text stringlengths 54 548k | label stringclasses 4
values | id_ stringlengths 32 32 |
|---|---|---|
Unlike the classic first-order distributed optimization method, the second-order distributed optimization method utilizes gradient information and second-order information (i.e., curvature information) to find a “better” descent direction to update the global model iteratively.
However, previous work {{cite:2f41fcb5d2... | m | 8a16ff24a4d3f0a8e617a745a32435f0 |
According to the end-to-end differentiable property of AutoLoss, we update {{formula:eb1773c1-28c6-4f39-836f-4d48515c449b}} and {{formula:0277c190-07fe-4ad0-bb04-76f109a750ac}} through gradient descent utilizing the differentiable architecture search (DARTS) techniques {{cite:8d169fb4085d30b663d9226476df81e40ddc1641}... | m | be0ea8a93f7f231b74a56f5f14a34096 |
In this paper, we use a simple yet powerful residual-like decoder with a new loss function for pixel-wise gaze prediction. The architecture is similar to the architecture in {{cite:9acc0f42baf7d5458567c5636c50d68c93c48c25}}, but we dispense with the GAN training and instead propose a simpler, residual decoder. We demon... | i | f6a1f2657c64a14a6e5df402f477f023 |
where, {{formula:eec59cdf-5a2d-4c7f-84aa-a79907791d71}} denotes the first two rows of the rotation matrix. Note that if the origin of the 3D world coordinate system gets mapped to the origin of the 2D image coordinate system then {{formula:329d781d-f592-41a2-9b70-b098e3e43f51}} ; this is usually implemented by center... | m | a4b827d2fa85580bc15d7f15713c5556 |
In Section REF we found that while AGN with relatively weak radio jets compared to the radiative emission from the nucleus (with {{formula:87e38392-4d8c-4b9b-a1d4-fbbcbf352ecf}} ) are found in galaxies with SMBH masses across the full range found in our sample ({{formula:14f381f6-81d6-4021-89e8-df0b5374a270}} ), all g... | d | 9aaa2b177e94f2c2e52601aebde9eadb |
Range of applicability.
The optimization framework introduced here can applied to any steady-state meso-scale system that can be modeled by a stationary Markovian (or Langevin-type) dynamics,
for which the rate of entropy production is related to the relative probability of forward and reverse
trajectories {{cite:32050... | d | e7353e88e1ac98000473124602923612 |
Positional encoding.
We observe that the proper positional encoding improve the quality of the generated images. In Table REF , we experimented with three different positional encoding schemes: sinusoidal {{cite:804a4f66498391641f4c447b78bd170d18203136}} encoding {{formula:c6982f68-88c3-4614-9a5c-59b0e2c34127}} in Eq.... | r | 05e25960c6f0db6c387d4f334afa0794 |
MF-BPR {{cite:a3b1f572a132a4022bac440a598757f648f33928}}: This is matrix factorization with Bayesian Personal Ranking (BPR) as the loss function.
FM {{cite:70c2c355656ffb267685f252333235220d0e0d1a}}: Factorization Machine (FM) is a content-based model
which uses feature interactions to model user preferences. We take... | m | 2bcea793893994522054f8a9ad5dc702 |
The band structure and Fermi surfaces are studied with the full potential, all-electron code fplo {{cite:6aa5422e82df74b254ad5d865e5fcf0c99837e64}} using the semi-local generalized gradient approximation (GGA) {{cite:685a2f155d71f63ad06c8e5f657c6de5edb7ed70}} exchange-correlation functional. This code uses a basis of p... | m | 5369ee7ba234ac9ee327b3223c6813d2 |
Several feature extraction methods, as well as classifiers, have been explored in the medical image processing area such as bag-of-visual-words {{cite:e96559a46a042245df3fcfa049cd9cefe356c4d7}}, the Gaussian filter, the Gabor filter {{cite:b64727c34289c2af039bd6838c40f5f56ddd358b}} as feature extractors; and K-Nearest ... | m | 035ceeb730a997507017dd83c62c0480 |
We implement a neural language model (NLM) using an existing encoder-decoder LSTMhttps://github.com/pytorch/examples/tree/master/word_language_model with 2 layers of size 200. We randomly split the dataset of each community into training (70%), validation (15%), and test (15%) sets and train one NLM per community using... | m | 0558829761d1b752bd1850a0b464cce8 |
The starting point for the proof of Theorem REF comes from the prior articles {{cite:076ba875dddc9f3e1631ace49908d7173f64beb0}}, {{cite:1c76a6788ce157b618bae7b534b078d65d60cf5b}}, {{cite:8473d860c5f3b2133ca287769bda0a98a5a3fc90}}, which obtained an insightful “function space” interpretation of {{formula:407e6e9c-2761-... | r | 8e7214547e2e4ae1767fa9b004d8e83b |
Comparisons on Market-1501.
