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6c70b0b3fd558a29c96d36641f722e930c986342 | subsection | 26 | 39 | Adaptive Algorithm for Theorem | Moreover, \mathcal {L} = 0 \!\Rightarrow \! \widetilde{r}_m = 1 and so the reference indicators introduced here for the general case devolve into those of the previous subsection for f independent of u. Therefore, we conclude that the two adaptive algorithms are the same for this type of data.
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38b5b9530b25f4f15916bcd3c5befca713693fe3 | subsection | 27 | 39 | Numerical Experiments | We consider an implementation of the adaptive algorithm of the previous section through an application of the deal.II finite element library , . In order to facilitate a comparison between the L^\infty L^\infty estimator of Theorem REF and the L^2H^1 estimator of , we consider Example 1 and Example 3 of but under the a... | {
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e635233936efd09d6a987beee3dcb37540280b62 | subsection | 28 | 39 | Example 1 | Let \Omega = (-8,8)^2, a = 1, f(u) = u^2 and choose the initial condition to be the Gaussian blob given by u_0(x,y) = 10\exp (-2x^2-2y^2). The blow-up set for this example consists of only a single point (the origin) making it spatially uncomplicated which allows us to focus solely on the temporal asymptotics. Now, sin... | {
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8cdd51c00b2464eff24504ca2329ee3daa16c595 | subsection | 29 | 39 | Example 1 | Using this approximation to T_{\infty }, we take the data from Table REF and plot the distance from the blow-up time |T-T_{\infty }| versus the total number of time steps N in Figure REF . The plot shows that for this example we have\begin{aligned}|T_{\infty } - T({\tt ttol^+}, N)| \propto N^{-3 \slash 4}.
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c75e8e94eebad05201f0df9e44bcfc8d56f63533 | subsection | 30 | 39 | Example 1 | \end{split}Therefore, we have \mathcal {L}(|v_1|,|v_2|) = |v_1| + |v_2| in (REF ) and so \delta _m (if it exists) is the smallest root of the function \varphi _m:[1,\infty ) \rightarrow \mathbb {R} given by\begin{split}
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97b4c38e6e5ae7e8fc5ddaf7db48951bb999ff81 | subsection | 31 | 39 | Example 1 | From the results, given in Figure REF , we deduce the asymptotic estimate\begin{aligned}||U(t)|| \propto |t - T_{\infty }|^{-1},
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644c336fb5f7c04f1add1782ef59cddcfa22a80b | subsection | 32 | 39 | Example 2 | Let \Omega = (-8,8)^2, a = 1, f(u) = u^2 and the “volcano” type initial condition be given by u_0(x,y) = 10(x^2+y^2)\exp (-0.5x^2-0.5y^2). The blow-up set for this example is a circle centered on the origin making this example a good test of the spatial capabilities of the adaptive algorithm as many degrees of freedom ... | {
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48e58d4fd31af7b68b6b3e518db9fed79991fb65 | subsection | 33 | 39 | Example 2 | For visualization purposes, we also display profile views of the finite element solution at times t=0 and t=T in Figure REF .Let \Omega = (-8,8)^2, a = 1, f(u) = u^2 and the “volcano” type initial condition be given by u_0(x,y) = 10(x^2+y^2)\exp (-0.5x^2-0.5y^2). The blow-up set for this example is a circle centered on... | {
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0c19f6bad025b678d44b1dcc6b0447bd7c6bf4a0 | subsection | 34 | 39 | Example 3 | In this example, we consider a nonlinear parabolic problem from , . We set \Omega = (0,1)^2, T = 0.75, a = 0.001, f(t,u) = \sin (t) - u^4 and u_0(x,y)=xy(x-1)(y-1). The solution is initially unremarkable but as time evolves it begins to exhibit boundary layers through the influence of the diffusion and the forcing term... | {
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dc14d65fb5fbc55fd7361eda28f9180a2dc246f3 | subsection | 35 | 39 | Example 3 | In order to quantify the rate of convergence of the estimator in space, we first choose a small temporal refinement tolerance {\tt ttol}^+ so that the size of the temporal contribution to the estimator is negligible; we then gradually decrease the spatial refinement tolerance {\tt stol}^+ for polynomials of degree thre... | {
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d609021445dfe89d2c8e544a529b4d75ddd58977 | subsection | 36 | 39 | Example 3 | To do this, we choose a large polynomial degree and a small spatial refinement tolerance {\tt stol}^+ so that the spatial contribution to the estimator is negligible; we then gradually reduce the temporal refinement tolerance {\tt ttol}^+ in order to observe the rate of convergence of the estimator in time. Next, we pl... | {
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30c761e64f6852a61ba25d926871f01844ddd532 | subsection | 37 | 39 | Conclusions | We derived a conditional L^{\infty }L^{\infty } a posteriori error bound (Theorem REF ) for the IMEX discretization (REF ) of the semilinear heat equation (REF ) with general local Lipschitz nonlinearity (REF ). Our numerical experiments indicate that the proposed estimator outperforms the L^2 H^1 estimator of with res... | {
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f4ef0ea181d152a83b91b88a21954b63cf8fc79a | subsection | 38 | 39 | Conclusions | The slow convergence in Example 1 can be explained by the initial condition not having a “compatible profile” with the blow-up, that is, this choice of initial condition causes the solution to be significantly influenced by the laplacian early on (this can be seen in Figure REF by noting that the numerical solution onl... | {
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5637b9a4d690e173d269a49a3a9a6ff34bcf7e69 | subsection | 1 | 30 | Introduction | With rapidly growing data volumes and variety, the need to scale up machine learning has sparked broad interests in developing efficient parallel optimization algorithms.
