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null | inputs
dict | prediction
null | prediction_agent
null | annotation
list | annotation_agent
null | multi_label
bool 1
class | explanation
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{
"abstract": " The Doppler effect is a shift in the frequency of waves emitted from an\nobject moving relative to the observer. By observing and analysing the Doppler\nshift in electromagnetic waves from astronomical objects, astronomers gain\ngreater insight into the structure and operation of our universe. In this\npaper, a simple technique is described for teaching the basics of the Doppler\neffect to undergraduate astrophysics students using acoustic waves. An\nadvantage of the technique is that it produces a visual representation of the\nacoustic Doppler shift. The equipment comprises a 40 kHz acoustic transmitter\nand a microphone. The sound is bounced off a computer fan and the signal\ncollected by a DrDAQ ADC and processed by a spectrum analyser. Widening of the\nspectrum is observed as the fan power supply potential is increased from 4 to\n12 V.\n",
"title": "Teaching the Doppler Effect in Astrophysics"
}
| null | null | null | null | true | null |
10101
| null |
Default
| null | null |
null |
{
"abstract": " We propose a new linear algebraic approach to the computation of Tarskian\nsemantics in logic. We embed a finite model M in first-order logic with N\nentities in N-dimensional Euclidean space R^N by mapping entities of M to N\ndimensional one-hot vectors and k-ary relations to order-k adjacency tensors\n(multi-way arrays). Second given a logical formula F in prenex normal form, we\ncompile F into a set Sigma_F of algebraic formulas in multi-linear algebra with\na nonlinear operation. In this compilation, existential quantifiers are\ncompiled into a specific type of tensors, e.g., identity matrices in the case\nof quantifying two occurrences of a variable. It is shown that a systematic\nevaluation of Sigma_F in R^N gives the truth value, 1(true) or 0(false), of F\nin M. Based on this framework, we also propose an unprecedented way of\ncomputing the least models defined by Datalog programs in linear spaces via\nmatrix equations and empirically show its effectiveness compared to\nstate-of-the-art approaches.\n",
"title": "Embedding Tarskian Semantics in Vector Spaces"
}
| null | null | null | null | true | null |
10102
| null |
Default
| null | null |
null |
{
"abstract": " We investigate the relation between disk mass and mass accretion rate to\nconstrain the mechanism of angular momentum transport in protoplanetary disks.\nDust mass and mass accretion rate in Chamaeleon I are correlated with a slope\nclose to linear, similar to the one recently identified in Lupus. We\ninvestigate the effect of stellar mass and find that the intrinsic scatter\naround the best-fit Mdust-Mstar and Macc-Mstar relations is uncorrelated. Disks\nwith a constant alpha viscosity can fit the observed relations between dust\nmass, mass accretion rate, and stellar mass, but over-predict the strength of\nthe correlation between disk mass and mass accretion rate when using standard\ninitial conditions. We find two possible solutions. 1) The observed scatter in\nMdust and Macc is not primoridal, but arises from additional physical processes\nor uncertainties in estimating the disk gas mass. Most likely grain growth and\nradial drift affect the observable dust mass, while variability on large time\nscales affects the mass accretion rates. 2) The observed scatter is primordial,\nbut disks have not evolved substantially at the age of Lupus and Chamaeleon I\ndue to a low viscosity or a large initial disk radius. More accurate estimates\nof the disk mass and gas disk sizes in a large sample of protoplanetary disks,\neither through direct observations of the gas or spatially resolved\nmulti-wavelength observations of the dust with ALMA, are needed to discriminate\nbetween both scenarios or to constrain alternative angular momentum transport\nmechanisms such as MHD disk winds.\n",
"title": "Constraints from Dust Mass and Mass Accretion Rate Measurements on Angular Momentum Transport in Protoplanetary Disks"
}
| null | null | null | null | true | null |
10103
| null |
Default
| null | null |
null |
{
"abstract": " We present a Character-Word Long Short-Term Memory Language Model which both\nreduces the perplexity with respect to a baseline word-level language model and\nreduces the number of parameters of the model. Character information can reveal\nstructural (dis)similarities between words and can even be used when a word is\nout-of-vocabulary, thus improving the modeling of infrequent and unknown words.\nBy concatenating word and character embeddings, we achieve up to 2.77% relative\nimprovement on English compared to a baseline model with a similar amount of\nparameters and 4.57% on Dutch. Moreover, we also outperform baseline word-level\nmodels with a larger number of parameters.\n",
"title": "Character-Word LSTM Language Models"
}
| null | null | null | null | true | null |
10104
| null |
Default
| null | null |
null |
{
"abstract": " Machine learning methods have found many applications in Raman spectroscopy,\nespecially for the identification of chemical species. However, almost all of\nthese methods require non-trivial preprocessing such as baseline correction\nand/or PCA as an essential step. Here we describe our unified solution for the\nidentification of chemical species in which a convolutional neural network is\ntrained to automatically identify substances according to their Raman spectrum\nwithout the need of ad-hoc preprocessing steps. We evaluated our approach using\nthe RRUFF spectral database, comprising mineral sample data. Superior\nclassification performance is demonstrated compared with other frequently used\nmachine learning algorithms including the popular support vector machine.\n",
"title": "Deep Convolutional Neural Networks for Raman Spectrum Recognition: A Unified Solution"
}
| null | null | null | null | true | null |
10105
| null |
Default
| null | null |
null |
{
"abstract": " The effect of monolayers of oxygen (O) and hydrogen (H) on the possibility of\nmaterial transfer at aluminium/titanium nitride (Al/TiN) and copper/diamond\n(Cu/C$_{\\text{dia}}$) interfaces, respectively, were investigated within the\nframework of density functional theory (DFT). To this end the approach,\ncontact, and subsequent separation of two atomically flat surfaces consisting\nof the aforementioned pairs of materials were simulated. These calculations\nwere performed for the clean as well as oxygenated and hydrogenated Al and\nC$_{\\text{dia}}$ surfaces, respectively. Various contact configurations were\nconsidered by studying several lateral arrangements of the involved surfaces at\nthe interface. Material transfer is typically possible at interfaces between\nthe investigated clean surfaces; however, the addition of O to the Al and H to\nthe C$_{\\text{dia}}$ surfaces was found to hinder material transfer. This\npassivation occurs because of a significant reduction of the adhesion energy at\nthe examined interfaces, which can be explained by the distinct bonding\nsituations.\n",
"title": "Suppression of material transfer at contacting surfaces: The effect of adsorbates on Al/TiN and Cu/diamond interfaces from first-principles calculations"
}
| null | null | null | null | true | null |
10106
| null |
Default
| null | null |
null |
{
"abstract": " Early recognition of abnormal rhythm in ECG signals is crucial for monitoring\nor diagnosing patients' cardiac conditions and increasing the success rate of\nthe treatment. Classifying abnormal rhythms into fine-grained categories is\nvery challenging due to the the broad taxonomy of rhythms, noises and lack of\nreal-world data and annotations from large number of patients. This paper\npresents a new ECG classification method based on Deep Convolutional Neural\nNetworks (DCNN) and online decision fusion. Different from previous methods\nwhich utilize hand-crafted features or learn features from the original signal\ndomain, the proposed DCNN based method learns features and classifiers from the\ntime-frequency domain in an end-to-end manner. First, the ECG wave signal is\ntransformed to time-frequency domain by using Short-Time Fourier Transform.\nNext, specific DCNN models are trained on ECG samples of specific length.\nFinally, an online decision fusion method is proposed to fuse past and current\ndecisions from different models into a more accurate one. Experimental results\non both synthetic and real-world ECG datasets convince the effectiveness and\nefficiency of the proposed method.\n",
"title": "Fine-grained ECG Classification Based on Deep CNN and Online Decision Fusion"
}
| null | null | null | null | true | null |
10107
| null |
Default
| null | null |
null |
{
"abstract": " Currently, most speech processing techniques use magnitude spectrograms as\nfront-end and are therefore by default discarding part of the signal: the\nphase. In order to overcome this limitation, we propose an end-to-end learning\nmethod for speech denoising based on Wavenet. The proposed model adaptation\nretains Wavenet's powerful acoustic modeling capabilities, while significantly\nreducing its time-complexity by eliminating its autoregressive nature.\nSpecifically, the model makes use of non-causal, dilated convolutions and\npredicts target fields instead of a single target sample. The discriminative\nadaptation of the model we propose, learns in a supervised fashion via\nminimizing a regression loss. These modifications make the model highly\nparallelizable during both training and inference. Both computational and\nperceptual evaluations indicate that the proposed method is preferred to Wiener\nfiltering, a common method based on processing the magnitude spectrogram.\n",
"title": "A Wavenet for Speech Denoising"
}
| null | null |
[
"Computer Science"
] | null | true | null |
10108
| null |
Validated
| null | null |
null |
{
"abstract": " Many classical results in relativity theory concerning spherically symmetric\nspace-times have easy generalizations to warped product space-times, with a\ntwo-dimensional Lorentzian base and arbitrary dimensional Riemannian fibers. We\nfirst give a systematic presentation of the main geometric constructions, with\nemphasis on the Kodama vector field and the Hawking energy; the construction is\nsignature independent. This leads to proofs of general Birkhoff-type theorems\nfor warped product manifolds; our theorems in particular apply to situations\nwhere the warped product manifold is not necessarily Einstein, and thus can be\napplied to solutions with matter content in general relativity. Next we\nspecialize to the Lorentzian case and study the propagation of null expansions\nunder the assumption of the dominant energy condition. We prove several\nnon-existence results relating to the Yamabe class of the fibers, in the spirit\nof the black-hole topology theorem of Hawking-Galloway-Schoen. Finally we\ndiscuss the effect of the warped product ansatz on matter models. In particular\nwe construct several cosmological solutions to the Einstein-Euler equations\nwhose spatial geometry is generally not isotropic.\n",
"title": "Warped Product Space-times"
}
| null | null | null | null | true | null |
10109
| null |
Default
| null | null |
null |
{
"abstract": " We present ALMA observations of the 2M1207 system, a young binary made of a\nbrown dwarf with a planetary-mass companion at a projected separation of about\n40 au. We detect emission from dust continuum at 0.89 mm and from the $J = 3 -\n2$ rotational transition of CO from a very compact disk around the young brown\ndwarf. The small radius found for this brown dwarf disk may be due to\ntruncation from the tidal interaction with the planetary-mass companion. Under\nthe assumption of optically thin dust emission, we estimated a dust mass of 0.1\n$M_{\\oplus}$ for the 2M1207A disk, and a 3$\\sigma$ upper limit of $\\sim\n1~M_{\\rm{Moon}}$ for dust surrounding 2M1207b, which is the tightest upper\nlimit obtained so far for the mass of dust particles surrounding a young\nplanetary-mass companion. We discuss the impact of this and other\nnon-detections of young planetary-mass companions for models of planet\nformation, which predict the presence of circum-planetary material surrounding\nthese objects.\n",
"title": "ALMA Observations of the Young Substellar Binary System 2M1207"
}
| null | null |
[
"Physics"
] | null | true | null |
10110
| null |
Validated
| null | null |
null |
{
"abstract": " A class of methods based on multichannel linear prediction (MCLP) can achieve\neffective blind dereverberation of a source, when the source is observed with a\nmicrophone array. We propose an inventive use of MCLP as a pre-processing step\nfor blind source separation with a microphone array. We show theoretically\nthat, under certain assumptions, such pre-processing reduces the original blind\nreverberant source separation problem to a non-reverberant one, which in turn\ncan be effectively tackled using existing methods. We demonstrate our claims\nusing real recordings obtained with an eight-microphone circular array in\nreverberant environments.\n",
"title": "Multichannel Linear Prediction for Blind Reverberant Audio Source Separation"
}
| null | null | null | null | true | null |
10111
| null |
Default
| null | null |
null |
{
"abstract": " Many successful methods have been proposed for learning low dimensional\nrepresentations on large-scale networks, while almost all existing methods are\ndesigned in inseparable processes, learning embeddings for entire networks even\nwhen only a small proportion of nodes are of interest. This leads to great\ninconvenience, especially on super-large or dynamic networks, where these\nmethods become almost impossible to implement. In this paper, we formalize the\nproblem of separated matrix factorization, based on which we elaborate a novel\nobjective function that preserves both local and global information. We further\npropose SepNE, a simple and flexible network embedding algorithm which\nindependently learns representations for different subsets of nodes in\nseparated processes. By implementing separability, our algorithm reduces the\nredundant efforts to embed irrelevant nodes, yielding scalability to\nsuper-large networks, automatic implementation in distributed learning and\nfurther adaptations. We demonstrate the effectiveness of this approach on\nseveral real-world networks with different scales and subjects. With comparable\naccuracy, our approach significantly outperforms state-of-the-art baselines in\nrunning times on large networks.\n",
"title": "SepNE: Bringing Separability to Network Embedding"
}
| null | null | null | null | true | null |
10112
| null |
Default
| null | null |
null |
{