As shown in Table REF , the proposed the proposed hierarchical similarity graph network (HSGNet) obtains the highest 95.0% Rank1, 98.9% Rank5 and 86.6% mAP.
For example, compared with two partition-based methods, i,e, SpindleNet {{cite:a691fdb77524dd394835247da668367ad8a04c42}} and PAR {{cit... | m | b5246472807e3d280fbb95af99116649 |
Positional Encoding Module (PEM). In the standard transformer{{cite:518ba724e3c5de57b57dc0f8733d6579afe7282f}}, {{cite:f3acbeb30e517f45837524dd4389f030fca6065c}} design, encoding spatial information has proved useful for recognition tasks. However, it is non-trivial for WSIs due to varying sizes. In this work, we emplo... | m | f1724b35da230d525b98b7271a5a1f08 |
Despite these benefits, there are situations and applications where alternative approaches might be better suited. In particular, autoregressive constructions that decompose the joint distribution into its telescoping univariate marginals {{cite:42be9100d82ce02abb248caec7da9609ed80d6f3}}, {{cite:91c4e8d62c2ca8ccba86ddb... | d | e341d89908d6ed817f3b5cbfcef7bc2b |
Finding short motifs presents significant challenges because many of the apparent relationships between short fragments could have arisen by chance and thus have no functional significance. Furthermore, most widely available tools for sequence database search and motif finding were designed with longer motifs in mind. ... | i | c6ad7958f2aa5168f0e1ff97f8b5b5a0 |
When designing models for EEG signal processing, we must take characteristics of EEG (e.g., non-stationarity and small amplitude) into consideration, as well as external circumstances such as electrode layout, interferences introduced by the surrounding environment during data collection. A possible instance may be ten... | d | f74cc58018bc99fcb400a617815091f7 |
Using the condition on probabilities {{formula:7e0f678a-c85e-4f8a-b209-84fb94bf30e7}} and
the first Borel-Cantelli lemma; see e.g., {{cite:4aff1991ddbbfc176dff9e6d3675dbd201b67508}}, we see that for
any {{formula:cf06bfa7-dbd3-4204-9d50-ac3815dd4f7e}}
{{formula:b5305a36-7e8c-4e56-b31a-f8feb8095c25}}
| r | ed30de359ca22b480bb2092460d03166 |
RotNet: Rotation prediction, proposed by Gidaris et al. {{cite:72b0bf6c9e4cb275dc7acd0ac6beec7c160be848}}, has been one of the most successful pretext tasks for the learning of useful semantic representations. In this approach, every image is transformed using all four rotation transformations, and the network is train... | m | 8540eff5c8a6277ac87f065a349b82af |
For {{formula:50767fca-e478-4981-9336-88c5fd32874a}} , {{formula:d135c2de-59d2-4687-8cf3-60f666ea12ea}} are defined in (REF ).
We call this method the Procrustes parareal method since
{{formula:cc690567-6f1c-4fd7-a2a0-315e4d9ca851}} is constructed by solving the Orthogonal Procrustes Problem {{cite:7a62482d64392c21f4... | m | 98a46f5a22eed5dc7e4f63a370c69fa7 |
The three-dimensional simulations are performed in a cubic domain of dimensions {{formula:53418594-7e57-4108-b06e-d6ead8416957}} . The numerical method {{cite:b4eb121cbf3467b8e8beddc3178c0b1d233bd06b}} combines the phase-field method {{cite:51247aeeb259bcdef13a8d9e6ca31586a609a720}}, {{cite:3d6210888eabf10042eaa3c9cdca... | m | b470474fd3e5a850c620a789b816783c |
Recently, many spectral GNN variants have been proposed, e.g., {{cite:710dcf473df7f1a83bbd9b54cc6e4f8d3b4b37eb}}, {{cite:4128b32f17a7fae0dc556019fca278ade9ad78c0}}. The main workflow of spectral GNNs follows four main steps: 1) transform the graph to the spectral domain using the graph Laplacian eigenfunctions (see Eq.... | m | 2ba2abe3983bd5b1618b975a23db5093 |
where {{formula:ddd06ee1-7076-4842-b3eb-66ff672a7269}} is the number of independent fitting parameters and {{formula:081eba65-ebd9-464b-9590-8aead408db78}} the number of data points.