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69160c56716745f9e3f5275d2619312657b7b373 | subsection | 2 | 30 | Introduction | An asynchronous parallel proximal gradient method has been presented in and has been shown to converge to stationary points for nonconvex problems. However, has essentially proposed a non-stochastic algorithm and has not provided its convergence rate.In this paper, we propose and analyze an asynchronous parallel proxim... | {
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9e9d7f583fc4f98bd7c1d317d8a6fa0b626b48ed | subsection | 3 | 30 | Preliminaries | In this paper, we use f(x) as the one defined in (REF ), and F(x;\xi ) as a function whose stochastic nature comes from the random variable \xi representing a random index selected from the training set \lbrace 1,\ldots , n\rbrace . We use \Vert {x} \Vert to denote the \ell _2 norm of the vector x, and \langle {x, y} \... | {
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22a4ee6aeac5883d64852278418ebd71242ca188 | subsection | 4 | 30 | Stochastic Optimization Problems | In this paper, we consider the following stochastic optimization problem instead of the original deterministic version (REF ):\begin{split}
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fb9969df762fc5b50c5e4502c42d2a85a45dabbd | subsection | 5 | 30 | Proximal Gradient Descent | The proximal operator is fundamental to many algorithms to solve problem (REF ) as well as its stochastic variant (REF ).Definition 1 (Proximal operator)
The proximal operator \mathbf {prox}^{}_{} of a point x \in \mathbb {R}^{{d}} under a proper and closed function h with parameter \eta > 0 is defined as:\mathbf {pro... | {
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34a8e64c9b9a58e937eef70df167dacbe5f74d6a | subsection | 6 | 30 | Parallel Stochastic Optimization | Recent years have witnessed rapid development of parallel and distributed computation frameworks for large-scale machine learning problems. One popular architecture is called parameter server , , which consists of some worker nodes and server nodes. In this architecture, one or multiple master machines play the role of... | {
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512dae4ebcc0e128ad16ece99d0fdf350ac4bc8d | subsection | 7 | 30 | Asynchronous Proximal Gradient Descent | We now present our asynchronous proximal gradient descent (Asyn-ProxSGD) algorithm, which is the main contribution in this paper. In the asynchronous algorithm, different workers may be in different local iterations due to random delays in computation and communication.For ease of presentation, let us first assume each... | {
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19cf53c178bc3f57f8151b87f0245494c29475ad | subsection | 8 | 30 | Convergence Analysis | To facilitate the analysis of Algorithm REF , we rewrite it in an equivalent global view (from the server's perspective), as described in Algorithm . In this algorithm, we use an iteration counter k to keep track of how many times the model x has been updated on the server; k increments every time a push request (model... | {
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6c43712e9292f327498d55f61768393a7c2ece49 | subsection | 9 | 30 | Assumptions and Metrics | We make the following assumptions for convergence analysis. We assume that f(\cdot ) is a smooth function with the following properties:Assumption 1 (Lipschitz Gradient)
For function f there are Lipschitz constants L>0 such that\Vert {\nabla f(x) - \nabla f(y)} \Vert \le L \Vert {x - y} \Vert , \forall x, y \in \mathb... | {
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c3b7d93bba80ca6c994c0c5483a6a309ee507542 | subsection | 10 | 30 | Theoretical Results | We present our main convergence theorem as follows:Theorem 1 If Assumptions REF and REF hold and the step length sequence \lbrace \eta _k\rbrace in Algorithm satisfies\eta _k \le \frac{1}{16L},\quad 6\eta _k L^2 T \sum _{l=1}^T \eta _{k+l} \le 1,for all k=1,2,\ldots , K, we have the following ergodic convergence rate ... | {
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ac12b6e57a151fc5398acf1878eba48dec2b091d | subsection | 11 | 30 | Theoretical Results | The reason is that by (REF ) and (REF ), as long as T is no more than O(K^{1/4}), the iteration complexity (from a global perspective) to achieve \epsilon -optimality is O(1/\epsilon ^2), which is independent from T.Remark 3 (Linear speedup w.r.t. number of workers)
As the iteration complexity is O(1/\epsilon ^2) to a... | {
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6e3928b4eb3e7d5938121b7aede1694ae41e08da | subsection | 12 | 30 | Experiments | We now present experimental results to confirm the capability and efficiency of our proposed algorithm to solve challenging non-convex non-smooth machine learning problems. We implemented our algorithm on TensorFlow , a flexible and efficient deep learning library. We execute our algorithm on Ray , a general-purpose fr... | {
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f6ca96f84d3c6435be26b43c4330145da37e282b | subsection | 13 | 30 | Experiments | For all experiments, we evaluate function suboptimality, which is
the gap f(x) - f(\hat{x}), against the number of sample gradients processed by the server (divided by the total number of samples n), and then against time.
[Figure: Performance of ProxGD and Async-ProxSGD on a9a (left) and mnist (right) datasets. Here t... | {
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e17182096ffed9b43c0949751e47ec0c064a2a93 | subsection | 14 | 30 | Related Work | Stochastic optimization problems have been studied since the seminal work in 1951 , in which a classical stochastic approximation algorithm is proposed for solving a class of strongly convex problems. Since then, a series of studies on stochastic programming have focused on convex problems using SGD , , . The convergen... | {
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98ea5604e354bba5e2063e37ddd632f2725a5160 | subsection | 15 | 30 | Concluding Remarks | In this paper, we study asynchronous parallel implementations of stochastic proximal gradient methods for solving nonconvex optimization problems, with convex yet possibly nonsmooth regularization.
However, compared to asynchronous parallel stochastic gradient descent (Asyn-SGD), which is targeting smooth optimization,... | {
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75d4480eedcebc48a041673b1a6161843f96dcab | subsection | 16 | 30 | Auxiliary Lemmas | Lemma 1 ()
For all y \leftarrow \mathbf {prox}^{}_{\eta h}(x - \eta g), we have:\langle {g, y-x} \rangle + (h(y) - h(x)) \le -\frac{\Vert {y-x} \Vert _2^2}{\eta }.Due to slightly different notations and definitions in , we provide a proof here for completeness. We refer readers to for more details.