"abstract": " We present a user of model interaction based on the physics of kinetic\nexchange, and extend it to individuals placed in a grid with local interaction.\nWe show with numerical analysis and partial analytical results that the\ncritical symmetry breaking transitions and percolation effects typical of the\nfull interaction model do not take place if the range of interaction is\nlimited, allowing for the co-existence of majorty and minority opinions in the\nsame community.\nWe then introduce a peer recommender system in the model, showing that, even\nwith very local iteraction and a small probability of appeal to the\nrecommender, its presence is sufficient to make both symmetry breaking and\npercolation reappear. This seems to indicate that one effect of a\nrecommendation system is to uniform the opinions of a community, reducing\nminority opinions or making them disappear. Although the recommender system\ndoes uniform the community opinion, it doesn't constrain it, in the sense that\nall opinions have the same probability of becoming the dominating one. We do a\npartial study, however, that suggests that a \"mischievous\" recommender might be\nable to bias a community so that one opinion will emerge over the opposite with\noverwhelming probability.\n",
"title": "Opinion formation in a locally interacting community with recommender"
}
| null | null | null | null | true | null |
10113
| null |
Default
| null | null |
null |
{
"abstract": " We consider the supervised learning problem with shallow neural networks.\nAccording to our unpublished experiments conducted several years prior to this\nstudy, we had noticed an interesting similarity between the distribution of\nhidden parameters after backprobagation (BP) training, and the ridgelet\nspectrum of the same dataset. Therefore, we conjectured that the distribution\nis expressed as a version of ridgelet transform, but it was not proven until\nthis study. One difficulty is that both the local minimizers and the ridgelet\ntransforms have an infinite number of varieties, and no relations are known\nbetween them. By using the integral representation, we reformulate the BP\ntraining as a strong-convex optimization problem and find a global minimizer.\nFinally, by developing ridgelet analysis on a reproducing kernel Hilbert space\n(RKHS), we write the minimizer explicitly and succeed to prove the conjecture.\nThe modified ridgelet transform has an explicit expression that can be computed\nby numerical integration, which suggests that we can obtain the global\nminimizer of BP, without BP.\n",
"title": "Integral representation of shallow neural network that attains the global minimum"
}
| null | null | null | null | true | null |
10114
| null |
Default
| null | null |
null |
{
"abstract": " Software engineering considers performance evaluation to be one of the key\nportions of software quality assurance. Unfortunately, there seems to be a lack\nof standard methodologies for performance evaluation even in the scope of\nexperimental computer science. Inspired by the concept of \"instantiation\" in\nobject-oriented programming, we distinguish the generic performance evaluation\nlogic from the distributed and ad-hoc relevant studies, and develop an abstract\nevaluation methodology (by analogy of \"class\") we name Domain Knowledge-driven\nMethodology (DoKnowMe). By replacing five predefined domain-specific knowledge\nartefacts, DoKnowMe could be instantiated into specific methodologies (by\nanalogy of \"object\") to guide evaluators in performance evaluation of different\nsoftware and even computing systems. We also propose a generic validation\nframework with four indicators (i.e.~usefulness, feasibility, effectiveness and\nrepeatability), and use it to validate DoKnowMe in the Cloud services\nevaluation domain. Given the positive and promising validation result, we plan\nto integrate more common evaluation strategies to improve DoKnowMe and further\nfocus on the performance evaluation of Cloud autoscaler systems.\n",
"title": "DoKnowMe: Towards a Domain Knowledge-driven Methodology for Performance Evaluation"
}
| null | null | null | null | true | null |
10115
| null |
Default
| null | null |
null |
{
"abstract": " Porous silicon layers (PS) have been prepared in this work via\nPhotoelectrochemical etching process (PEC) of n type silicon wafer of 0.8\nohm.cm resistivity in hydrofluoric (HF) acid of 24.5 precent concentration at\ndifferent etching times (5 to 25 min.). The irradiation has been achieved using\nTungsten lamp with different wavelengths (450 nm, 535 nm and 700 nm). The\nmorphological properties of these layers such as surface morphology, Porosity,\nlayer thickness, and also the etching rate have been investigated using optical\nmicroscopy and the gravimetric method.\n",
"title": "The Effect of Different Wavelengths on Porous Silicon Formation Process"
}
| null | null | null | null | true | null |
10116
| null |
Default
| null | null |
null |
{
"abstract": " Connectivity patterns of relevance in neuroscience and systems biology can be\nencoded in hierarchical modular networks (HMNs). Moreover, recent studies\nhighlight the role of hierarchical modular organization in shaping brain\nactivity patterns, providing an excellent substrate to promote both the\nsegregation and integration of neural information. Here we propose an extensive\nnumerical analysis of the critical spreading rate (or \"epidemic\" threshold)\n--separating a phase with endemic persistent activity from one in which\nactivity ceases-- on diverse HMNs. By employing analytical and computational\ntechniques we determine the nature of such a threshold and scrutinize how it\ndepends on general structural features of the underlying HMN. We critically\ndiscuss the extent to which current graph-spectral methods can be applied to\npredict the onset of spreading in HMNs, and we propose the network topological\ndimension as a relevant and unifying structural parameter, controlling the\nepidemic threshold.\n",
"title": "Topological dimension tunes activity patterns in hierarchical modular network models"
}
| null | null | null | null | true | null |
10117
| null |
Default
| null | null |
null |
{
"abstract": " Deep Convolutional Neural Networks (DCNNs) are currently popular in human\nactivity recognition applications. However, in the face of modern artificial\nintelligence sensor-based games, many research achievements cannot be\npractically applied on portable devices. DCNNs are typically resource-intensive\nand too large to be deployed on portable devices, thus this limits the\npractical application of complex activity detection. In addition, since\nportable devices do not possess high-performance Graphic Processing Units\n(GPUs), there is hardly any improvement in Action Game (ACT) experience.\nBesides, in order to deal with multi-sensor collaboration, all previous human\nactivity recognition models typically treated the representations from\ndifferent sensor signal sources equally. However, distinct types of activities\nshould adopt different fusion strategies. In this paper, a novel scheme is\nproposed. This scheme is used to train 2-bit Convolutional Neural Networks with\nweights and activations constrained to {-0.5,0,0.5}. It takes into account the\ncorrelation between different sensor signal sources and the activity types.\nThis model, which we refer to as DFTerNet, aims at producing a more reliable\ninference and better trade-offs for practical applications. Our basic idea is\nto exploit quantization of weights and activations directly in pre-trained\nfilter banks and adopt dynamic fusion strategies for different activity types.\nExperiments demonstrate that by using dynamic fusion strategy can exceed the\nbaseline model performance by up to ~5% on activity recognition like\nOPPORTUNITY and PAMAP2 datasets. Using the quantization method proposed, we\nwere able to achieve performances closer to that of full-precision counterpart.\nThese results were also verified using the UniMiB-SHAR dataset. In addition,\nthe proposed method can achieve ~9x acceleration on CPUs and ~11x memory\nsaving.\n",
"title": "DFTerNet: Towards 2-bit Dynamic Fusion Networks for Accurate Human Activity Recognition"
}
| null | null | null | null | true | null |
10118
| null |
Default
| null | null |
null |
{
"abstract": " We study translation invariant stochastic processes on $\\mathbb{R}^d$ or\n$\\mathbb{Z}^d$ whose diffraction spectrum or structure function $S(k)$, i.e.\nthe Fourier transform of the truncated total pair correlation function,\nvanishes on an open set $U$ in the wave space. A key family of such processes\nare stealthy hyperuniform point processes, for which the origin $k=0$ is in\n$U$; these are of much current physical interest. We show that all such\nprocesses exhibit the following remarkable maximal rigidity : namely, the\nconfiguration outside a bounded region determines, with probability 1, the\nexact value (or the exact locations of the points) of the process inside the\nregion. In particular, such processes are completely determined by their tail.\nIn the 1D discrete setting (i.e. $\\mathbb{Z}$-valued processes on\n$\\mathbb{Z}$), this can also be seen as a consequence of a recent theorem of\nBorichev, Sodin and Weiss; in higher dimensions or in the continuum, such a\nphenomenon seems novel. For stealthy hyperuniform point processes, we prove the\nZhang-Stillinger-Torquato conjecture that such processes have bounded holes\n(empty regions), with a universal bound that depends inversely on the size of\n$U$.\n",
"title": "Generalized stealthy hyperuniform processes : maximal rigidity and the bounded holes conjecture"
}
| null | null | null | null | true | null |
10119
| null |
Default
| null | null |
null |
{
"abstract": " We study the high-frequency behavior of the Dirichlet-to-Neumann map for an\narbitrary compact Riemannian manifold with a non-empty smooth boundary. We show\nthat far from the real axis it can be approximated by a simpler operator. We\nuse this fact to get new results concerning the location of the transmission\neigenvalues on the complex plane. In some cases we obtain optimal transmission\neigenvalue-free regions.\n",
"title": "High-frequency approximation of the interior dirichlet-to-neumann map and applications to the transmission eigenvalues"
}
| null | null |
[
"Mathematics"
] | null | true | null |
10120
| null |
Validated
| null | null |
null |
{
"abstract": " By using the Lyapunov-Schmidt reduction method without perturbation, we\nconsider existence results for the conformal scalar curvature on S^n (n greater\nor equal to 3) when the prescribed function (after being projected to R^n) has\ntwo close critical points, which have the same value (positive), equal\n\"flatness\" (twin, flatness < n - 2), and exhibit maximal behavior in certain\ndirections (pseudo-peaks). The proof relies on a balance between the two main\ncontributions to the reduced functional - one from the critical points and the\nother from the interaction of the two bubbles.\n",
"title": "Conformal scalar curvature equation on S^n: functions with two close critical points (twin pseudo-peaks)"
}
| null | null | null | null | true | null |
10121
| null |
Default
| null | null |
null |
{
"abstract": " A general formalism is introduced to allow the steady state of non-Markovian\nprocesses on networks to be reduced to equivalent Markovian processes on the\nsame substrates. The example of an epidemic spreading process is considered in\ndetail, where all the non-Markovian aspects are shown to be captured within a\nsingle parameter, the effective infection rate. Remarkably, this result is\nindependent of the topology of the underlying network, as demonstrated by\nnumerical simulations on two-dimensional lattices and various types of random\nnetworks. Furthermore, an analytic approximation for the effective infection\nrate is introduced, which enables the calculation of the critical point and of\nthe critical exponents for the non-Markovian dynamics.\n",
"title": "Equivalence between non-Markovian and Markovian dynamics in epidemic spreading processes"
}
| null | null | null | null | true | null |
10122
| null |
Default
| null | null |
null |
{
"abstract": " Neural networks with low-precision weights and activations offer compelling\nefficiency advantages over their full-precision equivalents. The two most\nfrequently discussed benefits of quantization are reduced memory consumption,\nand a faster forward pass when implemented with efficient bitwise operations.\nWe propose a third benefit of very low-precision neural networks: improved\nrobustness against some adversarial attacks, and in the worst case, performance\nthat is on par with full-precision models. We focus on the very low-precision\ncase where weights and activations are both quantized to $\\pm$1, and note that\nstochastically quantizing weights in just one layer can sharply reduce the\nimpact of iterative attacks. We observe that non-scaled binary neural networks\nexhibit a similar effect to the original defensive distillation procedure that\nled to gradient masking, and a false notion of security. We address this by\nconducting both black-box and white-box experiments with binary models that do\nnot artificially mask gradients.\n",
"title": "Attacking Binarized Neural Networks"
}
| null | null | null | null | true | null |
10123
| null |
Default
| null | null |
null |
{
"abstract": " Stable Marriage is a fundamental problem to both computer science and\neconomics. Four well-known NP-hard optimization versions of this problem are\nthe Sex-Equal Stable Marriage (SESM), Balanced Stable Marriage (BSM),\nmax-Stable Marriage with Ties (max-SMT) and min-Stable Marriage with Ties\n(min-SMT) problems. In this paper, we analyze these problems from the viewpoint\nof Parameterized Complexity. We conduct the first study of these problems with\nrespect to the parameter treewidth. First, we study the treewidth $\\mathtt{tw}$\nof the primal graph. We establish that all four problems are W[1]-hard. In\nparticular, while it is easy to show that all four problems admit algorithms\nthat run in time $n^{O(\\mathtt{tw})}$, we prove that all of these algorithms\nare likely to be essentially optimal. Next, we study the treewidth\n$\\mathtt{tw}$ of the rotation digraph. In this context, the max-SMT and min-SMT\nare not defined. For both SESM and BSM, we design (non-trivial) algorithms that\nrun in time $2^{\\mathtt{tw}}n^{O(1)}$. Then, for both SESM and BSM, we also\nprove that unless SETH is false, algorithms that run in time\n$(2-\\epsilon)^{\\mathtt{tw}}n^{O(1)}$ do not exist for any fixed $\\epsilon>0$.\nWe thus present a comprehensive, complete picture of the behavior of central\noptimization versions of Stable Marriage with respect to treewidth.\n",
"title": "On Treewidth and Stable Marriage"
}
| null | null | null | null | true | null |
10124
| null |
Default
| null | null |
null |
{
"abstract": " Electricity market price predictions enable energy market participants to\nshape their consumption or supply while meeting their economic and\nenvironmental objectives. By utilizing the basic properties of the\nsupply-demand matching process performed by grid operators, we develop a method\nto recover energy market's structure and predict the resulting nodal prices as\na function of generation mix and system load on the grid. Our methodology uses\nthe latest advancements in compressed sensing and statistics to cope with the\nhigh-dimensional and sparse power grid topologies, underlying physical laws, as\nwell as scarce, public market data. Rigorous validations using Southwest Power\nPool (SPP) market data demonstrate significantly higher accuracy of the\nproposed approach when compared to the state-of-the-art industry benchmark.\n",