The larger are the differences {{formula:13786f8b-8392-4dc1-ab08-2e1904130f69}} AIC ({{formula:187504cf-bfde-4a6f-85e3-c40f78d0cd26}} B... | d | 4c408ff7d312f82e00417b68349fe12d |
Time-series prediction plays an important role in many fields, such as sensor network monitoring{{cite:48575c194f5ac1f2e4196b3d6955f9b408dec5cd}},energy and smart grid management,economics and finance{{cite:8b4560c058b9f2ad13a00b524867caf8101e7170}},and disease propagation analysis{{cite:fcb70f364fa5667f171f0151ddb7fbb... | i | ced327d4aa58c2bf39c8c45de7581616 |
Software.
All experiments are done in Python 3.7.10. We used Scikit-learn 0.24.2 {{cite:1e9faea8b8d1c974f79cdea7ce441316ac1abb1b}} for machine learning models and PyTorch Geometric 1.6.3 {{cite:1897a2639c69d0b16bbeb0e960b43f4a6b4b32f6}} for graph neural network implementations.
For Riemannian geometric computations in ... | r | 718915eeeda7acac8908ae2cf2b37b6f |
Other cases of interest include arbitrary {{formula:6ea212c6-4a5b-4135-9b5f-e2ece022c1eb}} and {{formula:9806b22e-e3be-47ff-980d-3e335de6babb}} . These are the partition function for {{formula:cd7534fb-bf44-407b-b36b-9a4994b88da2}} -spin structures of type {{formula:7b93faf3-59c6-49f1-8a66-169782406694}} (here {{form... | d | 95934892a8675dc88767570f41c373ce |
There are several accounts of numerical treatment of energy balance models. The early simulations were based on spectral Legendre decomposition in space and the first order implicit Euler scheme in time {{cite:b965cbdc715403fb41a2c34457af340a3ce2a983}}. As was also noted in {{cite:ac6ead23e0c2f8f0a600c4a73652fb6310fa17... | i | 6cea8b73290eae3920360c3d059f7eb9 |
Since Richard Feynman's original and ground-breaking proposal in {{cite:4eb4f0605e08d53fb2650a0db98d4b437d96b3e7}} of constructing computers that follow the laws of quantum mechanics to simulate physical systems that obey said laws, the scientific community has gone to extraordinary lengths in order to build an operati... | i | 9ff01d462b3ff5b7e5eb251e1450def0 |
An important implication of our analysis is that while the
shapes of halos (and by extension elliptical galaxies) become more
oblate (especially at small radii), following the growth of a baryonic
component, the majority of their orbits are not S-tubes as might be
predicted from their shapes. Instead our analysis shows... | d | 837c022253b320306d0ff3ce32b29c7a |
The eigenvalue problem is solved with the FEAST library {{cite:4b7c1413f0d24764c4b00ebb61a96b4df121d36a}} which allows to solve for a specific region of the spectrum. We also use the MUMPS library {{cite:e1d4fb4d3b24ae5a75f64784039e3837916ca452}}, {{cite:d1d730950d4b14b350c73817d4f81590cfbdf4f8}} for performing matrix ... | m | fd5250ba224eac06004a8a2408a1f7ed |
In addition, we demonstrate that using a weighted adjacency matrix can lead to better performance of the graph neural network. In our solution, the weights for the edges are defined using a handcrafted formula. An alternative approach is to learn the edge weights with deep neural networks. For example, we can employ th... | d | 6947f09fb10c56e824003e244ac6501c |
AutoAugment and Model EMA show a decrease in accuracy. {{cite:9844fab048238004b1bcda051b1a5ee40120fcff}} mentions that Model EMA does not increase performance, so my findings fall in line with the original paper. Whereas AutoAugment, I did not put in the time to try to optimize the parameters so perhaps with some tunin... | r | 5962644f4ad564635ebdafb38c1d9b6f |
Questions raise themselves for future explorations. Is it possible to further generalize our results to RL with general function approximation under bounded Bellman Eluder dimensions {{cite:dded2336326c0745aabb62aa82cb86974d713283}}? Can we optimize the dependence on horizon {{formula:ac9e2ecc-8b2a-41bd-82df-024021a544... | d | 3a03e8f21217b9f8ee696b6b2304373c |
Proof. The inequality (REF ) can be derived from Lemma 2B.1 in {{cite:4b16d8d6c9bfd3ebf2c2e106c3164abc268288dd}}.