By the definition ... | {
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7f7f72b99027c5a099d99ee9513b8be5feb7df63 | subsection | 17 | 30 | Auxiliary Lemmas | Let z substitute u in the first inequality and y in the second one, we have\langle {G + \frac{y-x}{\eta } + p, z-y} \rangle &\ge 0, \\
\langle {g + \frac{z-x}{\eta } + q, y-z} \rangle &\ge 0.Then, we have\langle {G, z-y} \rangle &\ge \langle {\frac{y-x}{\eta }, y-z} \rangle + \langle {p, y-z} \rangle , \\
&= \frac{1}{\... | {
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ee52efcf6515b5dbfec35174ba45bf5a94f4c754 | subsection | 18 | 30 | Auxiliary Lemmas | We denote \tilde{G}_k as the average of delayed stochastic gradients and \tilde{g}_k as the average of delayed true gradients, respectively:\tilde{G}_k &:= \frac{1}{N}\sum _{i=1}^N \nabla F(x_{t(k,i)}; \xi _{t(k,i), i}) \\
\tilde{g}_k &:= \frac{1}{N}\sum _{i=1}^N \nabla f(x_{t(k,i)}).Moreover, we denote \delta _k := \t... | {
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762a70e61afe229b268bfbf45b8a27dce0b04bd9 | subsection | 19 | 30 | Milestone lemmas | We put some key results of convergence analysis as milestone lemmas listed below, and the detailed proof is listed in REF .Lemma 5 (Decent Lemma)\mathbb {E}[\Psi (x_{k+1}) \le \mathbb {E}[\Psi (x_k)|\mathcal {F}_k] - \frac{\eta _k - 4L\eta _k^2}{2} \Vert {P(x_k, \tilde{g}_k, \eta _k)} \Vert ^2
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1e14f7509e92aea948c3bbdb4dd4545495b4c0b6 | subsection | 20 | 30 | Proof of Theorem | From the fact 2\Vert {a} \Vert ^2 + 2\Vert {b} \Vert ^2 \ge \Vert {a+b} \Vert ^2, we have\Vert {P(x_k, \tilde{g}_k, \eta _k)} \Vert ^2 + \Vert {g_k - \tilde{g}_k} \Vert ^2
&\ge \Vert {P(x_k, \tilde{g}_k, \eta _k)} \Vert ^2 + \Vert {P(x_k, g_k, \eta _k) - P(x_k, \tilde{g}_k, \eta _k)} \Vert ^2 \\
&\ge \frac{1}{2} \Vert ... | {
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Optimization | [
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62167ac07a3092847d862538ea5d5f7597b189e3 | subsection | 21 | 30 | Proof of Theorem | According to our condition of \eta \le \frac{1}{16L}, we have 8L\eta _k^2 - \eta < 0 and therefore\begin{split}
&\quad \ \mathbb {E}[\Psi (x_{k+1})|\mathcal {F}_k] \\
&\le \mathbb {E}[\Psi (x_k)|\mathcal {F}_k] + \frac{\eta _k}{2}\Vert {g_k-\tilde{g}_k} \Vert ^2
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68159042781862e91ad64086a87d8c9a2a1d21d2 | subsection | 22 | 30 | Proof of Theorem | \end{split}Apply Lemma REF we have\begin{split}
&\quad \ \mathbb {E}[\Psi (x_{k+1})|\mathcal {F}_k] \\
&\le \mathbb {E}[\Psi (x_k)|\mathcal {F}_k] - \frac{\eta _k-8L\eta _k^2}{8} \Vert {P(x_k, g_k, \eta _k)} \Vert ^2
+ \frac{L\eta _k^2}{N}\sigma ^2 - \frac{\eta _k}{4}\Vert {P(x_k, \tilde{g}_k, \eta _k)} \Vert ^2 \\
&\q... | {
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-0.004698515869677067,
-0.02931995876133442,
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0.009923325851559639,
0.04347652569413185,
0.02855721302330494,
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0.0075931367464363575,
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0.04387315362691879,
-0.03706945851445198,
0.... | |
fb3a04fa60f5ef5bc42aa225e65cf2ec04534125 | subsection | 23 | 30 | Proof of Theorem | \end{split}By taking telescope sum, we have&\quad \ \mathbb {E}[\Psi (x_{K+1})|\mathcal {F}_K] \\
&\le \Psi (x_1)
- \sum _{k=1}^K \frac{\eta _k-8L\eta _k^2}{8} \Vert {P(x_k, g_k, \eta _k)} \Vert ^2
- \sum _{k=1}^K \left( \frac{\eta _k}{4}- \frac{3\eta _k^2L^2T}{2}\sum _{l=1}^{l_k}\eta _{k+l} \right) \Vert { P(x_{k}, \t... | {
"cite_spans": []
} | 1802.08880 | Asynchronous Stochastic Proximal Methods for Nonconvex Nonsmooth
Optimization | [
"Rui Zhu",
"Di Niu",
"Zongpeng Li"
] | [
"cs.LG"
] | 2,018 | en | Computer Science | [
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2eb327321ce67a23f19150a27dd6d5c8c147155a | subsection | 24 | 30 | Proof of Corollary | From the condition of Corollary, we have\eta \le \frac{1}{16L(T+1)^2}.It is clear that the above inequality also satisfies the condition in Theorem REF . By doing so, we can have
Furthermore, we have\frac{3LT^2\eta }{2} &\le \frac{3LT^2}{2}\cdot \frac{1}{16L(T+1)^2} \le 1, \\
\frac{3L^2T^2\eta ^3}{2} &\le L\eta ^2.Sinc... | {
"cite_spans": []
} | 1802.08880 | Asynchronous Stochastic Proximal Methods for Nonconvex Nonsmooth
Optimization | [
"Rui Zhu",
"Di Niu",
"Zongpeng Li"
] | [
"cs.LG"
] | 2,018 | en | Computer Science | [
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218ee003b70f108157d2af338c536e088d4f9f1e | subsection | 25 | 30 | Proof of milestone lemmas | [Proof of Lemma REF ]
Let \bar{x}_{k+1} = \mathbf {prox}^{}_{\eta _k h}(x_k - \eta _k \tilde{g}_k) and apply Lemma REF , we have\begin{split}
\Psi (x_{k+1}) &\le \Psi (\bar{x}_{k+1}) + \langle {x_{k+1}-\bar{x}_{k+1}, \nabla f(x_k) - \tilde{G}_k} \rangle + \left( \frac{L}{2} - \frac{1}{2\eta _k} \right) \Vert {x_{k+1}-x... | {
"cite_spans": []
} | 1802.08880 | Asynchronous Stochastic Proximal Methods for Nonconvex Nonsmooth
Optimization | [
"Rui Zhu",
"Di Niu",
"Zongpeng Li"
] | [
"cs.LG"
] | 2,018 | en | Computer Science | [
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27a927c428f700c1c592147d8e80845381ed965b | subsection | 26 | 30 | Proof of milestone lemmas | \end{split}Now we turn to bound \Psi (\bar{x}_{k+1}) as follows:\begin{split}
