"title": "Projected Power Iteration for Network Alignment"
}
| null | null | null | null | true | null |
11490
| null |
Default
| null | null |
null |
{
"abstract": " We give a nonparametric methodology for hypothesis testing for equality of\nextrinsic mean objects on a manifold embedded in a numerical spaces. The\nresults obtained in the general setting are detailed further in the case of 3D\nprojective shapes represented in a space of symmetric matrices via the\nquadratic Veronese-Whitney (VW) embedding. Large sample and nonparametric\nbootstrap confidence regions are derived for the common VW-mean of random\nprojective shapes for finite 3D configurations. As an example, the VW MANOVA\ntesting methodology is applied to the multi-sample mean problem for independent\nprojective shapes of $3D$ facial configurations retrieved from digital images,\nvia Agisoft PhotoScan technology.\n",
"title": "3D mean Projective Shape Difference for Face Differentiation from Multiple Digital Camera Images"
}
| null | null |
[
"Statistics"
] | null | true | null |
11491
| null |
Validated
| null | null |
null |
{
"abstract": " Sports channel video portals offer an exciting domain for research on\nmultimodal, multilingual analysis. We present methods addressing the problem of\nautomatic video highlight prediction based on joint visual features and textual\nanalysis of the real-world audience discourse with complex slang, in both\nEnglish and traditional Chinese. We present a novel dataset based on League of\nLegends championships recorded from North American and Taiwanese Twitch.tv\nchannels (will be released for further research), and demonstrate strong\nresults on these using multimodal, character-level CNN-RNN model architectures.\n",
"title": "Video Highlight Prediction Using Audience Chat Reactions"
}
| null | null | null | null | true | null |
11492
| null |
Default
| null | null |
null |
{
"abstract": " We investigate how dynamic correlations of hard-core bosonic excitation at\nfinite temperature are affected by additional interactions besides the\nhard-core repulsion which prevents them from occupying the same site. We focus\nespecially on dimerized spin systems, where these additional interactions\nbetween the elementary excitations, triplons, lead to the formation of bound\nstates, relevant for the correct description of scattering processes. In order\nto include these effects quantitatively we extend the previously developed\nBrückner approach to include also nearest-neighbor (NN) and next-nearest\nneighbor (NNN) interactions correctly in a low-temperature expansion. This\nleads to the extension of the scalar Bethe-Salpeter equation to a matrix-valued\nequation. Exemplarily, we consider the Heisenberg spin ladder to illustrate the\nsignificance of the additional interactions on the spectral functions at finite\ntemperature which are proportional to inelastic neutron scattering rates.\n",
"title": "Effects of Interactions on Dynamic Correlations of Hard-Core Bosons at Finite Temperatures"
}
| null | null | null | null | true | null |
11493
| null |
Default
| null | null |
null |
{
"abstract": " In this article, we consider the problem of estimating the parameters of the\nFréchet distribution from both frequentist and Bayesian points of view. First\nwe briefly describe different frequentist approaches, namely, maximum\nlikelihood, method of moments, percentile estimators, L-moments, ordinary and\nweighted least squares, maximum product of spacings, maximum goodness-of-fit\nestimators and compare them with respect to mean relative estimates, mean\nsquared errors and the 95\\% coverage probability of the asymptotic confidence\nintervals using extensive numerical simulations. Next, we consider the Bayesian\ninference approach using reference priors. The Metropolis-Hasting algorithm is\nused to draw Markov Chain Monte Carlo samples, and they have in turn been used\nto compute the Bayes estimates and also to construct the corresponding credible\nintervals. Five real data sets related to the minimum flow of water on\nPiracicaba river in Brazil are used to illustrate the applicability of the\ndiscussed procedures.\n",
"title": "The Frechet distribution: Estimation and Application an Overview"
}
| null | null | null | null | true | null |
11494
| null |
Default
| null | null |
null |
{
"abstract": " This letter adopts long short-term memory(LSTM) to predict sea surface\ntemperature(SST), which is the first attempt, to our knowledge, to use\nrecurrent neural network to solve the problem of SST prediction, and to make\none week and one month daily prediction. We formulate the SST prediction\nproblem as a time series regression problem. LSTM is a special kind of\nrecurrent neural network, which introduces gate mechanism into vanilla RNN to\nprevent the vanished or exploding gradient problem. It has strong ability to\nmodel the temporal relationship of time series data and can handle the\nlong-term dependency problem well. The proposed network architecture is\ncomposed of two kinds of layers: LSTM layer and full-connected dense layer.\nLSTM layer is utilized to model the time series relationship. Full-connected\nlayer is utilized to map the output of LSTM layer to a final prediction. We\nexplore the optimal setting of this architecture by experiments and report the\naccuracy of coastal seas of China to confirm the effectiveness of the proposed\nmethod. In addition, we also show its online updated characteristics.\n",