| r | 6565ffa69f2d9e304d1019d4fa912a79 |
The second group consists of sentence encoders from the Sentence-Transformers library trained using multilingual knowledge distillation method {{cite:ccb79c628cfb876c52490062dfd95001eb191403}}. These models offer varying performance on Polish language tasks. However, two of them stand out: paraphrase-xlm-r-multilingual... | r | e6cde92e9382186a738cc90371810079 |
For multi-organ segmentation, where multiple organs are segmented simultaneously, networks should be designed to own more powerful ability of discriminating the pixel-wise features.
OAN {{cite:23e55cc679887e4cdeb434de93151b99a54060e7}} designs a fusion network that takes 2D multi-view images as input and reconstructs t... | m | 4a337dc8dd7f90dc749cf932e2cdfab3 |
In terms of future directions given that the core renormalization group
functions of QCD are known at five loops in the {{formula:252bfa09-7fd1-4798-8a05-31b9b1039976}} scheme,
{{cite:d8ad8dcf57ef1bda6815751bce34319451a8176b}}, {{cite:2ca855621c9754af165dd9f9f152b053c84b8bf6}}, {{cite:627a81e6ecced5ae46c25d72959561800... | d | 5b6dcabd608e595901e18a441ccf82fd |
Matrix completion, whose central goal is to recover a large low-rank matrix based on a limited number of observable entries, has been widely studied in the last decade. Among various methods for matrix completion, spectral method is fast, easy to implement and achieves good performance {{cite:fada36ec08b3c215931d0cf0e4... | d | 1819c5dc00967a880d7971ef2c0f9929 |
Many other models use supervised learning rules {{cite:633b3b1d3c96b11a5e0c8ec906b3b0ba48a5ddad}}, {{cite:19092eed5b29beb37b36156f05a0c08a14065270}}, sometimes reaching impressive performance on natural image classification tasks {{cite:19092eed5b29beb37b36156f05a0c08a14065270}}. The main drawback of these supervised m... | d | d504adb3b658fe01c228f3dfcc4c0932 |
In HDNNPs{{cite:905fd6127e7b4b334e1c8f423d93d9f92c142ece}}, {{cite:3bb7b4cafd21812d7b8ff289edc1c0170cef5e9e}}, which we use to compute the energies and forces driving the molecular dynamics (MD) simulations, the potential energy is constructed as a sum of atomic energy contributions {{formula:ca72c310-d984-46e9-9a81-fe... | m | 705100c8040e1a628d719baf84a6cd61 |
Here, {{formula:9168d51f-7d18-4420-a04b-6c5e39a2c3ed}} is the observational uncertainty, and
{{formula:1bd62b53-5f70-4a39-92ba-6f8db811aa6f}} is the uncertainty associated with the
model itself, for the {{formula:1a321e67-47a0-45f2-b3fc-7aec0181cac2}} filter. {{cite:6e66db71ae92491baaf1b8154aeee65ce4d22d95}} estimat... | r | 218bf9acf4fcdef3edb07dd9790c412f |
Input {{cite:716ceedf02f19b4eaed10d6ca2c4a480835e4f27}}.
We sample historical flow videos from recent time to near history and distant history according to three corresponding temporal views: closeness, period, and trend.
We select hours, daily, and weekly as the key timesteps to construct the three views.