f(\bar{x}_{k+1}) &\le f(x_k) + \langle {\nabla f(x_k), \bar{x}_{k+1} - x_k} \rangle + \frac{L}{2}\Vert {\bar{x}_{k+1} - x_k} \Vert ^2 \\
&= f(x_k) + \langle {g_k, \bar{x}_{k+1} - x_k} \rangle + \frac{\eta _k^2 L}{2}\Vert {P(x_k, \tilde{g}_k, ... | {
"cite_spans": []
} | 1802.08880 | Asynchronous Stochastic Proximal Methods for Nonconvex Nonsmooth
Optimization | [
"Rui Zhu",
"Di Niu",
"Zongpeng Li"
] | [
"cs.LG"
] | 2,018 | en | Computer Science | [
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80de981796a3cbcc57edc007f6699a214252f14d | subsection | 27 | 30 | Proof of milestone lemmas | By rearranging terms on both sides, we have\Psi (\bar{x}_{k+1}) \le \Psi (x_k) - (\eta _k - \frac{\eta _k^2 L}{2}) \Vert {P(x_k, \tilde{g}_k, \eta _k)} \Vert ^2 + \langle {g_k - \tilde{g}_k, \bar{x}_{k+1} - x_k} \rangleTaking the summation of (REF ) and (REF ), we have\begin{split}
&\quad \ \Psi (x_{k+1}) \\
&\le \Psi ... | {
"cite_spans": []
} | 1802.08880 | Asynchronous Stochastic Proximal Methods for Nonconvex Nonsmooth
Optimization | [
"Rui Zhu",
"Di Niu",
"Zongpeng Li"
] | [
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43d1c3f3bf441b6dd114b288e52d143f0771139c | subsection | 28 | 30 | Proof of milestone lemmas | \end{split}Therefore, we have\begin{split}
&\quad \ \mathbb {E}[\Psi (x_{k+1})|\mathcal {F}_k] \\
&\le \mathbb {E}[\Psi (x_k)|\mathcal {F}_k] + \mathbb {E}[\langle {x_{k+1}-x_k, g_k-\tilde{g}_k} \rangle |\mathcal {F}_k] + \frac{L\eta _k^2 - \eta _k}{2} \mathbb {E}[\Vert {P(x_k, \tilde{G}_k, \eta _k)} \Vert ^2|\mathcal ... | {
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Optimization | [
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fdd0256d8c52c5888aa38a9eb57e0ab8d57bf097 | subsection | 29 | 30 | Proof of milestone lemmas | By taking the expectation on both sides, we have\begin{split}
\mathbb {E}[\Vert {x_k - x_{k-\tau }} \Vert ^2 ]
&\le 2\tau \sum _{l=1}^{\tau } \eta _{k-l}^2\Vert {\tilde{G}_{k-l}-\tilde{g}_{k-l}} \Vert ^2 + 2\left\Vert {\sum _{l=1}^{\tau } \eta _{k-l} P(x_{k-l}, \tilde{g}_{k-l}, \eta _{k-l}) } \right\Vert ^2 \\
&\le \fr... | {
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} | 1802.08880 | Asynchronous Stochastic Proximal Methods for Nonconvex Nonsmooth
Optimization | [
"Rui Zhu",
"Di Niu",
"Zongpeng Li"
] | [
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967d0ef79da3c987aed954aaa3642bacb7fd5acf | abstract | 0 | 12 | Abstract | Semantic parsing offers many opportunities to improve natural language
understanding. We present a semantically annotated parallel corpus for English,
German, Italian, and Dutch where sentences are aligned with scoped meaning
representations in order to capture the semantics of negation, modals,
quantification, and pre... | {
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} | 1802.08599 | Evaluating Scoped Meaning Representations | [
"Rik van Noord",
"Lasha Abzianidze",
"Hessel Haagsma",
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88ebff6f888d384f46aa3cb658938b32eb007c83 | subsection | 1 | 12 | Introduction | Semantic parsing is the task of assigning meaning representations to natural language expressions. Informally speaking, a meaning representation describes who did what to whom, when, and where, and to what extent this is the case or not.
The availability of open-domain, wide coverage semantic parsers has the potential ... | {
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3a20de804c222fb77478479d7d532515f67edef7 | subsection | 2 | 12 | Discourse Representation Structures | The backbone of the meaning representations in our annotated corpus is formed by the Discourse Representation Structures (DRS) of Discourse Representation Theory . Our version of DRS integrates WordNet senses , adopts a neo-Davidsonian analysis of events employing VerbNet roles , and includes an extensive set of compar... | {
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5ea2de15a9957b4fb5554ec3fb54f9e1f2e71c6c | subsection | 3 | 12 | Discourse Representation Structures | The resulting clauses are then of the form t R t' or t R t' t” where R \in \cal {C}\cup \cal {T}\cup \cal {O}.
The result of translating DRSs to sets of clauses is shown in Figure REF .
In a clausal form, it is assumed that different variables are represented with different variable names and vice versa.
Due to this, b... | {
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} | 1802.08599 | Evaluating Scoped Meaning Representations | [
"Rik van Noord",
"Lasha Abzianidze",
"Hessel Haagsma",
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ebbd46f5236b51ce7c11861addc4fc02d713fde8 | subsection | 4 | 12 | Comparing DRSs to AMRs | Since DRSs in a clausal form come close to the triple notation of AMRs , and both aim to model meaning of natural language expressions, it is instructive to compare these two meaning representations.
The main difference between AMRs and DRSs is that the latter ones have explicit scopes (boxes) and scopal operators such... | {
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a1810e842deeccaa8493f5e6fc0060fb68a423ed | subsection | 5 | 12 | The Parallel Meaning Bank | The scoped meaning representations, integrating word senses, thematic roles, and the list of operators, form the final product of our semantically annotated corpus: the Parallel Meaning Bank. The PMB is a semantically annotated corpus of English texts aligned with translations in Dutch, German and Italian .