"title": "A Holistic Approach to Forecasting Wholesale Energy Market Prices"
}
| null | null |
[
"Computer Science",
"Statistics"
] | null | true | null |
10125
| null |
Validated
| null | null |
null |
{
"abstract": " Community detection or clustering is a fundamental task in the analysis of\nnetwork data. Many real networks have a bipartite structure which makes\ncommunity detection challenging. In this paper, we consider a model which\nallows for matched communities in the bipartite setting, in addition to node\ncovariates with information about the matching. We derive a simple fast\nalgorithm for fitting the model based on variational inference ideas and show\nits effectiveness on both simulated and real data. A variation of the model to\nallow for degree-correction is also considered, in addition to a novel approach\nto fitting such degree-corrected models.\n",
"title": "Matched bipartite block model with covariates"
}
| null | null | null | null | true | null |
10126
| null |
Default
| null | null |
null |
{
"abstract": " The topological interference management (TIM) problem studies\npartially-connected interference networks with no channel state information\nexcept for the network topology (i.e., connectivity graph) at the transmitters.\nIn this paper, we consider a similar problem in the uplink cellular networks,\nwhile message passing is enabled at the receivers (e.g., base stations), so\nthat the decoded messages can be routed to other receivers via backhaul links\nto help further improve network performance. For this TIM problem with decoded\nmessage passing (TIM-MP), we model the interference pattern by conflict\ndigraphs, connect orthogonal access to the acyclic set coloring on conflict\ndigraphs, and show that one-to-one interference alignment boils down to\northogonal access because of message passing. With the aid of polyhedral\ncombinatorics, we identify the structural properties of certain classes of\nnetwork topologies where orthogonal access achieves the optimal\ndegrees-of-freedom (DoF) region in the information-theoretic sense. The\nrelation to the conventional index coding with simultaneous decoding is also\ninvestigated by formulating a generalized index coding problem with successive\ndecoding as a result of decoded message passing. The properties of reducibility\nand criticality are also studied, by which we are able to prove the linear\noptimality of orthogonal access in terms of symmetric DoF for the networks up\nto four users with all possible network topologies (218 instances). Practical\nissues of the tradeoff between the overhead of message passing and the\nachievable symmetric DoF are also discussed, in the hope of facilitating\nefficient backhaul utilization.\n",
"title": "Topological Interference Management with Decoded Message Passing"
}
| null | null | null | null | true | null |
10127
| null |
Default
| null | null |
null |
{
"abstract": " This paper studies robust regression in the settings of Huber's\n$\\epsilon$-contamination models. We consider estimators that are maximizers of\nmultivariate regression depth functions. These estimators are shown to achieve\nminimax rates in the settings of $\\epsilon$-contamination models for various\nregression problems including nonparametric regression, sparse linear\nregression, reduced rank regression, etc. We also discuss a general notion of\ndepth function for linear operators that has potential applications in robust\nfunctional linear regression.\n",
"title": "Robust Regression via Mutivariate Regression Depth"
}
| null | null | null | null | true | null |
10128
| null |
Default
| null | null |
null |
{
"abstract": " We introduce structures which model the quotients of buildings by\ntype-preserving group actions. These structures, which we call W-groupoids,\ngeneralize Bruhat decompositions, chambers systems of type M, and Tits\namalgams. We define the fundamental group of a W-groupoid, and characterize\nbuildings as connected simply connected W-groupoids. We give a brief outline of\nthe covering theory of W-groupoids, which produces buildings as the universal\ncovers of W-groupoids. The local-to-global theorem of Tits concerning spherical\n3-resides allows for the construction of W-groupoids by gluing together\nquotients of generalized polygons. In this way, W-groupoids can be used to\nconstruct exotic, hyperbolic, and wild buildings.\n",
"title": "Quotients of Buildings as $W$-Groupoids"
}
| null | null |
[
"Mathematics"
] | null | true | null |
10129
| null |
Validated
| null | null |
null |
{
"abstract": " In this paper, the origin of the generalized uncertainty principle (GUP) in\nan $M$-dimensional theory with Lie-$N$-algebra is considered. This theory which\nwe name GLNA(Generalized Lie-$N$-Algebra)-theory can be reduced to $M$-theory\nwith $M=11$ and $N=3$. In this theory, at the beginning, two energies with\npositive and negative signs are created from nothing and produce two types of\nbranes with opposite quantum numbers and different numbers of timing\ndimensions. Coincidence with the birth of these branes, various derivatives of\nbosonic fields emerge in the action of the system which produce the $r$ GUP for\nbosons. These branes interact with each other, compact and various derivatives\nof spinor fields appear in the action of the system which leads to the creation\nof the GUP for fermions. The previous predicted entropy of branes in the GUP is\ncorrected as due to the emergence of higher orders of derivatives and different\nnumber of timing dimensions.\n",
"title": "Birth of the GUP and its effect on the entropy of the Universe in Lie-$N$-algebra"
}
| null | null | null | null | true | null |
10130
| null |
Default
| null | null |
null |
{
"abstract": " We consider nonparametric inference of finite dimensional, potentially\nnon-pathwise differentiable target parameters. In a nonparametric model, some\nexamples of such parameters that are always non pathwise differentiable target\nparameters include probability density functions at a point, or regression\nfunctions at a point. In causal inference, under appropriate causal\nassumptions, mean counterfactual outcomes can be pathwise differentiable or\nnot, depending on the degree at which the positivity assumption holds.\nIn this paper, given a potentially non-pathwise differentiable target\nparameter, we introduce a family of approximating parameters, that are pathwise\ndifferentiable. This family is indexed by a scalar. In kernel regression or\ndensity estimation for instance, a natural choice for such a family is obtained\nby kernel smoothing and is indexed by the smoothing level. For the\ncounterfactual mean outcome, a possible approximating family is obtained\nthrough truncation of the propensity score, and the truncation level then plays\nthe role of the index.\nWe propose a method to data-adaptively select the index in the family, so as\nto optimize mean squared error. We prove an asymptotic normality result, which\nallows us to derive confidence intervals. Under some conditions, our estimator\nachieves an optimal mean squared error convergence rate. Confidence intervals\nare data-adaptive and have almost optimal width.\nA simulation study demonstrates the practical performance of our estimators\nfor the inference of a causal dose-response curve at a given treatment dose.\n",
"title": "Data-adaptive smoothing for optimal-rate estimation of possibly non-regular parameters"
}
| null | null | null | null | true | null |
10131
| null |
Default
| null | null |
null |
{
"abstract": " Knowledge base completion (KBC) aims to predict missing information in a\nknowledge base.In this paper, we address the out-of-knowledge-base (OOKB)\nentity problem in KBC:how to answer queries concerning test entities not\nobserved at training time. Existing embedding-based KBC models assume that all\ntest entities are available at training time, making it unclear how to obtain\nembeddings for new entities without costly retraining. To solve the OOKB entity\nproblem without retraining, we use graph neural networks (Graph-NNs) to compute\nthe embeddings of OOKB entities, exploiting the limited auxiliary knowledge\nprovided at test time.The experimental results show the effectiveness of our\nproposed model in the OOKB setting.Additionally, in the standard KBC setting in\nwhich OOKB entities are not involved, our model achieves state-of-the-art\nperformance on the WordNet dataset. The code and dataset are available at\nthis https URL\n",
"title": "Knowledge Transfer for Out-of-Knowledge-Base Entities: A Graph Neural Network Approach"
}
| null | null |
[
"Computer Science"
] | null | true | null |
10132
| null |
Validated
| null | null |
null |
{
"abstract": " Feature engineering is a crucial step in the process of predictive modeling.\nIt involves the transformation of given feature space, typically using\nmathematical functions, with the objective of reducing the modeling error for a\ngiven target. However, there is no well-defined basis for performing effective\nfeature engineering. It involves domain knowledge, intuition, and most of all,\na lengthy process of trial and error. The human attention involved in\noverseeing this process significantly influences the cost of model generation.\nWe present a new framework to automate feature engineering. It is based on\nperformance driven exploration of a transformation graph, which systematically\nand compactly enumerates the space of given options. A highly efficient\nexploration strategy is derived through reinforcement learning on past\nexamples.\n",
"title": "Feature Engineering for Predictive Modeling using Reinforcement Learning"
}
| null | null | null | null | true | null |
10133
| null |
Default
| null | null |
null |
{
"abstract": " It is shown that any two cellular automata (CA) in rule space can be\nconnected by a continuous path parameterized by a real number $\\kappa \\in (0,\n\\infty)$, each point in the path corresponding to a coupled map lattice (CML).\nIn the limits $\\kappa \\to 0$ and $\\kappa \\to \\infty$ the CML becomes each of\nthe two CA entering in the connection. A mean-field, reduced model is obtained\nfrom the connection and allows to gain insight in those parameter regimes at\nintermediate $\\kappa$ where the dynamics is approximately homogeneous within\neach neighborhood.\n",
"title": "Cellular automata connections"
}
| null | null | null | null | true | null |
10134
| null |
Default
| null | null |
null |
{
"abstract": " We discuss an \"operational\" approach to testing convex composite hypotheses\nwhen the underlying distributions are heavy-tailed. It relies upon Euclidean\nseparation of convex sets and can be seen as an extension of the approach to\ntesting by convex optimization developed in [8, 12]. In particular, we show how\none can construct quasi-optimal testing procedures for families of\ndistributions which are majorated, in a certain precise sense, by a\nsub-spherical symmetric one and study the relationship between tests based on\nEuclidean separation and \"potential-based tests.\" We apply the promoted\nmethodology in the problem of sequential detection and illustrate its practical\nimplementation in an application to sequential detection of changes in the\ninput of a dynamic system.\n[8] Goldenshluger, Alexander and Juditsky, Anatoli and Nemirovski, Arkadi,\nHypothesis testing by convex optimization, Electronic Journal of Statistics,9\n(2):1645-1712, 2015. [12] Juditsky, Anatoli and Nemirovski, Arkadi, Hypothesis\ntesting via affine detectors, Electronic Journal of Statistics, 10:2204--2242,\n2016.\n",
"title": "Hypothesis Testing via Euclidean Separation"
}
| null | null | null | null | true | null |
10135
| null |
Default
| null | null |
null |
{
"abstract": " We present experimental data and simulations on the effects of in-plane\ntension on nanoindentation hardness and pop-in noise. Nanoindentation\nexperiments using a Berkovich tip are performed on bulk polycrystaline Al\nsamples, under tension in a custom 4pt-bending fixture. The hardness displays a\ntransition, for indentation depths smaller than 10nm, as function of the\nin-plane stress at a value consistent with the bulk tensile yield stress.\nDisplacement bursts appear insensitive to in-plane tension and this transition\ndisappears for larger indentation depths. Two dimensional discrete dislocation\ndynamics simulations confirm that a regime exists where hardness is sensitive\nto tension-induced pre-existing dislocations.\n",
"title": "Detecting in-plane tension induced crystal plasticity transition with nanoindentation"
}
| null | null | null | null | true | null |
10136
| null |
Default
| null | null |
null |
{
"abstract": " Frequent Pattern Mining is a one field of the most significant topics in data\nmining. In recent years, many algorithms have been proposed for mining frequent\nitemsets. A new algorithm has been presented for mining frequent itemsets based\non N-list data structure called Prepost algorithm. The Prepost algorithm is\nenhanced by implementing compact PPC-tree with the general tree. Prepost\nalgorithm can only find a frequent itemsets with required (pre-order and\npost-order) for each node. In this chapter, we improved prepost algorithm based\non Hadoop platform (HPrepost), proposed using the Mapreduce programming model.\nThe main goals of proposed method are efficient mining frequent itemsets\nrequiring less running time and memory usage. We have conduct experiments for\nthe proposed scheme to compare with another algorithms. With dense datasets,\nwhich have a large average length of transactions, HPrepost is more effective\nthan frequent itemsets algorithms in terms of execution time and memory usage\nfor all min-sup. Generally, our algorithm outperforms algorithms in terms of\nruntime and memory usage with small thresholds and large datasets.\n",
"title": "A novel approach for fast mining frequent itemsets use N-list structure based on MapReduce"
}
| null | null | null | null | true | null |
10137
| null |
Default
| null | null |
null |
{
"abstract": " We study the dynamics of the Fermi-Hubbard model driven by a time-periodic\nmodulation of the interaction within nonequilibrium Dynamical Mean-Field\nTheory. For moderate interaction, we find clear evidence of thermalization to a\ngenuine infinite-temperature state with no residual oscillations. Quite\ndifferently, in the strongly correlated regime, we find a quasi-stationary\nextremely long-lived state with oscillations synchronized with the drive\n(Floquet prethermalization). Remarkably, the nature of this state dramatically\nchanges upon tuning the drive frequency. In particular, we show the existence\nof a critical frequency at which the system rapidly thermalizes despite the\nlarge interaction. We characterize this resonant thermalization and provide an\nanalytical understanding in terms of a break down of the periodic\nSchrieffer-Wolff transformation.\n",
"title": "Resonant thermalization of periodically driven strongly correlated electrons"
}
| null | null |
[
"Physics"
] | null | true | null |
10138
| null |
Validated
| null | null |