For each of ... | m | cfcf72a783acc5e7bcc7bb9803fe4381 |
In SNFs, MCMC is combined with normalizing flows by introducing sampling layers between the standard flow layers {{cite:8058a1e85827f93f65335a2a264e0d7582df9c34}}, {{cite:99eb6ff0e51c763f978c9ad34a681511c978f25d}}. They are usually trained with samples from the target and perform poorly when trained with samples from t... | d | 9a9946b03e8397647c3116e7b24aaf6c |
Motivated by the possibility of Planck-scale departures from known physics in the form of spacetime-symmetry breaking, a substantial research effort has been put towards highly accurate tests of Lorentz and CPT symmetry in gravity.{{cite:ec81de776bb89af54af470a24a4c54f926cf4446}}, {{cite:b8503622b0b67b91585c36391fd2591... | i | cce786aad82eb1f48fabab03a05e1b28 |
An alternative approach could be to consider the axions as inflaton candidates, as opposed to the saxions described above. However, these models are also in tension with the swampland programme and in particular with the axionic weak gravity conjecture (aWGC) {{cite:ce449f6a5785b7db8364bf502252d3c206fb54a3}}. The aWGC ... | d | 996c1cfe6d2443d2264861dc71a67832 |
Thus, {{formula:445cf8d4-9181-43be-a957-f8b44d681c9b}} is an infinite tree in which every vertex has finite degree. By König's infinity lemma (see {{cite:f19bfc26a1437360175e39b8187e9981333699eb}}), there exists an infinite path {{formula:7963e646-9ad4-434b-88f6-8a94ed5a76cb}} in {{formula:647fa469-0df4-4492-93c2-a47... | r | e5c70b224ef4d4c47b63707506927403 |
All methods included in the vision and language understanding experiments are summarized in Table REF . Specifically, we evaluate SNGP on a Wide ResNet 28-10 {{cite:37d4d4206d7e3555cb603739c167e9fcf313c574}} for image classification, and BERT{{formula:3c46b739-fc28-4914-a78e-7a265ec01825}} {{cite:74cc6165503e28fda8e8e... | m | fb304debdd5d4eaa94de1f89c92681f8 |
Each task {{formula:76850150-0cec-4a76-82aa-cf789c4e0f1b}} is represented by a corresponding vector representation {{formula:3190523c-13a0-412f-bfb7-99fdb9d8ab6c}} . Our framework allows any fixed-length vector representation for this task descriptor, including semantic embeddings such as GloVe {{cite:20761d08f1f5687e... | m | ad6b7b3428a849a29745026893e152d0 |
In the uplink of a communication network, several BSs may be able to measure the CSI of a UE. With the CSI from the available BSs, the UE localization can be performed with an early fusion or late fusion approach. In early fusion, the combined CSI of all the multiple BSs is considered as a single fingerprint that is us... | i | d47c0c0a680e5b83c0fffb00f3556fe9 |
The simple but naïve way to identify discussion topics is to look directly for keywords in posts (see visualisations in the Appendix). A better but more complex approach is topic modelling. We use a probabilistic generative model called Latent Dirichlet Allocation (LDA) {{cite:dc4821ea3ca413272516b60a17a1419564dcd1ff}}... | d | 1a5f5dcf89ff91e79086dfeac55831fa |
1. The nearest neighbor search is a greedy one. As a result it is possible for it to stop at the local optimum {{cite:6633d9b69084647a7611ba8180f7bd678bf39a6f}}.
| r | 97274a0f348234cb7a45ac4d33fd2d52 |
We visualize these two metrics on a sample CT volume in Fig. REF , along with the segmentation labels. For patches that are not relevant to the labels, {{formula:dc7cf560-9314-4814-98de-4804d735602c}} generates very similar {{formula:0e351d07-1663-41f7-9abf-5081675a4cb9}} ; on the other hand, patches that contain labe... | m | 1d7d4be3db9aeba255d61da6a44d4ec8 |
We point out that the low Mach number limit is an interesting topic
in fluid dynamics and applied mathematics. Now we briefly review
some related results on the Euler, Navier-Stokes and MHD equations.