It uses the... | {
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21b369ec3ad28babb5229d727eaa083f76ab1dae | subsection | 6 | 12 | Evaluation by Matching | In the context of the Parallel Meaning Bank there are two main reasons to verify whether two scoped meaning representations capture the same meaning or not:
(1) to be able to evaluate semantic parsers that produce scoped meaning representations by comparing gold-standard DRSs to system output; and (2) to check whether ... | {
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823ca944b000d39fa7238c1de304bb338bb1efe2 | subsection | 7 | 12 | Evaluation by Matching | Before matching two DRSs, redundant REF-clauses are removed. A REF-clause \langle [0]b1 REF x1\rangle is redundant if its discourse referent [0]x1 occurs in some basic condition of the same DRS [0]b1. Figure REF shows some examples of redundant REF-clauses. Not removing these redundant clauses would lead to inflated ma... | {
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} | 1802.08599 | Evaluating Scoped Meaning Representations | [
"Rik van Noord",
"Lasha Abzianidze",
"Hessel Haagsma",
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7cf2f4364bc71b00c8aa6e8c9ff05f3011cfeb14 | subsection | 8 | 12 | Evaluating Matching | As we showed in Figure REF , DRSs are about twice as large as AMRs. This increase in size might be problematic, since it increases the average runtime for comparing DRSs. Moreover, if there are more variables, more restarts might be needed to ensure a reliable score, again increasing runtime.Therefore, our goal is that... | {
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76c5066f13a22d285e1c86512d4caab1dd4f79be | subsection | 9 | 12 | Semantic Parsing | The first purpose of counter is to evaluate semantic parsers for DRSs. Since this is a new task, there are no existing systems that are able to do this. Therefore, we show the results of three baseline systems pmb pipeline,
Spar, and amr2drs (Subsection REF ).Spar and amr2drs are available at: https://github.com/RikVN/... | {
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f82c345189736c405bdcf82974452d1f11e0b27f | subsection | 10 | 12 | Comparing Translations | The second purpose of counter is checking whether translations are meaning-preserving. As a pilot study, we compare the gold standard meaning representations of German, Italian and Dutch translations in the release to their English counterparts. The results are shown in Table REF . The high F-scores indicate that the m... | {
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958d2e056db865b5417a4584601025b1425ee8f1 | subsection | 11 | 12 | Conclusions and Future Work | Large semantically annotated corpora are rare. Within the Parallel Meaning Bank project, we are creating a large, open-domain corpus annotated with formal meaning representations. We take advantage of parallel corpora, enabling the production of meaning representations for several languages at the same time. Currently,... | {
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"raw": "Hamp, B. and Feldweg, H. (1997). GermaNet - a lexical-semantic net for German. In In Proceedings of ACL workshop Automatic Information Extraction and Building of Lex... | 1802.08599 | Evaluating Scoped Meaning Representations | [
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cdb1fc34cb9d352f4feef85cdf80c7b0cf3cb34b | abstract | 0 | 33 | Abstract | Citation parsing is fundamental for search engines within academia and the
protection of intellectual property. Meticulous extraction is further needed
when evaluating the similarity of documents and calculating their citation
impact. Citation parsing involves the identification and dissection of citation
strings into ... | {
"cite_spans": []
} | 1805.04798 | Citation Data-set for Machine Learning Citation Styles and Entity
Extraction from Citation Strings | [
"Niall Martin Ryan"
] | [
"cs.DL"
] | 2,018 | en | Computer Science | [
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4ba8d7d66b53a6830a4f9923ea97cedfb0e539c2 | subsection | 1 | 33 | Introduction | In the past decade there has been an explosion in the amount of scientific publications accessible on many different library websites. In order to search and use these libraries effectively there is a amplifying demand for accurate organization. Organizing these articles by using the meta-data associated with them, all... | {
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"raw": "Joeran Beel, Bela Gipp, Stefan Langer, and Corinna Breitinger. Research paper recommender systems: A literature survey. International Journal on Digital Libraries, (4):305–338, 2016. doi: 10.1007/s0... | 1805.04798 | Citation Data-set for Machine Learning Citation Styles and Entity
Extraction from Citation Strings | [
"Niall Martin Ryan"
] | [
"cs.DL"
] | 2,018 | en | Computer Science | [
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9f006e5c546f2ca7b4b818db2117d7f1786b18c3 | subsection | 2 | 33 | Introduction | Argon, Cenk, and Steven W. McLaughlin 2002 A parallel decoder for low latency decoding of turbo product codes. IEEE Communications Letters 6(2). IEEE Communications Letters: 70–72. Retrieved. from https://doi.org/10.1109/4234.984698.
ARGON, Cenk; MCLAUGHLIN, Steven W. A parallel decoder for low latency decoding of turb... | {
"cite_spans": []
} | 1805.04798 | Citation Data-set for Machine Learning Citation Styles and Entity
Extraction from Citation Strings | [
"Niall Martin Ryan"
] | [
"cs.DL"
] | 2,018 | en | Computer Science | [
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29c2742d7202e04a23aba4eee605a24b0061a382 | subsection | 3 | 33 | Motivation | Accurate citation parsing is a necessity within academic search engines and for the security of intellectual property. ‘Automated extraction of bibliographic data, such as article titles, author names, abstracts, and references is essential to the affordable creation of large citation databases’ [1]. It aids in the ide... | {
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"raw": "Mateusz Fedoryszak, Dominika Tkaczyk, and Lukasz Bolikowski. Large scale citation matching using apache hadoop. In Trond Aalberg, Christos... | 1805.04798 | Citation Data-set for Machine Learning Citation Styles and Entity
Extraction from Citation Strings | [
"Niall Martin Ryan"
] | [
"cs.DL"
] | 2,018 | en | Computer Science | [
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... | |
226cc35a87b0695867c41f2ba9ef1e65f37e0533 | subsection | 4 | 33 | Research Problem | Over the years many approaches to reference parsing have been proposed, including regular expressions, knowledge-based approaches and supervised machine learning. Machine learning-based solutions, in particular those falling into the category of supervised sequence tagging, are considered a state-of-the-art technique f... | {
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"raw": "Andrew Kachites McCallum, Kamal Nigam, Jason Rennie, and Kristie Seymore. Automating the construction of internet portals with machine learning. Information Retrieval, 3(2):127–163, 2000.",
"s... | 1805.04798 | Citation Data-set for Machine Learning Citation Styles and Entity
Extraction from Citation Strings | [
"Niall Martin Ryan"
] | [
"cs.DL"
] | 2,018 | en | Computer Science | [
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a00d4ae5811501ae4671821ee0c8661bb46e18f6 | subsection | 5 | 33 | Research Problem | Rule-based methods have some draw-backs and only really have success in specific small to moderate journal areas due to the fact that journal publishers regularly are given specifics on what predefined citation styles to use. These rule-based methods are also less adaptable and are difficult to optimize under these cir... | {
"cite_spans": []
} | 1805.04798 | Citation Data-set for Machine Learning Citation Styles and Entity
Extraction from Citation Strings | [
"Niall Martin Ryan"
] | [
"cs.DL"
] | 2,018 | en | Computer Science | [
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... | |