null |
{
"abstract": " We exhibit an $O((\\log k)^6)$-competitive randomized algorithm for the\n$k$-server problem on any metric space. It is shown that a potential-based\nalgorithm for the fractional $k$-server problem on hierarchically separated\ntrees (HSTs) with competitive ratio $f(k)$ can be used to obtain a randomized\nalgorithm for any metric space with competitive ratio $f(k)^2 O((\\log k)^2)$.\nEmploying the $O((\\log k)^2)$-competitive algorithm for HSTs from our joint\nwork with Bubeck, Cohen, Lee, and Mądry (2017) yields the claimed bound.\nThe best previous result independent of the geometry of the underlying metric\nspace is the $2k-1$ competitive ratio established for the deterministic work\nfunction algorithm by Koutsoupias and Papadimitriou (1995). Even for the\nspecial case when the underlying metric space is the real line, the best known\ncompetitive ratio was $k$. Since deterministic algorithms can do no better than\n$k$ on any metric space with at least $k+1$ points, this establishes that for\nevery metric space on which the problem is non-trivial, randomized algorithms\ngive an exponential improvement over deterministic algorithms.\n",
"title": "Fusible HSTs and the randomized k-server conjecture"
}
| null | null | null | null | true | null |
10139
| null |
Default
| null | null |
null |
{
"abstract": " We provide a physical definition of new homological invariants $\\mathcal{H}_a\n(M_3)$ of 3-manifolds (possibly, with knots) labeled by abelian flat\nconnections. The physical system in question involves a 6d fivebrane theory on\n$M_3$ times a 2-disk, $D^2$, whose Hilbert space of BPS states plays the role\nof a basic building block in categorification of various partition functions of\n3d $\\mathcal{N}=2$ theory $T[M_3]$: $D^2\\times S^1$ half-index, $S^2\\times S^1$\nsuperconformal index, and $S^2\\times S^1$ topologically twisted index. The\nfirst partition function is labeled by a choice of boundary condition and\nprovides a refinement of Chern-Simons (WRT) invariant. A linear combination of\nthem in the unrefined limit gives the analytically continued WRT invariant of\n$M_3$. The last two can be factorized into the product of half-indices. We show\nhow this works explicitly for many examples, including Lens spaces, circle\nfibrations over Riemann surfaces, and plumbed 3-manifolds.\n",
"title": "BPS spectra and 3-manifold invariants"
}
| null | null | null | null | true | null |
10140
| null |
Default
| null | null |
null |
{
"abstract": " We prove, by topological methods, new results on the existence of nonzero\npositive weak solutions for a class of multi-parameter second order elliptic\nsystems subject to functional boundary conditions. The setting is fairly\ngeneral and covers the case of multi-point, integral and nonlinear boundary\nconditions. We also present a non-existence result. We provide some examples to\nillustrate the applicability our theoretical results.\n",
"title": "Nonzero positive solutions of a multi-parameter elliptic system with functional BCs"
}
| null | null | null | null | true | null |
10141
| null |
Default
| null | null |
null |
{
"abstract": " This paper proposes an approach to detect emotion from human speech employing\nmajority voting technique over several machine learning techniques. The\ncontribution of this work is in two folds: firstly it selects those features of\nspeech which is most promising for classification and secondly it uses the\nmajority voting technique that selects the exact class of emotion. Here,\nmajority voting technique has been applied over Neural Network (NN), Decision\nTree (DT), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). Input\nvector of NN, DT, SVM and KNN consists of various acoustic and prosodic\nfeatures like Pitch, Mel-Frequency Cepstral coefficients etc. From speech\nsignal many feature have been extracted and only promising features have been\nselected. To consider a feature as promising, Fast Correlation based feature\nselection (FCBF) and Fisher score algorithms have been used and only those\nfeatures are selected which are highly ranked by both of them. The proposed\napproach has been tested on Berlin dataset of emotional speech [3] and\nElectromagnetic Articulography (EMA) dataset [4]. The experimental result shows\nthat majority voting technique attains better accuracy over individual machine\nlearning techniques. The employment of the proposed approach can effectively\nrecognize the emotion of human beings in case of social robot, intelligent chat\nclient, call-center of a company etc.\n",
"title": "Emotion Recognition from Speech based on Relevant Feature and Majority Voting"
}
| null | null | null | null | true | null |
10142
| null |
Default
| null | null |
null |
{
"abstract": " In article the basic principles put in a basis of algorithmicallysoftware of\nhypercomplex number calculations, structure of a software, structure of\nfunctional subsystems are considered. The most important procedures included in\nsubsystems are considered, program listings and examples of their application\nare given.\n",
"title": "The basic principles and the structure and algorithmically software of computing by hypercomplex number"
}
| null | null | null | null | true | null |
10143
| null |
Default
| null | null |
null |
{
"abstract": " Cone spherical metrics are conformal metrics with constant curvature one and\nfinitely many conical singularities on compact Riemann surfaces. By using\nStrebel differentials as a bridge, we construct a new class of cone spherical\nmetrics on compact Riemann surfaces by drawing on the surfaces some class of\nconnected metric ribbon graphs.\n",
"title": "Drawing cone spherical metrics via Strebel differentials"
}
| null | null | null | null | true | null |
10144
| null |
Default
| null | null |
null |
{
"abstract": " We suggest a new type of an ultrasensitive detector of electromagnetic fields\nexploiting the giant thermoelectric effect recently found in\nsuperconductor/ferromagnet hybrid structures. Compared to other types of\nsuperconducting detectors where the detected signal is based on variations of\nthe detector impedance, the thermoelectric detector has the advantage of\nrequiring no external driving fields. This becomes especially relevant in\nmulti-pixel detectors where the number of bias lines and the heating induced by\nthem becomes an issue. We propose different material combinations to implement\nthe detector and provide a detailed analysis of its sensitivity and speed. In\nparticular, we perform to our knowledge the first proper noise analysis that\nincludes the cross correlation between heat and charge current noise and\nthereby describes also thermoelectric detectors with a large thermoelectric\nfigure of merit.\n",
"title": "Thermoelectric radiation detector based on superconductor/ferromagnet systems"
}
| null | null | null | null | true | null |
10145
| null |
Default
| null | null |
null |
{
"abstract": " New upper bounds on the pointwise behaviour of Christoffel function on convex\ndomains in ${\\mathbb{R}}^d$ are obtained. These estimates are established by\nexplicitly constructing the corresponding \"needle\"-like algebraic polynomials\nhaving small integral norm on the domain, and are stated in terms of few\neasy-to-measure geometric characteristics of the location of the point of\ninterest in the domain. Sharpness of the results is shown and examples of\napplications are given.\n",
"title": "Upper estimates of Christoffel function on convex domains"
}
| null | null | null | null | true | null |
10146
| null |
Default
| null | null |
null |
{
"abstract": " From string theory, the notion of deformed Hermitian Yang-Mills connections\nhas been introduced by Mariño, Minasian, Moore and Strominger. After that,\nLeung, Yau and Zaslow proved that it naturally appears as mirror objects of\nspecial Lagrangian submanifolds via Fourier-Mukai transform between dual torus\nfibrations. In their paper, some conditions are imposed for simplicity. In this\npaper, data to glue their construction on tropical manifolds are proposed and a\ngeneralization of the correspondence is proved without the assumption that the\nLagrangian submanifold is a section of the torus fibration.\n",
"title": "Special Lagrangian and deformed Hermitian Yang-Mills on tropical manifold"
}
| null | null | null | null | true | null |
10147
| null |
Default
| null | null |
null |
{
"abstract": " We present an introduction to periodic and stochastic homogenization of\nellip- tic partial differential equations. The first part is concerned with the\nqualitative theory, which we present for equations with periodic and random\ncoefficients in a unified approach based on Tartar's method of oscillating test\nfunctions. In partic- ular, we present a self-contained and elementary argument\nfor the construction of the sublinear corrector of stochastic homogenization.\n(The argument also applies to elliptic systems and in particular to linear\nelasticity). In the second part we briefly discuss the representation of the\nhomogenization error by means of a two- scale expansion. In the last part we\ndiscuss some results of quantitative stochastic homogenization in a discrete\nsetting. In particular, we discuss the quantification of ergodicity via\nconcentration inequalities, and we illustrate that the latter in combi- nation\nwith elliptic regularity theory leads to a quantification of the growth of the\nsublinear corrector and the homogenization error.\n",
"title": "An introduction to the qualitative and quantitative theory of homogenization"
}
| null | null | null | null | true | null |
10148
| null |
Default
| null | null |
null |
{
"abstract": " Yttria-stabilized zirconia (YSZ), a ZrO2-Y2O3 solid solution that contains a\nlarge population of oxygen vacancies, is widely used in energy and industrial\napplications. Past computational studies correctly predicted the anion\ndiffusivity but not the cation diffusivity, which is important for material\nprocessing and stability. One of the challenges lies in identifying a plausible\nconfiguration akin to the ground state in a glassy landscape. This is unlikely\nto come from random sampling of even a very large sample space, but the odds\nare much improved by incorporating packing preferences revealed by a modest\nsized configurational library established from empirical potential\ncalculations. Ab initio calculations corroborated these preferences, which\nprove remarkably robust extending to the fifth cation-oxygen shell about 8\n{\\AA} away. Yet because of frustration there are still rampant violations of\npacking preferences and charge neutrality in the ground state, and the approach\ntoward it bears a close analogy to glass relaxations. Fast relaxations proceed\nby fast oxygen movement around cations, while slow relaxations require slow\ncation diffusion. The latter is necessarily cooperative because of strong\ncoupling imposed by the long-range packing preferences.\n",
"title": "A Computational Study of Yttria-Stabilized Zirconia: I. Using Crystal Chemistry to Search for the Ground State on a Glassy Energy Landscape"
}
| null | null | null | null | true | null |
10149
| null |
Default
| null | null |
null |
{
"abstract": " Reactive power compensation is an important challenge in current and future\nsmart power systems. However, in the context of reactive power compensation,\nmost existing studies assume that customers can assess their compensation\nvalue, i.e., Var unit, objectively. In this paper, customers are assumed to\nmake decisions that pertain to reactive power coordination. In consequence, the\nway in which those customers evaluate the compensation value resulting from\ntheir individual decisions will impact the overall grid performance. In\nparticular, a behavioral framework, based on the framing effect of prospect\ntheory (PT), is developed to study the effect of both objective value and\nsubjective evaluation in a reactive power compensation game. For example, such\neffect allows customers to optimize a subjective value of their utility which\nessentially frames the objective utility with respect to a reference point.\nThis game enables customers to coordinate the use of their electrical devices\nto compensate reactive power. For the proposed game, both the objective case\nusing expected utility theory (EUT) and the PT consideration are solved via a\nlearning algorithm that converges to a mixed-strategy Nash equilibrium. In\naddition, several key properties of this game are derived analytically.\nSimulation results show that, under PT, customers are likely to make decisions\nthat differ from those predicted by classical models. For instance, using an\nillustrative two-customer case, we show that a PT customer will increase the\nconservative strategy (achieving a high power factor) by 29% compared to a\nconventional customer. Similar insights are also observed for a case with three\ncustomers.\n",
"title": "Reactive Power Compensation Game under Prospect-Theoretic Framing Effects"
}
| null | null | null | null | true | null |
10150
| null |
Default
| null | null |
null |
{
"abstract": " We investigate additional properties of protolocalizations, introduced and\nstudied by F. Borceux, M. M. Clementino, M. Gran, and L. Sousa, and of\nprotoadditive reflections, introduced and studied by T. Everaert and M. Gran.\nAmong other things we show that there are no non-trivial (protolocalizations\nand) protoadditive reflections of the category of groups, and establish a\nconnection between protolocalizations and Kurosh--Amitsur radicals of groups\nwith multiple operators whose semisimple classes form subvarieties.\n",
"title": "Some remarks on protolocalizations and protoadditive reflections"
}
| null | null | null | null | true | null |
10151
| null |
Default
| null | null |
null |
{
"abstract": " We consider the Dirichlet Laplacian in a straight three dimensional waveguide\nwith non-rotationally invariant cross section, perturbed by a twisting of small\namplitude. It is well known that such a perturbation does not create\neigenvalues below the essential spectrum. However, around the bottom of the\nspectrum, we provide a meromorphic extension of the weighted resolvent of the\nperturbed operator, and show the existence of exactly one resonance near this\npoint. Moreover, we obtain the asymptotic behavior of this resonance as the\nsize of the twisting goes to 0. We also extend the analysis to the upper\neigenvalues of the transversal problem, showing that the number of resonances\nis bounded by the multiplicity of the eigenvalue and obtaining the\ncorresponding asymptotic behavior\n",
"title": "Resonances near Thresholds in slightly Twisted Waveguides"
}
| null | null |
[
"Mathematics"
] | null | true | null |
10152
| null |
Validated
| null | null |
null |
{