In {{cite:6ed7012679ca9ca59ac1ed67b6eacb604e6f78ac}}, Schochet obtained the convergence of the non-isentropic
compressi... | i | 41baf1e63e9681d218b42573cfec8340 |
Some works proposed the learning-based sampling methods, which learn the sampling strategies through the lightweight neural networks. SampleNet {{cite:fd3ab7f60ba256f41f94736a1cc396460d4251a5}} and S-NET learn a subset of a point cloud by the neural networks. In these methods, other heuristic sampling methods (such as ... | m | 800f0256759ecd1b0b999582da9b8e42 |
Data Explanation: We employ two data sets of epidemic dynamics.
The real data set is collected from the Social Evolution experiment
{{cite:9647f3283e8decedc3e2d401f46cf078b3d2f8a4}}. This study records “common cold” symptoms of 65
students living in a university residence hall from January 2009 to
April 2009, tracking ... | r | 2ce6eeb3292ae74077b83b44ee7bce96 |
The alternating direction method of multipliers (ADMM) is an important variant of ALM and has been widely applied in machine learning, data analysis and signal processing. The studies of ADMM in the convex setting is comprehensive and clear, see, for instance, {{cite:cd59a62d4f945238a22c91cffe7c7818399e8462}}, {{cite:f... | i | d9c03f2d829688b76a6f5ce66873457d |
We compared our models with the state-of-the-art systems on English{{cite:b7501b12562b601ce61eaaedeabe460c0475fd4c}}'s results are different since their implementation did not convert the predicted BIOES tags back to BIO2 during evaluation. For fair comparison, we only report the results of the standard evaluation., Du... | r | 3538301a1895089a083f4f1e4939add1 |
The V-QCD baryon solution we present here has several advantages with
respect to similar constructions in the literature, as well as some
limitations. In order to discuss them, we shall compare the results of
this work with the two main models of holographic QCD in which
single-baryon solutions were analyzed. These are... | d | 061f83cedefa6dbc0589cb67e03bdcf4 |
We have assumed {{formula:23f70cc4-139d-4169-b0bf-eb167ce2c9e4}} is real, and that the different components
have equal number and mass. The second term contains
both the exchange and anomalous term, which couples {{formula:d9ecf841-1e03-47d1-af89-430aa69acb70}} to {{formula:d3a13c6e-3b37-49ee-bcc5-cfbfc451c05a}} and... | m | b965d8d9aafa445867ac0e99425f6b7d |
Statistical Comparisons of the Different {{formula:e658682b-9527-4cfa-853f-2e72ba2cad31}} Variants for Window Size 300ms: To examine the importance and significance of {{formula:5422ae3e-5440-49a5-b0e4-f721ed35064b}} variants, we perform statistical tests for all models considering {{formula:b37ef6a6-0c57-4dfe-ac72-c... | r | 0641fd52ee035a65a25e83c83a84f140 |
Large data sets and giant models, together with advances in deep learning algorithms and hardware, enable researchers to push the limit of many machine learning tasks {{cite:c5a4ee8534df1adb3e96bddf3b161b58a5303c29}}, {{cite:5d352fca4d766ffe62d5eefa1edfc980700ecb13}}, {{cite:1329a66a6347a173bc54523e2a1c7337b17c2fe0}} i... | i | dfb9907af0a21ef1accc5ef161a69529 |
Thus, we show that the inversion bias guarantee for LESS embeddings
matches our result for sub-gaussian sketches up to a logarithmic
factor. This additional factor is standard in the analysis of fast
sketching methods. It comes from the fact that, as an artifact of the
matrix concentration bounds {{cite:53fe26c641e6081... | r | ec9da7c3970f703ce4890804b3485916 |
The following word association and word similarity datasets are used throughout experimentation: Simlex {{cite:1d71206d2b8cf775ca8cd64e3e3ed967d55fde64}}, WordSim-353 {{cite:89bb314d0be760554de1b381ef73228b7da7b620}}, RG {{cite:4fee3827dfb0202867788aa7178b85ee9939987e}}, MTurk (MechanicalTurk-771) {{cite:ccd79329d0d77a... | r | cb3b7b687908d35123679a41129b994c |