4412542e534c2b329a1151e6f39194ee6402deed | subsection | 6 | 33 | Research Goal | The research goal is to build a massive, open source data set which can be used for training high end citation parsing machine learning tools and evaluate their performance. This goal can be broken into gathering BibTeX entries, citation generation into thousands of different styles and annotating of citations. The Bib... | {
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{
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"raw": "Michel Krämer. citeproc-java: A citation style language (csl) processor for java. URL http://michel-kraemer.github.io/citeproc-java/.",
"source_ref_id": "0cffb25ebf229bc6fc2a4719f333cbca3b237d... | 1805.04798 | Citation Data-set for Machine Learning Citation Styles and Entity
Extraction from Citation Strings | [
"Niall Martin Ryan"
] | [
"cs.DL"
] | 2,018 | en | Computer Science | [
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0.020785... | |
ff9648b98ee4efddbe502742d886b6a6ec4df8fd | subsection | 7 | 33 | State of the Art | Numerous methodology have been used in research articles to solve the generalized problem of meta data extraction in reference strings. One of which is "A structural SVM approach for reference parsing" [1]. This approach involves using structural support vector machines which is a supervised machine learning algorithm/... | {
"cite_spans": [
{
"arxiv_id": "",
"doi": "10.1186/1471-2105-12-s3-s7",
"end": 561,
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"raw": "Xiaoli Zhang, Jie Zou, Daniel X Le, and George R Thoma. A structural svm approach for reference parsing. BMC bioinformatics, 12(3):S7, 2011a... | 1805.04798 | Citation Data-set for Machine Learning Citation Styles and Entity
Extraction from Citation Strings | [
"Niall Martin Ryan"
] | [
"cs.DL"
] | 2,018 | en | Computer Science | [
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3c131b58d28c2caa4ef8605898b2057b1343a176 | subsection | 8 | 33 | State of the Art | The extraction process proceeds by determining the arrangement of states that is most probabilistic for generating the complete reference and then placing corresponding labels with their fields according to the sequence of the states. Yin et al uses a "dynamic programming solution called the viterbi algorithm" which so... | {
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{
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"raw": "Ping Yin, Ming Zhang, ZhiHong Deng, and DongQing Yang. Metadata extraction from bibliographies using bigram hmm. In International Conferen... | 1805.04798 | Citation Data-set for Machine Learning Citation Styles and Entity
Extraction from Citation Strings | [
"Niall Martin Ryan"
] | [
"cs.DL"
] | 2,018 | en | Computer Science | [
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0... | |
46cd720e18df6bba3ec3bb0f7c705be1d885df50 | subsection | 9 | 33 | Pre-Processing of documents | Pre-Processing is important because of the crucial need for accuracy when calculating citation density, connections and making recommendations. It is needed because of errors obtained in a lot of databases because of human error upon citing and creating references and also through formatting issues which do not maintai... | {
"cite_spans": [
{
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"end": 1359,
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"raw": "Isaac G Councill, C Lee Giles, and Min-Yen Kan. Parscit: an open-source crf reference string parsing package. In LREC, volume 8, pages 661–667, 2008.",
"source... | 1805.04798 | Citation Data-set for Machine Learning Citation Styles and Entity
Extraction from Citation Strings | [
"Niall Martin Ryan"
] | [
"cs.DL"
] | 2,018 | en | Computer Science | [
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0... | |
450d9e64fa094784bcdddf31724f38be788544bd | subsection | 10 | 33 | Pre-Processing of documents | We just have more data.". Of course this is not always necessarily true, but in general more data is rarely a negative impact on results. A paper describing the means by which human complexities cannot be extrapolated through mathematical equations and instead accept those complexities, use what is useful and available... | {
"cite_spans": [
{
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"doi": "10.1109/mis.2009.36",
"end": 348,
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"raw": "Alon Halevy, Peter Norvig, and Fernando Pereira. The unreasonable effectiveness of data. IEEE Intelligent Systems, 24(2):8–12, 2009.",
"sourc... | 1805.04798 | Citation Data-set for Machine Learning Citation Styles and Entity
Extraction from Citation Strings | [
"Niall Martin Ryan"
] | [
"cs.DL"
] | 2,018 | en | Computer Science | [
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0... | |
c0239be062732e3e97637318c690c27ce7bf4b35 | subsection | 11 | 33 | Introduction to Scraping | After trudging through the internet looking for data-sets that match my criteria and coming up empty. Automated scraping seemed like the most effective solution considering there were many sites that offer articles in BibTeX format with the click of a button. Scraping involves the requesting of html pages/files from se... | {
"cite_spans": []
} | 1805.04798 | Citation Data-set for Machine Learning Citation Styles and Entity
Extraction from Citation Strings | [
"Niall Martin Ryan"
] | [
"cs.DL"
] | 2,018 | en | Computer Science | [
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-0.003225130494683981,
-0.024458352476358414,... | |
9ee7e7dbe89a12f2c098b6dfb6e3f552e6291030 | subsection | 12 | 33 | Security and Reliable Sources without inaccuracies | All of the data scraped was freely available to the public and I don't need to consider data protection laws when porting it to a database. A main concept behind this investigation was making sure the data collected was of good standard from reliable sources so that the data did not contain errors or artificial entries... | {
"cite_spans": []
} | 1805.04798 | Citation Data-set for Machine Learning Citation Styles and Entity
Extraction from Citation Strings | [
"Niall Martin Ryan"
] | [
"cs.DL"
] | 2,018 | en | Computer Science | [
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... | |
8c83de1e8186254b154b40cec4beb82477aea3ab | subsection | 13 | 33 | Finding efficiency | Google Scholar is a excellent resource for finding articles that suit your research tastes or articles that relate to other topics through citations. This is why scraping it seemed ideal for my data-set needs as a reliable product. Although upon further investigation citation counts were susceptible to manipulation by ... | {
"cite_spans": [
{
"arxiv_id": "",
"doi": "10.1145/1810617.1810683",
"end": 346,
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"raw": "Joeran Beel and Bela Gipp. On the robustness of google scholar against spam. In Proceedings of the 21st ACM Conference on Hypertext and Hyperme... | 1805.04798 | Citation Data-set for Machine Learning Citation Styles and Entity
Extraction from Citation Strings | [
"Niall Martin Ryan"
] | [
"cs.DL"
] | 2,018 | en | Computer Science | [
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... | |
08ea85d119a15a41bb79f0db44aa097ceb65063f | subsection | 14 | 33 | DBLP | On account of most of the scrapers having id's for each of their BibTeX entries, I was on average getting 1 BibTeX entry per 1 request which is good depending on the speed of your requests and if you can cut the time down by running things in parallel or concurrently. DBLP, which is a computer science bibliography libr... | {
"cite_spans": []
} | 1805.04798 | Citation Data-set for Machine Learning Citation Styles and Entity
Extraction from Citation Strings | [
"Niall Martin Ryan"
] | [
"cs.DL"
] | 2,018 | en | Computer Science | [
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... | |
7139e39dd905fd1be54aee3e13b66a123e98b55d | subsection | 15 | 33 | Scraping under the radar | Automated scraping using your own 'bot' violates almost every single search engine's terms of service especially if your bot doesn't even look at their robot.txt file which outlines the terms and rules that bots must follow when using the site.