"abstract": " A liquid film wetting the interior of a long circular cylinder redistributes\nunder the action of surface tension to form annular collars or occlusive plugs.\nThese equilibrium structures are invariant under axial translation within a\nperfectly smooth uniform tube and therefore can be displaced axially by very\nweak external forcing. We consider how this degeneracy is disrupted when the\ntube wall is rough, and determine threshold conditions under which collars or\nplugs resist displacement under forcing. Wall roughness is modelled as a\nnon-axisymmetric Gaussian random field of prescribed correlation length and\nsmall variance, mimicking some of the geometric irregularities inherent in\napplications such as lung airways. The thin film coating this surface is\nmodelled using lubrication theory. When the roughness is weak, we show how the\nlocations of equilibrium collars and plugs can be identified in terms of the\nazimuthally averaged tube radius; we derive conditions specifying equilibrium\ncollar locations under an externally imposed shear flow, and plug locations\nunder an imposed pressure gradient. We use these results to determine the\nprobability of external forcing being sufficient to displace a collar or plug\nfrom a rough-walled tube, when the tube roughness is defined only in\nstatistical terms.\n",
"title": "Trapping and displacement of liquid collars and plugs in rough-walled tubes"
}
| null | null | null | null | true | null |
10153
| null |
Default
| null | null |
null |
{
"abstract": " Existing brain network distances are often based on matrix norms. The\nelement-wise differences in the existing matrix norms may fail to capture\nunderlying topological differences. Further, matrix norms are sensitive to\noutliers. A major disadvantage to element-wise distance calculations is that it\ncould be severely affected even by a small number of extreme edge weights. Thus\nit is necessary to develop network distances that recognize topology. In this\npaper, we provide a survey of bottleneck, Gromov-Hausdorff (GH) and\nKolmogorov-Smirnov (KS) distances that are adapted for brain networks, and\ncompare them against matrix-norm based network distances. Bottleneck and\nGH-distances are often used in persistent homology. However, they were rarely\nutilized to measure similarity between brain networks. KS-distance is recently\nintroduced to measure the similarity between networks across different\nfiltration values. The performance analysis was conducted using the random\nnetwork simulations with the ground truths. Using a twin imaging study, which\nprovides biological ground truth, we demonstrate that the KS distance has the\nability to determine heritability.\n",
"title": "Topological Brain Network Distances"
}
| null | null |
[
"Quantitative Biology"
] | null | true | null |
10154
| null |
Validated
| null | null |
null |
{
"abstract": " Online trust systems are playing an important role in to-days world and face\nvarious challenges in building them. Billions of dollars of products and\nservices are traded through electronic commerce, files are shared among large\npeer-to-peer networks and smart contracts can potentially replace paper\ncontracts with digital contracts. These systems rely on trust mechanisms in\npeer-to-peer networks like reputation systems or a trustless public ledger. In\nmost cases, reputation systems are build to determine the trustworthiness of\nusers and to provide incentives for users to make a fair contribution to the\npeer-to-peer network. The main challenges are how to set up a good trust\nsystem, how to deal with security issues and how to deal with strategic users\ntrying to cheat on the system. The Sybil attack, the most important attack on\nreputation systems is discussed. At last match making in two sided markets and\nthe strategy proofness of these markets are discussed.\n",
"title": "The challenge of decentralized marketplaces"
}
| null | null |
[
"Computer Science"
] | null | true | null |
10155
| null |
Validated
| null | null |
null |
{
"abstract": " We study the collapse of pebble clouds with a statistical model to find the\ninternal structure of comet-sized planetesimals. Pebble-pebble collisions occur\nduring the collapse and the outcome of these collisions affect the resulting\nstructure of the planetesimal. We expand our previous models by allowing the\nindividual pebble sub-clouds to contract at different rates and by including\nthe effect of gas drag on the contraction speed and in energy dissipation. Our\nresults yield comets that are porous pebble-piles with particle sizes varying\nwith depth. In the surface layers there is a mixture of primordial pebbles and\npebble fragments. The interior, on the other hand, consists only of primordial\npebbles with a narrower size distribution, yielding higher porosity there. Our\nresults imply that the gas in the protoplanetary disc plays an important role\nin determining the radial distribution of pebble sizes and porosity inside\nplanetesimals.\n",
"title": "Radially resolved simulations of collapsing pebble clouds in protoplanetary discs"
}
| null | null | null | null | true | null |
10156
| null |
Default
| null | null |
null |
{
"abstract": " Accurate path integral Monte Carlo or molecular dynamics calculations of\nisotope effects have until recently been expensive because of the necessity to\nreduce three types of errors present in such calculations: statistical errors\ndue to sampling, path integral discretization errors, and thermodynamic\nintegration errors. While the statistical errors can be reduced with virial\nestimators and path integral discretization errors with high-order\nfactorization of the Boltzmann operator, here we propose a method for\naccelerating isotope effect calculations by eliminating the integration error.\nWe show that the integration error can be removed entirely by changing particle\nmasses stochastically during the calculation and by using a piecewise linear\numbrella biasing potential. Moreover, we demonstrate numerically that this\napproach does not increase the statistical error. The resulting acceleration of\nisotope effect calculations is demonstrated on a model harmonic system and on\ndeuterated species of methane.\n",
"title": "Accelerating equilibrium isotope effect calculations: I. Stochastic thermodynamic integration with respect to mass"
}
| null | null |
[
"Physics"
] | null | true | null |
10157
| null |
Validated
| null | null |
null |
{
"abstract": " The Constrained Application Protocol (CoAP) is a specialized Web transfer\nprotocol for resource-oriented applications intended to run on constrained\ndevices, typically part of the Internet of Things. In this paper we leverage\nInformation-Centric Networking (ICN), deployed within the domain of a network\nprovider that interconnects, in addition to other terminals, CoAP endpoints in\norder to provide enhanced CoAP services. We present various CoAP-specific\ncommunication scenarios and discuss how ICN can provide benefits to both\nnetwork providers and CoAP applications, even though the latter are not aware\nof the existence of ICN. In particular, the use of ICN results in smaller state\nmanagement complexity at CoAP endpoints, simpler implementation at CoAP\nendpoints, and less communication overhead in the network.\n",
"title": "CoAP over ICN"
}
| null | null | null | null | true | null |
10158
| null |
Default
| null | null |
null |
{
"abstract": " The physics of active systems of self-propelled particles, in the regime of a\ndense liquid state, is an open puzzle of great current interest, both for\nstatistical physics and because such systems appear in many biological\ncontexts. We develop a nonequilibrium mode-coupling theory (MCT) for such\nsystems, where activity is included as a colored noise with the particles\nhaving a self-propulsion foce $f_0$ and persistence time $\\tau_p$. Using the\nextended MCT and a generalized fluctuation-dissipation theorem, we calculate\nthe effective temperature $T_{eff}$ of the active fluid. The nonequilibrium\nnature of the systems is manifested through a time-dependent $T_{eff}$ that\napproaches a constant in the long-time limit, which depends on the activity\nparameters $f_0$ and $\\tau_p$. We find, phenomenologically, that this long-time\nlimit is captured by the potential energy of a single, trapped active particle\n(STAP). Through a scaling analysis close to the MCT glass transition point, we\nshow that $\\tau_\\alpha$, the $\\alpha$-relaxation time, behaves as\n$\\tau_\\alpha\\sim f_0^{-2\\gamma}$, where $\\gamma=1.74$ is the MCT exponent for\nthe passive system. $\\tau_\\alpha$ may increase or decrease as a function of\n$\\tau_p$ depending on the type of active force correlations, but the behavior\nis always governed by the same value of the exponent $\\gamma$. Comparison with\nnumerical solution of the nonequilibrium MCT as well as simulation results give\nexcellent agreement with the scaling analysis.\n",
"title": "Nonequilibrium mode-coupling theory for dense active systems of self-propelled particles"
}
| null | null |
[
"Physics"
] | null | true | null |
10159
| null |
Validated
| null | null |
null |
{
"abstract": " In this letter we present a theorem on the dynamics of the generalized\nHubbard models. This theorem shows that the symmetry of the single particle\nHamiltonian can protect a kind of dynamical symmetry driven by the\ninteractions. Here the dynamical symmetry refers to that the time evolution of\ncertain observables are symmetric between the repulsive and attractive Hubbard\nmodels. We demonstrate our theorem with three different examples in which the\nsymmetry involves bipartite lattice symmetry, reflection symmetry and\ntranslation symmetry, respectively. Each of these examples relates to one\nrecent cold atom experiment on the dynamics in the optical lattices where such\na dynamical symmetry is manifested. These experiments include expansion\ndynamics of cold atoms, chirality of atomic motion within a synthetic magnetic\nfield and melting of charge-density-wave order. Therefore, our theorem provides\na unified view of these seemingly disparate phenomena.\n",
"title": "Symmetry Protected Dynamical Symmetry in the Generalized Hubbard Models"
}
| null | null | null | null | true | null |
10160
| null |
Default
| null | null |
null |
{
"abstract": " Realistic evolutionary fitness landscapes are notoriously difficult to\nconstruct. A recent cutting-edge model of virus assembly consists of a\ndodecahedral capsid with $12$ corresponding packaging signals in three affinity\nbands. This whole genome/phenotype space consisting of $3^{12}$ genomes has\nbeen explored via computationally expensive stochastic assembly models, giving\na fitness landscape in terms of the assembly efficiency. Using latest\nmachine-learning techniques by establishing a neural network, we show that the\nintensive computation can be short-circuited in a matter of minutes to\nastounding accuracy.\n",
"title": "Machine-learning a virus assembly fitness landscape"
}
| null | null | null | null | true | null |
10161
| null |
Default
| null | null |
null |
{
"abstract": " Data-driven modeling plays an increasingly important role in different areas\nof engineering. For most of existing methods, such as genetic programming (GP),\nthe convergence speed might be too slow for large scale problems with a large\nnumber of variables. It has become the bottleneck of GP for practical\napplications. Fortunately, in many applications, the target models are\nseparable in some sense. In this paper, we analyze different types of\nseparability of some real-world engineering equations and establish a\nmathematical model of generalized separable system (GS system). In order to get\nthe structure of the GS system, a multilevel block building (MBB) algorithm is\nproposed, in which the target model is decomposed into a number of blocks,\nfurther into minimal blocks and factors. Compare to the conventional GP, MBB\ncan make large reductions to the search space. This makes MBB capable of\nmodeling a complex system. The minimal blocks and factors are optimized and\nassembled with a global optimization search engine, low dimensional simplex\nevolution (LDSE). An extensive study between the proposed MBB and a\nstate-of-the-art data-driven fitting tool, Eureqa, has been presented with\nseveral man-made problems, as well as some real-world problems. Test results\nindicate that the proposed method is more effective and efficient under all the\ninvestigated cases.\n",
"title": "A multilevel block building algorithm for fast modeling generalized separable systems"
}
| null | null |
[
"Mathematics"
] | null | true | null |
10162
| null |
Validated
| null | null |
null |
{
"abstract": " In many domains, a latent competition among different conventions determines\nwhich one will come to dominate. One sees such effects in the success of\ncommunity jargon, of competing frames in political rhetoric, or of terminology\nin technical contexts. These effects have become widespread in the online\ndomain, where the data offers the potential to study competition among\nconventions at a fine-grained level.\nIn analyzing the dynamics of conventions over time, however, even with\ndetailed on-line data, one encounters two significant challenges. First, as\nconventions evolve, the underlying substance of their meaning tends to change\nas well; and such substantive changes confound investigations of social\neffects. Second, the selection of a convention takes place through the complex\ninteractions of individuals within a community, and contention between the\nusers of competing conventions plays a key role in the convention's evolution.\nAny analysis must take place in the presence of these two issues.\nIn this work we study a setting in which we can cleanly track the competition\namong conventions. Our analysis is based on the spread of low-level authoring\nconventions in the eprint arXiv over 24 years: by tracking the spread of macros\nand other author-defined conventions, we are able to study conventions that\nvary even as the underlying meaning remains constant. We find that the\ninteraction among co-authors over time plays a crucial role in the selection of\nthem; the distinction between more and less experienced members of the\ncommunity, and the distinction between conventions with visible versus\ninvisible effects, are both central to the underlying processes. Through our\nanalysis we make predictions at the population level about the ultimate success\nof different synonymous conventions over time--and at the individual level\nabout the outcome of \"fights\" between people over convention choices.\n",
"title": "Competition and Selection Among Conventions"
}
| null | null | null | null | true | null |
10163
| null |
Default
| null | null |
null |
{
"abstract": " Analog-to-digital converters (ADCs) are a major contributor to the power\nconsumption of multiple-input multiple-output (MIMO) communication systems with\nlarge number of antennas. Use of low resolution ADCs has been proposed as a\nmeans to decrease power consumption in MIMO receivers. However, reducing the\nADC resolution leads to performance loss in terms of achievable transmission\nrates. In order to mitigate the rate-loss, the receiver can perform analog\nprocessing of the received signals before quantization. Prior works consider\none-shot analog processing where at each channel-use, analog linear\ncombinations of the received signals are fed to a set of one-bit threshold\nADCs. In this paper, a receiver architecture is proposed which uses a sequence\nof delay elements to allow for blockwise linear combining of the received\nanalog signals. In the high signal to noise ratio regime, it is shown that the\nproposed architecture achieves the maximum achievable transmission rate given a\nfixed number of one-bit ADCs. Furthermore, a tradeoff between transmission rate\nand the number of delay elements is identified which quantifies the increase in\nmaximum achievable rate as the number of delay elements is increased.\n",