for some fixed {{formula:85ee782a-44a9-412e-8436-59de77fa65f0}} {{cite:7a89b9e1484c6b71bf133dc2dd4ddaa666929262}}, {{cite:9b7ab0041aaa7242ab0803b09cdb1a10b55b8b4b}}, {{cite:0835c5846b4c5811d1a94e92e9fbd4cdcd59a490}}, {{cite:6e9225b07ce005d6b92e0ed2950f54fa79783967}}, {{cite:dc5f815831e73de7fc121a564ea8c2d13caa19a7}}. ... | m | 7a5e6657dd46a1c979fdb1e6c4e23820 |
Trends in sea level at long time-scales also impact changes in weather-like extremes. Recently, many studies have focused on quantification of current and future changes in sea level extremes driven by storms, tides and waves {{cite:0a73f37f605956893891a5b9eda23b76727a48b9}}, {{cite:e45d4fc8f1fe5b06690f700b5e5e48728c24... | i | 3f4dc1796abeb30e2b24bd2f2807ff6d |
where the authors utilized the mapping {{formula:74faf65c-aab0-44ad-8bc0-fc644e9ac453}} with step length {{formula:c2b664d8-3533-4d5a-8131-2d244eb02efa}} and the forcing terms {{formula:0a03219b-1f27-4b14-ae49-76b843dff7ec}} and {{formula:4ea53655-b6fe-4b77-bcfe-23427a3052fd}} to control the level of accuracy of th... | m | 5128e565cb7b541bc9165aa08be6754b |
The sum of all terms beyond quadratic order in the density expansion is known as the
`bridge functional' {{cite:7cc176748b103ebc9bd3c43ae73e95460b792509}}, {{cite:657f868c24cfe34c8264b0563d9c20b922ec5d21}}.
Rosenfeld has shown that the quadratic functional can be much improved by replacing the true bridge
functional of... | d | 15917058fa36e7c892563c062a57cbc2 |
In this paper, we will present two different proofs of the regularity of the compactified metrics near the conformal infinity for the cases when {{formula:5d71e1a9-528f-4a62-a0a4-d1ae145b3746}} is even and when {{formula:b09103cf-9ff5-4744-9fa0-da7593061de9}} is arbitrary. When {{formula:40bace37-c7ce-460e-a601-71288... | r | 7866aeae388537d2f73d581c30a5f8d0 |
In the absence of the constraint {{formula:23465510-dd31-4d80-9858-763f73a41d06}} , if we set {{formula:6cf4ece9-150e-48ab-a7fe-fa913f227b5f}} , then solving problem (REF ) via
IG-involved descent method (REF )
will reduce to the ordinary IG-absent method that solves problem (REF ).
This is a known BLO result (e.g. {{... | d | 53d2fb5307855a4c74efdce9957432b7 |
The loop model and the Potts model can be studied by means of representation theory of the affine Temperley-Lieb algebra {{cite:f03153d5c5b97c65708ee45d88070315b67735fb}}, {{cite:e135d436fc55247ea45198595e598834888b0eb3}}, {{cite:af21b0123545543d04c4054fdcbd10ccf627bfc4}}, and more precisely a quotient thereof, known a... | i | 9606b1751993744190acf622b86c9a15 |
Finally, we observe that among the three ArtLM variants, FT-Art-BERT has the best performance, surpassing the both smaller FT-Art-DistilBERT and the larger FT-Art-RoBERTa, although RoBERTa reports a superior performance than BERT on most of the NLP tasks {{cite:1f02d362375634e00c6da1d817bab8e86c64b149}}.
A possible cau... | r | 3244c0dc06da62b524c22348e403e44c |
In this work, we propose a stereo superpixel segmentation method with a decoupling mechanism of spatial information,
the framework is illustrated in Fig. REF .
In general, the proposed method can be divided into the following steps:
First, stereo image pairs with Lab color space are input
into fully convolutional netwo... | m | 1ab34b0b15f2235458ff01d42aae3e3e |
We focus on attribute features as input in this paper. An input instance is a vector {{formula:808cf4d9-057a-4f12-aab0-79a652f7666a}} where each entry {{formula:d909004d-7ba0-4345-aa56-57266c94bd2f}} denotes a raw feature (like age, occ., edu., etc.). If {{formula:c3f415fd-c56f-41c4-a8cd-f38ce1fa6c5c}} is a discrete... | m | d77ef169221754a0c264d98871cb06d6 |
Given the scale of {{formula:1fa2eeb6-8cf4-4543-9570-967a94a519f6}} , it is only at galactic length
scales or longer where the impact of the extended
GEOM is expected to be seen, and in {{cite:c9ae8531c1674eb9a574d887706113615dfc58a6}} we applied this
extension to the analysis of the motion of bodies at these
scales. U... | i | 9959c28a4205d6f1c39107e9aefc8588 |
We further report the quantitative results compared with the prior DG methods, as shown in Table REF .