"The search engines aren't naive. Google knows every search engine operator... | {
"cite_spans": [
{
"arxiv_id": "",
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"end": 409,
"openalex_id": "",
"raw": "How to: Scrape search engines without pissing them off, Feb 2017. URL https://searchnewscentral.com/blog/2011/09/28/how-to-scrape-se arch-engines-without-pissing-them-off/.",
"source_ref_id": ... | 1805.04798 | Citation Data-set for Machine Learning Citation Styles and Entity
Extraction from Citation Strings | [
"Niall Martin Ryan"
] | [
"cs.DL"
] | 2,018 | en | Computer Science | [
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66121ce9b856e0711ffef1a44e796a48df09de7c | subsection | 16 | 33 | Maintenance | On my server I had upwards of five to seven scrapers running at any one time. This meant that I was constantly checking their progress and fixing bugs that would arise quite often. I implemented a simple tracking system so that if any of the scrapers was cut short on execution, it would give information on the error, t... | {
"cite_spans": []
} | 1805.04798 | Citation Data-set for Machine Learning Citation Styles and Entity
Extraction from Citation Strings | [
"Niall Martin Ryan"
] | [
"cs.DL"
] | 2,018 | en | Computer Science | [
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... | |
055e96482beae3a27492f0d07f1d603e1c79c47c | subsection | 17 | 33 | Parsing & Removal of Impurities | BibTeX is a format which is relatively simple to parse in most languages. The issue lies in entries containing special characters which will affect a parsers interpretation of an entry, which in turn will produce errors. There exists many 'Bib cleaners' which attempt to clean BibTeX databases by removing duplicates, ch... | {
"cite_spans": []
} | 1805.04798 | Citation Data-set for Machine Learning Citation Styles and Entity
Extraction from Citation Strings | [
"Niall Martin Ryan"
] | [
"cs.DL"
] | 2,018 | en | Computer Science | [
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-... | |
fa8f03183a229f269b9637bb83fa974b09e9e658 | subsection | 18 | 33 | The 'HomePage' problem | Within the DBLP scraper I noticed a large number of similar disparities which had accumulated due to the @misc field type for BibTeX entries.All BibTeX field types can be seen in the B appendix. Misc in particular is the shortening of miscellaneous and is used when the type of the entry is not apparent. Within DBLP the... | {
"cite_spans": []
} | 1805.04798 | Citation Data-set for Machine Learning Citation Styles and Entity
Extraction from Citation Strings | [
"Niall Martin Ryan"
] | [
"cs.DL"
] | 2,018 | en | Computer Science | [
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a7a1c84260a2c2653df5f991ec0cd7852ccd2a90 | subsection | 19 | 33 | Citation Styles | When referencing research papers and other scholarly articles, particular styles are used depending on the preference of the university or department. This is why citation parsing for meta-data generation is such a complex issue, of course if the parser knew which citation style is being used it could reverse engineer ... | {
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Extraction from Citation Strings | [
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092352c882cf225e4c9fb3294773173536ea75db | subsection | 20 | 33 | Annotating Citation References | Annotated Citation References are references which contain meta-data fields in XML format which outline the fields that belong to the associated reference tokens. In order to create these annotated references with the correct corresponding fields or labels, the CSL files need to be altered in order to make the addition... | {
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} | 1805.04798 | Citation Data-set for Machine Learning Citation Styles and Entity
Extraction from Citation Strings | [
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27b0489096ad0b335c4a199ea538c1e2486ee216 | subsection | 21 | 33 | CiteProc Software & Efficiency Challenges | CiteProc-java is a Citation Style Language (CSL) processor for Java, it interprets and translates CSL styles and generates citations and bibliographies . Citeproc uses a CSL processor in order to make the conversion to a citation. Initializing the processor takes the style as a parameter, which means that you cannot us... | {
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dd27252c13881b96378b44fdd2e6598607ad3d3a | subsection | 22 | 33 | Structure of the Data-set | The structure of the data-set is dictated by the fields and information needed in order to train and evaluate existing citation tools which are lacking in regards to the extensiveness and diversity of its training data. The BibTeX information at the top of each entry is what is used to create the reference strings whic... | {
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} | 1805.04798 | Citation Data-set for Machine Learning Citation Styles and Entity
Extraction from Citation Strings | [
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206f90ef410a2567af60b3603410740dc12074ae | subsection | 23 | 33 | Meta-Data Field consistency & Noise | The need for clean, consistent and clear meta-data fields are necessary when making string comparisons between both the meta-data fields themselves and the inner values of the fields. This means that inaccuracies of meta-data fields can impact assessment percentages of parsing tools. A simple example of this is if the ... | {
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} | 1805.04798 | Citation Data-set for Machine Learning Citation Styles and Entity
Extraction from Citation Strings | [
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4e0149f102b42950eec179cf145002223eb2f87f | subsection | 24 | 33 | Results | The overall scraping resulted in 7-8 text files containing an estimated 2.5 million BibTeX entries in total. Which when multiplied by the number of styles I have prepared for annotation (1600) gives 4 billion possible instances over 2.5 million unique reference strings
Currently the data-set contains approximately 20 m... | {
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} | 1805.04798 | Citation Data-set for Machine Learning Citation Styles and Entity
Extraction from Citation Strings | [
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96d12273fdfe3ff2078523f390dd203356260b18 | subsection | 25 | 33 | Future Work & Data-Set Usability | This Data-set will be used for training citation parsing tools which feature machine learning based approaches. The tools will learn custom parsing rules from this training data. To mention a few of the open sources tools, CERMINE , GROBID , ParsCit . | {
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Extraction from Citation Strings | [
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fe4266b6e522f397e7ea90e107778450a334ec32 | subsection | 26 | 33 | The Process of Evaluation Testing | 'Out of the box' versions of the tools contain pre-trained machine learning models which are used if no other model is provided. The comprehensive data-set is split into 66% training data and 33% evaluation sets. A lot of the tools ask for the annotated references to be formatted in a particular way before being proces... | {
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} | 1805.04798 | Citation Data-set for Machine Learning Citation Styles and Entity
Extraction from Citation Strings | [
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96b9babab11b6e6b84cfbac5c853bfbc34fd6288 | subsection | 27 | 33 | Comparison Method | For each tool, the given output fields that were extracted for each reference are compared against the annotated values of the same reference stored within the data-set which are referred to as the 'ground truth' values. The output fields are altered slightly in order to clean them, which includes cleaning of special c... | {
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Extraction from Citation Strings | [
"Niall Martin Ryan"
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cd3ab12b2928e097d4f9c2839b5ce55eecdd5aa1 | subsection | 28 | 33 | Outlook | The scraping was cut short because of time constraints on the project. Additionally the scarcity of available disk and memory on my server was a contributing factor.