"title": "Tradeoff Between Delay and High SNR Capacity in Quantized MIMO Systems"
}
| null | null | null | null | true | null |
10164
| null |
Default
| null | null |
null |
{
"abstract": " We design controllers from formal specifications for positive discrete-time\nmonotone systems that are subject to bounded disturbances. Such systems are\nwidely used to model the dynamics of transportation and biological networks.\nThe specifications are described using signal temporal logic (STL), which can\nexpress a broad range of temporal properties. We formulate the problem as a\nmixed-integer linear program (MILP) and show that under the assumptions made in\nthis paper, which are not restrictive for traffic applications, the existence\nof open-loop control policies is sufficient and almost necessary to ensure the\nsatisfaction of STL formulas. We establish a relation between satisfaction of\nSTL formulas in infinite time and set-invariance theories and provide an\nefficient method to compute robust control invariant sets in high dimensions.\nWe also develop a robust model predictive framework to plan controls optimally\nwhile ensuring the satisfaction of the specification. Illustrative examples and\na traffic management case study are included.\n",
"title": "Formal Synthesis of Control Strategies for Positive Monotone Systems"
}
| null | null | null | null | true | null |
10165
| null |
Default
| null | null |
null |
{
"abstract": " Progress in deep learning is slowed by the days or weeks it takes to train\nlarge models. The natural solution of using more hardware is limited by\ndiminishing returns, and leads to inefficient use of additional resources. In\nthis paper, we present a large batch, stochastic optimization algorithm that is\nboth faster than widely used algorithms for fixed amounts of computation, and\nalso scales up substantially better as more computational resources become\navailable. Our algorithm implicitly computes the inverse Hessian of each\nmini-batch to produce descent directions; we do so without either an explicit\napproximation to the Hessian or Hessian-vector products. We demonstrate the\neffectiveness of our algorithm by successfully training large ImageNet models\n(Inception-V3, Resnet-50, Resnet-101 and Inception-Resnet-V2) with mini-batch\nsizes of up to 32000 with no loss in validation error relative to current\nbaselines, and no increase in the total number of steps. At smaller mini-batch\nsizes, our optimizer improves the validation error in these models by 0.8-0.9%.\nAlternatively, we can trade off this accuracy to reduce the number of training\nsteps needed by roughly 10-30%. Our work is practical and easily usable by\nothers -- only one hyperparameter (learning rate) needs tuning, and\nfurthermore, the algorithm is as computationally cheap as the commonly used\nAdam optimizer.\n",
"title": "Neumann Optimizer: A Practical Optimization Algorithm for Deep Neural Networks"
}
| null | null |
[
"Computer Science",
"Statistics"
] | null | true | null |
10166
| null |
Validated
| null | null |
null |
{
"abstract": " Sensing and reciprocating cellular systems (SARs) are important for the\noperation of many biological systems. Production in interferon (IFN) SARs is\nachieved through activation of the Jak-Stat pathway, and downstream\nupregulation of IFN regulatory factor (IRF)-3 and IFN transcription, but the\nrole that high and low affinity IFNs play in this process remains unclear. We\npresent a comparative between a minimal spatio-temporal partial differential\nequation (PDE) model and a novel spatio-structural-temporal (SST) model for the\nconsideration of receptor, binding, and metabolic aspects of SAR behaviour.\nUsing the SST framework, we simulate single- and multi-cluster paradigms of IFN\ncommunication. Simulations reveal a cyclic process between the binding of IFN\nto the receptor, and the consequent increase in metabolism, decreasing the\npropensity for binding due to the internal feed-back mechanism. One observes\nthe effect of heterogeneity between cellular clusters, allowing them to\nindividualise and increase local production, and within clusters, where we\nobserve `sub popular quiescence'; a process whereby intra-cluster\nsubpopulations reduce their binding and metabolism such that other such\nsubpopulations may augment their production. Finally, we observe the ability\nfor low affinity IFN to communicate a long range signal, where high affinity\ncannot, and the breakdown of this relationship through the introduction of cell\nmotility. Biological systems may utilise cell motility where environments are\nunrestrictive and may use fixed system, with low affinity communication, where\na localised response is desirable.\n",
"title": "Signal propagation in sensing and reciprocating cellular systems with spatial and structural heterogeneity"
}
| null | null | null | null | true | null |
10167
| null |
Default
| null | null |
null |
{
"abstract": " A new three-parameter cumulative distribution function defined on\n$(\\alpha,\\infty)$, for some $\\alpha\\geq0$, with asymmetric probability density\nfunction and showing exponential decays at its both tails, is introduced. The\nnew distribution is near to familiar distributions like the gamma and\nlog-normal distributions, but this new one shows own elements and thus does not\ngeneralize neither of these distributions. Hence, the new distribution\nconstitutes a new alternative to fit values showing light-tailed behaviors.\nFurther, this new distribution shows great flexibility to fit the bulk of data\nby tuning some parameters. We refer to this new distribution as the generalized\nexponential log-squared distribution (GEL-S). Statistical properties of the\nGEL-S distribution are discussed. The maximum likelihood method is proposed for\nestimating the model parameters, but incorporating adaptations in computational\nprocedures due to difficulties in the manipulation of the parameters. The\nperfomance of the new distribution is studied using simulations. Applications\nto real data sets coming from different domains are showed.\n",
"title": "A New Family of Asymmetric Distributions for Modeling Light-Tailed and Right-Skewed Data"
}
| null | null |
[
"Mathematics",
"Statistics"
] | null | true | null |
10168
| null |
Validated
| null | null |
null |
{
"abstract": " This paper proposes a new scheme to secure the transmissions in an untrusted\ndecode-and-forward (DF) relaying network. A legitimate source node, Alice,\nsends her data to a legitimate destination node, Bob, with the aid of an\nuntrusted DF relay node, Charlie. To secure the transmissions from Charlie\nduring relaying time slots, each data codeword is secured using a secret-key\ncodeword that has been previously shared between Alice and Bob during the\nperfectly secured time slots (i.e., when the channel secrecy rate is positive).\nThe secret-key bits exchanged between Alice and Bob are stored in a\nfinite-length buffer and are used to secure data transmission whenever needed.\nWe model the secret-key buffer as a queueing system and analyze its Markov\nchain. Our numerical results show the gains of our proposed scheme relative to\nbenchmarks. Moreover, the proposed scheme achieves an upper bound on the secure\nthroughput.\n",
"title": "Secret-Key-Aided Scheme for Securing Untrusted DF Relaying Networks"
}
| null | null | null | null | true | null |
10169
| null |
Default
| null | null |
null |
{
"abstract": " We investigate deep neural network performance in the textindependent speaker\nrecognition task. We demonstrate that using angular softmax activation at the\nlast classification layer of a classification neural network instead of a\nsimple softmax activation allows to train a more generalized discriminative\nspeaker embedding extractor. Cosine similarity is an effective metric for\nspeaker verification in this embedding space. We also address the problem of\nchoosing an architecture for the extractor. We found that deep networks with\nresidual frame level connections outperform wide but relatively shallow\narchitectures. This paper also proposes several improvements for previous\nDNN-based extractor systems to increase the speaker recognition accuracy. We\nshow that the discriminatively trained similarity metric learning approach\noutperforms the standard LDA-PLDA method as an embedding backend. The results\nobtained on Speakers in the Wild and NIST SRE 2016 evaluation sets demonstrate\nrobustness of the proposed systems when dealing with close to real-life\nconditions.\n",
"title": "On deep speaker embeddings for text-independent speaker recognition"
}
| null | null | null | null | true | null |
10170
| null |
Default
| null | null |
null |
{
"abstract": " Bayesian model selection and model averaging rely on estimates of marginal\ndata densities (MDDs) also known as marginal likelihoods. Estimation of MDDs is\noften nontrivial and requires elaborate numerical integration methods. We\npropose using the variational Bayes posterior density as a weighting density\nwithin the class of reciprocal importance sampling MDD estimators. This\nproposal is computationally convenient, is based on variational Bayes posterior\ndensities that are available for many models, only requires simulated draws\nfrom the posterior distribution, and provides accurate estimates with a\nmoderate number of posterior draws. We show that this estimator is\ntheoretically well-justified, has finite variance, provides a minimum variance\ncandidate for the class of reciprocal importance sampling MDD estimators, and\nthat its reciprocal is consistent, asymptotically normally distributed and\nunbiased. We also investigate the performance of the variational Bayes\napproximate density as a weighting density within the class of bridge sampling\nestimators. Using several examples, we show that our proposed estimators are at\nleast as good as the best existing estimators and outperform many MDD\nestimators in terms of bias and numerical standard errors.\n",
"title": "Accurate Computation of Marginal Data Densities Using Variational Bayes"
}
| null | null | null | null | true | null |
10171
| null |
Default
| null | null |
null |
{
"abstract": " We analyze subway arrival times in the New York City subway system. We find\nregimes where the gaps between trains exhibit both (unitarily invariant) random\nmatrix statistics and Poisson statistics. The departure from random matrix\nstatistics is captured by the value of the Coulomb potential along the subway\nroute. This departure becomes more pronounced as trains make more stops.\n",
"title": "Random matrices and the New York City subway system"
}
| null | null |
[
"Physics"
] | null | true | null |
10172
| null |
Validated
| null | null |
null |
{
"abstract": " The superconducting transition of FeSe$_{1-x}$S$_x$ with three distinct\nsulphur concentrations $x$ was studied under hydrostatic pressure up to\n$\\sim$70 kbar via bulk AC susceptibility. The pressure dependence of the\nsuperconducting transition temperature ($T_c$) features a small dome-shaped\nvariation at low pressures for $x=0.04$ and $x=0.12$, followed by a more\nsubstantial $T_c$ enhancement to a value of around 30 K at moderate pressures.\nIn $x=0.21$, a similar overall pressure dependence of $T_c$ is observed, except\nthat the small dome at low pressures is flattened. For all three\nconcentrations, a significant weakening of the diamagnetic shielding is\nobserved beyond the pressure around which the maximum $T_c$ of 30 K is reached\nnear the verge of pressure-induced magnetic phase. This observation points to a\nstrong competition between the magnetic and high-$T_c$ superconducting states\nat high pressure in this system.\n",
"title": "Weakening of the diamagnetic shielding in FeSe$_{1-x}$S$_x$ at high pressures"
}
| null | null | null | null | true | null |
10173
| null |
Default
| null | null |
null |
{
"abstract": " This paper explores an incremental training strategy for the skip-gram model\nwith negative sampling (SGNS) from both empirical and theoretical perspectives.\nExisting methods of neural word embeddings, including SGNS, are multi-pass\nalgorithms and thus cannot perform incremental model update. To address this\nproblem, we present a simple incremental extension of SGNS and provide a\nthorough theoretical analysis to demonstrate its validity. Empirical\nexperiments demonstrated the correctness of the theoretical analysis as well as\nthe practical usefulness of the incremental algorithm.\n",
"title": "Incremental Skip-gram Model with Negative Sampling"
}
| null | null | null | null | true | null |
10174
| null |
Default
| null | null |
null |
{
"abstract": " Treating optimization methods as dynamical systems can be traced back\ncenturies ago in order to comprehend the notions and behaviors of optimization\nmethods. Lately, this mind set has become the driving force to design new\noptimization methods. Inspired by the recent dynamical system viewpoint of\nNesterov's fast method, we propose two classes of fast methods, formulated as\nhybrid control systems, to obtain pre-specified exponential convergence rate.\nAlternative to the existing fast methods which are parametric-in-time second\norder differential equations, we dynamically synthesize feedback controls in a\nstate-dependent manner. Namely, in the first class the damping term is viewed\nas the control input, while in the second class the amplitude with which the\ngradient of the objective function impacts the dynamics serves as the\ncontroller. The objective function requires to satisfy a certain sharpness\ncriterion, the so-called Polyak--{\\L}ojasiewicz inequality. Moreover, we\nestablish that both hybrid structures possess Zeno-free solution trajectories.\nWe finally provide a mechanism to determine the discretization step size to\nattain an exponential convergence rate.\n",
"title": "Continuous-Time Accelerated Methods via a Hybrid Control Lens"
}
| null | null | null | null | true | null |
10175
| null |
Default
| null | null |
null |
{
"abstract": " Processing sequential data of variable length is a major challenge in a wide\nrange of applications, such as speech recognition, language modeling,\ngenerative image modeling and machine translation. Here, we address this\nchallenge by proposing a novel recurrent neural network (RNN) architecture, the\nFast-Slow RNN (FS-RNN). The FS-RNN incorporates the strengths of both\nmultiscale RNNs and deep transition RNNs as it processes sequential data on\ndifferent timescales and learns complex transition functions from one time step\nto the next. We evaluate the FS-RNN on two character level language modeling\ndata sets, Penn Treebank and Hutter Prize Wikipedia, where we improve state of\nthe art results to $1.19$ and $1.25$ bits-per-character (BPC), respectively. In\naddition, an ensemble of two FS-RNNs achieves $1.20$ BPC on Hutter Prize\nWikipedia outperforming the best known compression algorithm with respect to\nthe BPC measure. We also present an empirical investigation of the learning and\nnetwork dynamics of the FS-RNN, which explains the improved performance\ncompared to other RNN architectures. Our approach is general as any kind of RNN\ncell is a possible building block for the FS-RNN architecture, and thus can be\nflexibly applied to different tasks.\n",