"title": "Prediction of Sea Surface Temperature using Long Short-Term Memory"
}
| null | null |
[
"Computer Science"
] | null | true | null |
11495
| null |
Validated
| null | null |
null |
{
"abstract": " There has been great progress recently in formally specifying the memory\nmodel of microprocessors like ARM and POWER. These specifications are, however,\ntoo complicated for reasoning about program behaviors, verifying compilers\netc., because they involve microarchitectural details like the reorder buffer\n(ROB), partial and speculative execution, instruction replay on speculation\nfailure, etc. In this paper we present a new Instantaneous Instruction\nExecution (I2E) framework which allows us to specify weak memory models in the\nsame style as SC and TSO. Each instruction in I2E is executed instantaneously\nand in-order such that the state of the processor is always correct. The effect\nof instruction reordering is captured by the way data is moved between the\nprocessors and the memory non-deterministically, using three conceptual\ndevices: invalidation buffers, timestamps and dynamic store buffers. We prove\nthat I2E models capture the behaviors of modern microarchitectures and\ncache-coherent memory systems accurately, thus eliminating the need to think\nabout microarchitectural details.\n",
"title": "An Operational Framework for Specifying Memory Models using Instantaneous Instruction Execution"
}
| null | null | null | null | true | null |
11496
| null |
Default
| null | null |
null |
{
"abstract": " Lineage tracing, the joint segmentation and tracking of living cells as they\nmove and divide in a sequence of light microscopy images, is a challenging\ntask. Jug et al. have proposed a mathematical abstraction of this task, the\nmoral lineage tracing problem (MLTP), whose feasible solutions define both a\nsegmentation of every image and a lineage forest of cells. Their branch-and-cut\nalgorithm, however, is prone to many cuts and slow convergence for large\ninstances. To address this problem, we make three contributions: (i) we devise\nthe first efficient primal feasible local search algorithms for the MLTP, (ii)\nwe improve the branch-and-cut algorithm by separating tighter cutting planes\nand by incorporating our primal algorithms, (iii) we show in experiments that\nour algorithms find accurate solutions on the problem instances of Jug et al.\nand scale to larger instances, leveraging moral lineage tracing to practical\nsignificance.\n",
"title": "Efficient Algorithms for Moral Lineage Tracing"
}
| null | null |
[
"Computer Science"
] | null | true | null |
11497
| null |
Validated
| null | null |
null |
{
"abstract": " We consider a generalization of $k$-median and $k$-center, called the {\\em\nordered $k$-median} problem. In this problem, we are given a metric space\n$(\\mathcal{D},\\{c_{ij}\\})$ with $n=|\\mathcal{D}|$ points, and a non-increasing\nweight vector $w\\in\\mathbb{R}_+^n$, and the goal is to open $k$ centers and\nassign each point each point $j\\in\\mathcal{D}$ to a center so as to minimize\n$w_1\\cdot\\text{(largest assignment cost)}+w_2\\cdot\\text{(second-largest\nassignment cost)}+\\ldots+w_n\\cdot\\text{($n$-th largest assignment cost)}$. We\ngive an $(18+\\epsilon)$-approximation algorithm for this problem. Our\nalgorithms utilize Lagrangian relaxation and the primal-dual schema, combined\nwith an enumeration procedure of Aouad and Segev. For the special case of\n$\\{0,1\\}$-weights, which models the problem of minimizing the $\\ell$ largest\nassignment costs that is interesting in and of by itself, we provide a novel\nreduction to the (standard) $k$-median problem showing that LP-relative\nguarantees for $k$-median translate to guarantees for the ordered $k$-median\nproblem; this yields a nice and clean $(8.5+\\epsilon)$-approximation algorithm\nfor $\\{0,1\\}$ weights.\n",
"title": "Interpolating between $k$-Median and $k$-Center: Approximation Algorithms for Ordered $k$-Median"
}
| null | null |
[
"Computer Science"
] | null | true | null |
11498
| null |
Validated
| null | null |
null |
{
"abstract": " Brain signal data are inherently big: massive in amount, complex in\nstructure, and high in dimensions. These characteristics impose great\nchallenges for statistical inference and learning. Here we review several key\nchallenges, discuss possible solutions, and highlight future research\ndirections.\n",
"title": "Statistical Challenges in Modeling Big Brain Signals"
}
| null | null |
[
"Statistics"
] | null | true | null |
11499
| null |
Validated
| null | null |
null |
{
"abstract": " Every automorphism-invariant right non-singular $A$-module is injective if\nand only if the factor ring of the ring $A$ with respect to its right Goldie\nradical is a right strongly semiprime ring.\n",
"title": "Injective and Automorphism-Invariant Non-Singular Modules"
}
| null | null | null | null | true | null |
11500
| null |
Default
| null | null |
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