My server was running out of disk due to the scale of these entries and the amount of data that was being stored in text files from the BibTeX entries. I ... | {
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} | 1805.04798 | Citation Data-set for Machine Learning Citation Styles and Entity
Extraction from Citation Strings | [
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cea9a295db31e0fc0f0d1304c0cf9b57a8cd1666 | subsection | 29 | 33 | Conclusion | Through research of countless scientific papers in this area and maneuvering through the struggles of scraping, I can see why open source data sets for citation parsing tools are sparse. Regardless of this, I am positive that this data set will continue to grow and increase performance for open source meta data extract... | {
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Extraction from Citation Strings | [
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4e69136111e72ff25fa23dde5ba57084a5e9c040 | subsection | 30 | 33 | Terms | 1.5BibTeX - Bibliography Type SettingDBLP - The dblp computer science bibliography is the on-line reference for open bibliographic information on computer science journals and proceedingsCCS - The Collection of Computer Science BibliographiesPubMed - PubMed is a free search engine accessing primarily the MEDLINE databa... | {
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} | 1805.04798 | Citation Data-set for Machine Learning Citation Styles and Entity
Extraction from Citation Strings | [
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316eb50f12b64127d5dbb7666e041519574c39ea | subsection | 31 | 33 | Information on BibTeX | These are the standard entry types of BibTeX entries and the details of what is required or optional for eacharticle
An article from a journal or magazine. Required fields: author, title, journal, year. Optional fields: volume, number, pages, month, note.
book
A book with an explicit publisher. Required fields: author... | {
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eacc71c4f01210c7955e038118ad2b3aaf73c65e | subsection | 32 | 33 | Information on BibTeX | If there is also an author field, then the editor field gives the editor of the book or collection in which the reference appears.
howpublished
How something strange has been published. The first word should be capitalized.
institution
The sponsoring institution of a technical report.
journal
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7e08357c852ec49d57320e6feb119bd48e32a7a8 | abstract | 0 | 118 | Abstract | This is an exciting time for the study of r-process nucleosynthesis.
Recently, a neutron star merger GW170817 was observed in extraordinary detail
with gravitational waves and electromagnetic radiation from radio to gamma
rays. The very red color of the associated kilonova suggests that neutron star
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7a0e21b706fd18e53e4b36eccdbd774aaf3ef9c4 | subsection | 1 | 118 | Introduction | How were the elements from Iron to Uranium made? The influential National Academy of Science report “Connecting Quarks to the Cosmos” identified this question as one of eleven questions at the intersections of astronomy and physics that are of deep interest and are ripe for answering . Ever since the pioneering works o... | {
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54600cea357f86f14ba8c7f9b3ce3cba051b78d1 | subsection | 2 | 118 | Introduction | Galactic chemical evolution simulations are reviewed in Sec. , while Sec. reviews astrophysical simulations of r-process sites. Given conditions present in a site, one can perform detailed nuclear reaction network simulations to predict nucleosynthetic yields. Here, an accurate understanding of the relevant nuclear phy... | {
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251b1fd6a19558c59144f85666a7103491a0c4e8 | subsection | 3 | 118 | Observations of stellar abundances | The heavy elements in the atmospheres of most late-type (F-G-K) stars, which have effective temperatures of \approx 4000–7000 K, reflect the stars' natal compositions, and are untouched by the products of nuclear burning in the interior. Each star thus retains a chemical memory of the content of one piece of the inter... | {
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eebd5bd6e09398ef7256237392cc991271da583e | subsection | 4 | 118 | Measuring detailed | Late-type stars are the only sites beyond the Solar System where detailed chemical abundance patterns for large numbers of elements can be derived. When metal-poor stars that are highly enhanced in r-process elements are identified, detailed abundance analyses based on high-resolution spectra, model atmospheres, and at... | {
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0ad7f3f6a4d5e5aa05da640333e0116a8d5cb7f4 | subsection | 5 | 118 | Measuring detailed | The relative abundances and locations (in mass) of the r-process peaks are sensitive to the conditions (e.g., ), so they are especially valuable probes of the r-process. Many of the radioactive progenitor nuclei for elements at the first and second r-process peak can be produced by current radioactive beam facilities i... | {
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f62f488c69db0240285a1477a1d643ebe9586706 | subsection | 6 | 118 | Deviations from the | One key observational result is that the r-process pattern is robust from one star to another, and agrees well with the Solar System r-process residuals, for elements at and between the second and third r-process peaks (Ba to Au, e.g., , ). Sometimes this so-called “universality” extends to lighter r-process elements a... | {
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e1952faf3fe2698612c55061e6bdba5a41e06f86 | subsection | 7 | 118 | Environmental constraints on the | Stars like HD 108317 or CS 22892-052 are located in the field, unaffiliated with any known stellar cluster, stream, or galaxy. This limits their utility in terms of constraining the site of the r-process based on its environment. Recently, the lowest-luminosity galaxies known—also called ultra-faint dwarf galaxies, or ... | {
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59b7ed33a6f3c1e9eb945dc01c07c5d5d5150053 | subsection | 8 | 118 | Evidence from elements not produced by the | Another observational approach to identify the astrophysical site(s) of the r-process is to consider the light elements that could be produced along with the r-process. Are the abundance ratios among elements from C to Zn (6 \le Z \le 30) statistically different in stars with high levels of r-process enhancement and t... | {
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99891db330be3fa4be0613936c448954986b8e5c | subsection | 9 | 118 | Stars with low levels of | Highly r-process enhanced stars, as highlighted in the previous sections, comprise only a few percent of the local Galactic halo field population, and only a small fraction of UFD galaxies boast large numbers of highly r-process enhanced stars. In these stars, elements produced by the r-process are still in the minorit... | {
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09f2b04d09b3165dd0c284ce13cb30077416aaf3 | subsection | 10 | 118 | Multi-messenger observations of possible | Observations of energetic astronomical events with not just photons, but also neutrinos or gravitational waves, can provide especially important information on the r-process. This is because neutrinos and gravitational waves come from deep within an astronomical event and may directly probe the extreme conditions that ... | {
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cd10ebdb3edbbc963f4ed262ede2872164f4a8ba | subsection | 11 | 118 | Neutrinos from core collapse supernovae | One frequently studied theoretical r-process site is the neutrino-driven wind during a core collapse supernova (CCSN). Here intense neutrino and antineutrino fluxes blow baryons off of the protoneutron star and determine the ratio of neutrons to protons in the wind. Antineutrinos capture on protons to make neutrons\bar... | {
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