"title": "Fast-Slow Recurrent Neural Networks"
}
| null | null | null | null | true | null |
10176
| null |
Default
| null | null |
null |
{
"abstract": " OR multi-access channel is a simple model where the channel output is the\nBoolean OR among the Boolean channel inputs. We revisit this model, showing\nthat employing Bloom filter, a randomized data structure, as channel inputs\nachieves its capacity region with joint decoding and the symmetric sum rate of\n$\\ln 2$ bits per channel use without joint decoding. We then proceed to the\n\"many-access\" regime where the number of potential users grows without bound,\ntreating both activity recognition and message transmission problems,\nestablishing scaling laws which are optimal within a constant factor, based on\nBloom filter channel inputs.\n",
"title": "On OR Many-Access Channels"
}
| null | null | null | null | true | null |
10177
| null |
Default
| null | null |
null |
{
"abstract": " Social media platforms contain a great wealth of information which provides\nopportunities for us to explore hidden patterns or unknown correlations, and\nunderstand people's satisfaction with what they are discussing. As one\nshowcase, in this paper, we present a system, TwiInsight which explores the\ninsight of Twitter data. Different from other Twitter analysis systems,\nTwiInsight automatically extracts the popular topics under different categories\n(e.g., healthcare, food, technology, sports and transport) discussed in Twitter\nvia topic modeling and also identifies the correlated topics across different\ncategories. Additionally, it also discovers the people's opinions on the tweets\nand topics via the sentiment analysis. The system also employs an intuitive and\ninformative visualization to show the uncovered insight. Furthermore, we also\ndevelop and compare six most popular algorithms - three for sentiment analysis\nand three for topic modeling.\n",
"title": "TwiInsight: Discovering Topics and Sentiments from Social Media Datasets"
}
| null | null | null | null | true | null |
10178
| null |
Default
| null | null |
null |
{
"abstract": " Transparency, user trust, and human comprehension are popular ethical\nmotivations for interpretable machine learning. In support of these goals,\nresearchers evaluate model explanation performance using humans and real world\napplications. This alone presents a challenge in many areas of artificial\nintelligence. In this position paper, we propose a distinction between\ndescriptive and persuasive explanations. We discuss reasoning suggesting that\nfunctional interpretability may be correlated with cognitive function and user\npreferences. If this is indeed the case, evaluation and optimization using\nfunctional metrics could perpetuate implicit cognitive bias in explanations\nthat threaten transparency. Finally, we propose two potential research\ndirections to disambiguate cognitive function and explanation models, retaining\ncontrol over the tradeoff between accuracy and interpretability.\n",
"title": "The Promise and Peril of Human Evaluation for Model Interpretability"
}
| null | null | null | null | true | null |
10179
| null |
Default
| null | null |
null |
{
"abstract": " In this paper, we reconsider the unfolding-based technique that we have\nintroduced previously for detecting loops in standard term rewriting. We\nimprove it by guiding the unfolding process, using distinguished positions in\nthe rewrite rules. This results in a depth-first computation of the unfoldings,\nwhereas the original technique was breadth-first. We have implemented this new\napproach in our tool NTI and compared it to the previous one on a bunch of\nrewrite systems. The results we get are promising (better times, more\nsuccessful proofs).\n",
"title": "Guided Unfoldings for Finding Loops in Standard Term Rewriting"
}
| null | null | null | null | true | null |
10180
| null |
Default
| null | null |
null |
{
"abstract": " The present study investigates different strategies for the treatment of a\nmixture of digestate from an anaerobic digester diluted and secondary effluent\nfrom a high rate algal pond. To this aim, the performance of two\nphoto-sequencing batch reactors (PSBRs) operated at high nutrients loading\nrates and different solids retention times (SRTs) were compared with a\nsemi-continuous photobioreactor (SC). Performances were evaluated in terms of\nwastewater treatment, biomass composition and biopolymers accumulation during\n30 days of operation. PSBRs were operated at a hydraulic retention time (HRT)\nof 2 days and SRTs of 10 and 5 days (PSBR2-10 and PSBR2-5, respectively),\nwhereas the semi-continuous reactor was operated at a coupled HRT/SRT of 10\ndays (SC10-10). Results showed that PSBR2-5 achieved the highest removal rates\nin terms of TN (6.7 mg L-1 d-1), TP (0.31 mg L-1 d-1), TOC (29.32 mg L-1 d-1)\nand TIC (3.91 mg L-1 d-1). These results were in general 3-6 times higher than\nthe removal rates obtained in the SC10-10 (TN 29.74 mg L-1 d-1, TP 0.96 mg L-1\nd-1, TOC 29.32 mg L-1 d-1 and TIC 3.91 mg L-1 d-1). Furthermore, both PSBRs\nwere able to produce biomass up to 0.09 g L-1 d-1, more than twofold the\nbiomass produced by the semi-continuous reactor (0.04 g L-1 d-1), and achieved\na biomass settleability of 86-92%. This study also demonstrated that the\nmicrobial composition could be controlled by the nutrients loads, since the\nthree reactors were dominated by different species depending on the nutritional\nconditions. Concerning biopolymers accumulation, carbohydrates concentration\nachieved similar values in the three reactors (11%), whereas <0.5 % of\npolyhydrohybutyrates (PHB) was produced. These low values in biopolymers\nproduction could be related to the lack of microorganisms as cyanobacteria that\nare able to accumulate carbohydrates/PHB.\n",
"title": "Nutrients and biomass dynamics in photo-sequencing batch reactors treating wastewater with high nutrients loadings"
}
| null | null | null | null | true | null |
10181
| null |
Default
| null | null |
null |
{
"abstract": " Random Fourier features is one of the most popular techniques for scaling up\nkernel methods, such as kernel ridge regression. However, despite impressive\nempirical results, the statistical properties of random Fourier features are\nstill not well understood. In this paper we take steps toward filling this gap.\nSpecifically, we approach random Fourier features from a spectral matrix\napproximation point of view, give tight bounds on the number of Fourier\nfeatures required to achieve a spectral approximation, and show how spectral\nmatrix approximation bounds imply statistical guarantees for kernel ridge\nregression.\nQualitatively, our results are twofold: on the one hand, we show that random\nFourier feature approximation can provably speed up kernel ridge regression\nunder reasonable assumptions. At the same time, we show that the method is\nsuboptimal, and sampling from a modified distribution in Fourier space, given\nby the leverage function of the kernel, yields provably better performance. We\nstudy this optimal sampling distribution for the Gaussian kernel, achieving a\nnearly complete characterization for the case of low-dimensional bounded\ndatasets. Based on this characterization, we propose an efficient sampling\nscheme with guarantees superior to random Fourier features in this regime.\n",
"title": "Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees"
}
| null | null | null | null | true | null |
10182
| null |
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| null | null |
null |
{
"abstract": " The DArk Matter Particle Explorer (DAMPE) is one of the four satellites\nwithin Strategic Pioneer Research Program in Space Science of the Chinese\nAcademy of Science (CAS). DAMPE can detect electrons, photons in a wide energy\nrange (5 GeV to 10 TeV) and ions up to iron (100GeV to 100 TeV).\nSilicon-Tungsten Tracker (STK) is one of the four subdetectors in DAMPE,\nproviding photon-electron conversion, track reconstruction and charge\nidentification for ions. Ion beam test was carried out in CERN with 60GeV/u\nLead primary beams. Charge reconstruction and charge resolution of STK\ndetectors were investigated.\n",
"title": "Charge reconstruction study of the DAMPE Silicon-Tungsten Tracker with ion beams"
}
| null | null | null | null | true | null |
10183
| null |
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| null | null |
null |
{
"abstract": " The superconducting nanowire single photon detector (SNSPD) is a leading\ntechnology for quantum information science applications using photons, and they\nare finding increasing uses in photon-starved classical imaging applications.\nCritical detector characteristics, such as timing resolution (jitter), reset\ntime and maximum count rate, are heavily influenced by the readout electronics\nthat sense and amplify the photon detection signal. We describe a readout\ncircuit for SNSPDs using commercial off-the-shelf amplifiers operating at\ncryogenic temperatures. Our design demonstrates a 35 ps timing resolution and a\nmaximum count rate of over 2x10^7 counts per second while maintaining <3 mW\npower consumption per channel, making it suitable for a multichannel readout.\n",
"title": "Scalable Cryogenic Read-out Circuit for a Superconducting Nanowire Single-Photon Detector System"
}
| null | null | null | null | true | null |
10184
| null |
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| null | null |
null |
{
"abstract": " Just like Atiyah Lie algebroids encode the infinitesimal symmetries of\nprincipal bundles, exact Courant algebroids are believed to encode the\ninfinitesimal symmetries of $S^1$-gerbes. At the same time, transitive Courant\nalgebroids may be viewed as the higher analogue of Atiyah Lie algebroids, and\nthe non-commutative analogue of exact Courant algebroids. In this article, we\nexplore what the \"principal bundle\" behind transitive Courant algebroids are,\nand they turn out to be principal 2-bundles of string groups. First, we\nconstruct the stack of principal 2-bundles of string groups with connection\ndata. We prove a lifting theorem for the stack of string principal bundles with\nconnections and show the multiplicity of the lifts once they exist. This is a\ndifferential geometrical refinement of what is known for string structures by\nRedden, Waldorf and Stolz-Teichner. We also extend the result of Bressler and\nChen-Stiénon-Xu on extension obstruction involving transitive Courant\nalgebroids to the case of transitive Courant algebroids with connections, as a\nlifting theorem with the description of multiplicity once liftings exist. At\nthe end, we build a morphism between these two stacks. The morphism turns out\nto be neither injective nor surjective in general, which shows that the process\nof associating the \"higher Atiyah algebroid\" loses some information and at the\nsame time, only some special transitive Courant algebroids come from string\nbundles.\n",
"title": "String principal bundles and Courant algebroids"
}
| null | null | null | null | true | null |
10185
| null |
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| null | null |
null |
{
"abstract": " We demonstrate creation of electroforming-free TaOx memristive devices using\nfocused ion beam irradiations to locally define conductive filaments in TaOx\nfilms. Electrical characterization shows that these irradiations directly\ncreate fully functional memristors without the need for electroforming. Ion\nbeam forming of conductive filaments combined with state-of-the-art\nnano-patterning presents a CMOS compatible approach to wafer level fabrication\nof fully formed and operational memristors.\n",
"title": "Electroforming-Free TaOx Memristors using Focused Ion Beam Irradiations"
}
| null | null |
[
"Physics"
] | null | true | null |
10186
| null |
Validated
| null | null |
null |
{
"abstract": " The present paper considers testing an Erdos--Renyi random graph model\nagainst a stochastic block model in the asymptotic regime where the average\ndegree of the graph grows with the graph size n. Our primary interest lies in\nthose cases in which the signal-to-noise ratio is at a constant level. Focusing\non symmetric two block alternatives, we first derive joint central limit\ntheorems for linear spectral statistics of power functions for properly\nrescaled graph adjacency matrices under both the null and local alternative\nhypotheses. The powers in the linear spectral statistics are allowed to grow to\ninfinity together with the graph size. In addition, we show that linear\nspectral statistics of Chebyshev polynomials are closely connected to signed\ncycles of growing lengths that determine the asymptotic likelihood ratio test\nfor the hypothesis testing problem of interest. This enables us to construct a\nsequence of test statistics that achieves the exact optimal asymptotic power\nwithin $O(n^3 \\log n)$ time complexity in the contiguous regime when $n^2\np_{n,av}^3 \\to\\infty$ where $p_{n,av}$ is the average connection probability.\nWe further propose a class of adaptive tests that are computationally tractable\nand completely data-driven. They achieve nontrivial powers in the contiguous\nregime and consistency in the singular regime whenever $n p_{n,av} \\to\\infty$.\nThese tests remain powerful when the alternative becomes a more general\nstochastic block model with more than two blocks.\n",
"title": "Optimal hypothesis testing for stochastic block models with growing degrees"
}
| null | null | null | null | true | null |
10187
| null |
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| null | null |
null |
{
"abstract": " In this paper, we propose a new loss function called generalized end-to-end\n(GE2E) loss, which makes the training of speaker verification models more\nefficient than our previous tuple-based end-to-end (TE2E) loss function. Unlike\nTE2E, the GE2E loss function updates the network in a way that emphasizes\nexamples that are difficult to verify at each step of the training process.\nAdditionally, the GE2E loss does not require an initial stage of example\nselection. With these properties, our model with the new loss function\ndecreases speaker verification EER by more than 10%, while reducing the\ntraining time by 60% at the same time. We also introduce the MultiReader\ntechnique, which allows us to do domain adaptation - training a more accurate\nmodel that supports multiple keywords (i.e. \"OK Google\" and \"Hey Google\") as\nwell as multiple dialects.\n",
"title": "Generalized End-to-End Loss for Speaker Verification"
}
| null | null | null | null | true | null |
10188
| null |
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| null | null |
null |
{
"abstract": " The regularization approach for variable selection was well developed for a\ncompletely observed data set in the past two decades. In the presence of\nmissing values, this approach needs to be tailored to different missing data\nmechanisms. In this paper, we focus on a flexible and generally applicable\nmissing data mechanism, which contains both ignorable and nonignorable missing\ndata mechanism assumptions. We show how the regularization approach for\nvariable selection can be adapted to the situation under this missing data\nmechanism. The computational and theoretical properties for variable selection\nconsistency are established. The proposed method is further illustrated by\ncomprehensive simulation studies and real data analyses, for both low and high\ndimensional settings.\n",
"title": "Penalized pairwise pseudo likelihood for variable selection with nonignorable missing data"
}
| null | null | null | null | true | null |
10189
| null |
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| null | null |
null |
{
"abstract": " Learning interpretable features from complex multilayer networks is a\nchallenging and important problem. The need for such representations is\nparticularly evident in multilayer networks of the brain, where nodal\ncharacteristics may help model and differentiate regions of the brain according\nto individual, cognitive task, or disease. Motivated by this problem, we\nintroduce the multi-node2vec algorithm, an efficient and scalable feature\nengineering method that automatically learns continuous node feature\nrepresentations from multilayer networks. Multi-node2vec relies upon a\nsecond-order random walk sampling procedure that efficiently explores the\ninner- and intra- layer ties of the observed multilayer network is utilized to\nidentify multilayer neighborhoods. Maximum likelihood estimators of the nodal\nfeatures are identified through the use of the Skip-gram neural network model\non the collection of sampled neighborhoods. We investigate the conditions under\nwhich multi-node2vec is an approximation of a closed-form matrix factorization\nproblem. We demonstrate the efficacy of multi-node2vec on a multilayer\nfunctional brain network from resting state fMRI scans over a group of 74\nhealthy individuals. We find that multi-node2vec outperforms contemporary\nmethods on complex networks, and that multi-node2vec identifies nodal\ncharacteristics that closely associate with the functional organization of the\nbrain.\n",
"title": "Fast embedding of multilayer networks: An algorithm and application to group fMRI"
}
| null | null | null | null | true | null |
10190
| null |
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| null | null |
null |
{
"abstract": " Recent years have witnessed the growing demands for resolving numerous bug\nreports in software maintenance. Aiming to reduce the time testers/developers\ntake in perusing bug reports, the task of bug report summarization has\nattracted a lot of research efforts in the literature. However, no systematic\nanalysis has been conducted on attribute construction which heavily impacts the\nperformance of supervised algorithms for bug report summarization. In this\nstudy, we first conduct a survey to reveal the existing methods for attribute\nconstruction in mining software repositories. Then, we propose a new method\nnamed Crowd-Attribute to infer new effective attributes from the crowdgenerated\ndata in crowdsourcing and develop a new tool named Crowdsourcing Software\nEngineering Platform to facilitate this method. With Crowd-Attribute, we\nsuccessfully construct 11 new attributes and propose a new supervised algorithm\nnamed Logistic Regression with Crowdsourced Attributes (LRCA). To evaluate the\neffectiveness of LRCA, we build a series of large scale data sets with 105,177\nbug reports. Experiments over both the public data set SDS with 36 manually\nannotated bug reports and new large-scale data sets demonstrate that LRCA can\nconsistently outperform the state-of-the-art algorithms for bug report\nsummarization.\n",
"title": "Towards Better Summarizing Bug Reports with Crowdsourcing Elicited Attributes"
}
| null | null | null | null | true | null |
10191
| null |
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| null | null |
null |
{
"abstract": " Graphene-based photodetectors have demonstrated mechanical flexibility, large\noperating bandwidth, and broadband spectral response. However, their linear\ndynamic range (LDR) is limited by graphene's intrinsichot-carrier dynamics,\nwhich causes deviation from a linear photoresponse at low incident powers. At\nthe same time, multiplication of hot carriers causes the photoactive region to\nbe smeared over distances of a few micro-meters, limiting the use of graphene\nin high-resolution applications. We present a novel method for engineer-ing\nphotoactive junctions in FeCl3-intercalated graphene using laser irradiation.\nPhotocurrent measured at these planar junctions shows an extraordinary linear\nresponse with an LDR value at least 4500 times larger than that of other\ngraphene devices (44 dB) while maintaining high stability against environmental\ncontamination without the need for encapsulation. The observed photoresponse is\npurely photovoltaic, demonstrating complete quenching of hot-carrier effects.\nThese results pave the way toward the design of ultrathin photode-tectors with\nunprecedented LDR for high-definition imaging and sensing.\n",
"title": "Extraordinary linear dynamic range in laser-defined functionalized graphene photodetectors"
}
| null | null | null | null | true | null |
10192
| null |
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| null | null |
null |
{
"abstract": " In this paper, we propose an efficient transfer leaning methods for training\na personalized language model using a recurrent neural network with long\nshort-term memory architecture. With our proposed fast transfer learning\nschemes, a general language model is updated to a personalized language model\nwith a small amount of user data and a limited computing resource. These\nmethods are especially useful for a mobile device environment while the data is\nprevented from transferring out of the device for privacy purposes. Through\nexperiments on dialogue data in a drama, it is verified that our transfer\nlearning methods have successfully generated the personalized language model,\nwhose output is more similar to the personal language style in both qualitative\nand quantitative aspects.\n",
"title": "Efficient Transfer Learning Schemes for Personalized Language Modeling using Recurrent Neural Network"
}
| null | null | null | null | true | null |
10193
| null |
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| null | null |
null |
{
"abstract": " We consider the nonlinear Schrödinger equation $-\\Delta u+(\\lambda\na(x)+1)u=|u|^{p-1}u$ on a locally finite graph $G=(V,E)$. We prove via the\nNehari method that if $a(x)$ satisfies certain assumptions, for any\n$\\lambda>1$, the equation admits a ground state solution $u_\\lambda$. Moreover,\nas $\\lambda\\rightarrow \\infty$, the solution $u_\\lambda$ converges to a\nsolution of the Dirichlet problem $-\\Delta u+u=|u|^{p-1}u$ which is defined on\nthe potential well $\\Omega$. We also provide a numerical experiment which\nsolves the equation on a finite graph to illustrate our results.\n",
"title": "Convergence of ground state solutions for nonlinear Schrödinger equations on graphs"
}
| null | null | null | null | true | null |
10194
| null |
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| null | null |
null |
{
"abstract": " This paper is concerned with the convergence and long-term stability analysis\nof the feedback particle filter (FPF) algorithm. The FPF is an interacting\nsystem of $N$ particles where the interaction is designed such that the\nempirical distribution of the particles approximates the posterior\ndistribution. It is known that in the mean-field limit ($N=\\infty$), the\ndistribution of the particles is equal to the posterior distribution. However\nlittle is known about the convergence to the mean-field limit. In this paper,\nwe consider the FPF algorithm for the linear Gaussian setting. In this setting,\nthe algorithm is similar to the ensemble Kalman-Bucy filter algorithm. Although\nthese algorithms have been numerically evaluated and widely used in\napplications, their convergence and long-term stability analysis remains an\nactive area of research. In this paper, we show that, (i) the mean-field limit\nis well-defined with a unique strong solution; (ii) the mean-field process is\nstable with respect to the initial condition; (iii) we provide conditions such\nthat the finite-$N$ system is long term stable and we obtain some mean-squared\nerror estimates that are uniform in time.\n",
"title": "Error Analysis of the Stochastic Linear Feedback Particle Filter"
}
| null | null | null | null | true | null |
10195
| null |
Default
| null | null |
null |
{
"abstract": " There has been a recent media blitz on a cohort of mathematicians valiantly\nworking to fix America's democratic system by combatting gerrymandering with\ngeometry. While statistics commonly features in the courtroom (forensics, DNA\nanalysis, etc.), the gerrymandering news raises a natural question: in what\nother ways has pure math, specifically geometry and topology, been involved in\ncourt cases and legal scholarship? In this survey article, we collect a few\nexamples with topics ranging from the Pythagorean formula to the Ham Sandwich\nTheorem, and we discuss some jurists' perspectives on geometric reasoning in\nthe legal realm. One of our goals is to provide math educators with engaging\nreal-world instances of some abstract geometric concepts.\n",
"title": "Geometry in the Courtroom"
}
| null | null | null | null | true | null |
10196
| null |
Default
| null | null |
null |
{
"abstract": " Temporary earth retaining structures (TERS) help prevent collapse during\nconstruction excavation. To ensure that these structures are operating within\ndesign specifications, load forces on supports must be monitored. Current\nmonitoring approaches are expensive, sparse, off-line, and thus difficult to\nintegrate into predictive models. This work aims to show that wirelessly\nconnected battery powered sensors are feasible, practical, and have similar\naccuracy to existing sensor systems. We present the design and validation of\nReStructure, an end-to-end prototype wireless sensor network for collection,\ncommunication, and aggregation of strain data. ReStructure was validated\nthrough a six months deployment on a real-life excavation site with all but one\nnode producing valid and accurate strain measurements at higher frequency than\nexisting ones. These results and the lessons learnt provide the basis for\nfuture widespread wireless TERS monitoring that increase measurement density\nand integrate closely with predictive models to provide timely alerts of damage\nor potential failure.\n",
"title": "Proof of Concept of Wireless TERS Monitoring"
}
| null | null | null | null | true | null |
10197
| null |
Default
| null | null |
null |
{
"abstract": " A subset $\\mathcal{G}$ generating a $C^*$-algebra $A$ is said to be\nhyperrigid if for every faithful nondegenerate $*$-representation $A\\subseteq\nB(H)$ and a sequence $\\phi_n:B(H) \\to B(H)$ of unital completely positive maps,\nwe have that \\[ \\lim_{n\\to\\infty}\\phi_n(g)= g~~\\text{for all } g\\in \\mathcal{G}\n~~ \\implies ~~ \\lim_{n\\to\\infty}\\phi_n(a)= a~~\\text{for all } a\\in A \\] where\nall convergence are in norm. In this paper, we show that for the Cuntz-Krieger\nalgebra $\\mathcal{O}(G)$ associated to a row-finite directed graph $G$ with no\nisolated vertices, the set of partial isometries $\\mathcal{E}=\\{S_e:e\\in E\\}$\nis hyperrigid.\nIn addition, we define and examine a closely related notion: the property of\nrigidity at $0$. A generating subset $\\mathcal{G}$ of a $C^*$-algebra $A$ is\nsaid to be rigid at $0$ if for every sequence of contractive positive maps\n$\\varphi_n:A\\to \\mathbb C$ satisfying $\\lim_{n\\to \\infty}\\varphi_n(g)=0$ for\nevery $g\\in \\mathcal{G}$, we have that $\\lim_{n\\to \\infty}\\varphi_n(a)=0$ for\nevery $a\\in A$.\nWe show that, when combined, hyperrigidity and rigidity at $0$ are equivalent\nto a somewhat stronger notion of hyperrigidity, and we connect this to the\nunique extension property. This, however, is not the case for the generating\nset $\\mathcal{E}$. More precisely, we show that for any graph $G$, subsets of\nthe Cuntz-Krieger family generating $\\mathcal{O}(G)$ are rigid at $0$ if and\nonly if they contain every vertex projection.\n",
"title": "Hyperrigid subsets of Cuntz-Krieger algebras and the property of rigidity at zero"
}
| null | null | null | null | true | null |
10198
| null |
Default
| null | null |
null |
{
"abstract": " We study the fully gapped chiral Mott insulator (CMI) of frustrated\nBose-Hubbard models on ladders and two-dimensional lattices by perturbative\nstrong-coupling analysis and density matrix renormalization group (DMRG). First\nwe show the existence of a low-lying exciton state on all geometries carrying\nthe correct quantum numbers responsible for the condensation of excitons and\nformation of the CMI in the intermediate interaction regime. Then we perform\nsystematic DMRG simulations on several two-leg ladder systems with $\\pi$-flux\nand carefully characterize the two quantum phase transitions. We discuss the\npossibility to extend the generally very small CMI window by including\nrepulsive nearest-neighbour interactions or changing density and coupling\nratios.\n",
"title": "Chiral Mott insulators in frustrated Bose-Hubbard models on ladders and two-dimensional lattices: a combined perturbative and density matrix renormalization group study"
}
| null | null | null | null | true | null |
10199
| null |
Default
| null | null |
null |
{
"abstract": " We establish a functional weak law of large numbers for observable\nmacroscopic state variables of interacting particle systems (e.g., voter and\ncontact processes) over fast time-varying sparse random networks of\ninteractions. We show that, as the number of agents $N$ grows large, the\nproportion of agents $\\left(\\overline{Y}_{k}^{N}(t)\\right)$ at a certain state\n$k$ converges in distribution -- or, more precisely, weakly with respect to the\nuniform topology on the space of \\emph{càdlàg} sample paths -- to the\nsolution of an ordinary differential equation over any compact interval\n$\\left[0,T\\right]$. Although the limiting process is Markov, the prelimit\nprocesses, i.e., the normalized macrostate vector processes\n$\\left(\\mathbf{\\overline{Y}}^{N}(t)\\right)=\\left(\\overline{Y}_{1}^{N}(t),\\ldots,\\overline{Y}_{K}^{N}(t)\\right)$,\nare non-Markov as they are tied to the \\emph{high-dimensional} microscopic\nstate of the system, which precludes the direct application of standard\narguments for establishing weak convergence. The techniques developed in the\npaper for establishing weak convergence might be of independent interest.\n",
"title": "Thermodynamic Limit of Interacting Particle Systems over Time-varying Sparse Random Networks"
}
| null | null | null | null | true | null |
10200
| null |
Default
| null | null |
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