We implement these approaches on the task of liver segmentation based on the official public codes with some necessary modifications.
When the source domains are BTCV, CHAOS, IRCAD and the unseen test domain is LITS, ... | m | 63cb912ea94565b6d9440b2e252069c4 |
Data science and machine learning have an increasingly prominent role in our science, as is evident from any recent particle physics conference and this Snowmass process. In recent years, machine learning techniques for detector and accelerator control {{cite:f4eb6548674df5e1df17368c14a74ef8535322a6}}, data simulation ... | i | 1ab47c88fdd7b87f412c14cbb28adaf0 |
The north-south asymmetries in velocity dispersions are detected in this work, though the differences between {{formula:d0b54a41-8cb0-42d4-b52f-8b43764ec791}} stars and {{formula:bb096046-eb34-4822-83d7-3c94d0eab50c}} stars are less pronounced than those for the mean velocities. It is interesting to find that the {{f... | d | 7aaf629803766d136b9d10d782c5058c |
At last, the critical assumptions of the present approach and its realization merit a discussion. Our methodology is based on the hierarchical distribution of the attached eddies, and the hypothesis that the characteristic velocity scales carried by the attached eddies with different wall-normal heights are identical w... | m | f4f338b2def3ed60357201fd1a196829 |
We compare our CT3D with state-of-the-art methods on both the KITTI test and val sets with 0.7 IoU threshold. For our test submission, all the released training data is used to train the model. Following {{cite:31489bcd7b68bab28c7ac373a7ceffce0678e69d}}, {{cite:1cd641a548f184e2fd4862fdab99c28e06f74b4f}}, {{cite:10a0f9... | r | eb4f0661f9467732e1eb250cff30818e |
where {{formula:323a87fe-da92-4875-9928-3754a034381f}} denotes the eddy viscosity. Although it is named as viscosity, {{formula:3a4b7b09-8914-4439-8731-8f69d02c7d5e}} is an artificial parameter different from the molecular viscosity in the original equations. Similar idea has been adopted to the large eddy simulation... | m | 50864a22027b7091d74bdb76322c99f7 |
Our proposed framework (Figure REF ) employs transfer learning {{cite:14369e600d59ced9468e78b81269e5fc3f5317ac}} of a pre-trained convolutional neural network (CNN) for fast adaptation to the Mel-spectrograms extracted from speech inputs. Transfer learning is an analysis technique in machine learning that used to solve... | m | f710da587f34e08afe7ecad84bd3e0c3 |
We refer to {{cite:3fcc87b7a3ad33f633f7a4f8a4f6408bd79047fc}} for the following definition and results. Given a Banach space {{formula:7f2f0827-452f-4a07-8140-708e974a3d12}} , set
{{formula:1fb5fc0b-a472-479a-88e9-2725dd7e4bbc}}
| r | ed665f20e6ddb45b89f1166a9029d9db |
We now consider the numerical performance of our proposed procedure. In sec:sims we provide simulation results for several examples, including varying levels of heteroskedasticity. We then apply our algorithm to three different flight data sets – the first is the data described in {{cite:5dd30a60ccf31b28f6f8b69df2314c7... | r | 541a44b0830e10260b10f54d070e0cda |
In recent years much attention has been given to the field of high dimensional expanders which are high dimensioanl analogues of expander graphs.
One extremely useful property of high dimensional expanders is that higher dimensional random walks (which are higher dimensional analogues of random walks on graphs) converg... | i | d635be68a7b3452c0705747e30058be9 |
The importance of these tools has been seen in many places, such as those used for signaling games{{cite:a44a0b77c0e12b8a65a4f912bb0d8ff2dc90a604}}, population dynamics and biomolecular networks, mesh network topologies{{cite:ca09e199c02f5e9e257f2a2df1d5358e6cda1fb9}}, 3-D neural imaging reconstruction{{cite:0f808bc2e4... | i | 691f85b2ec2fa42ff2184f31b527b70f |
BH: the vanilla BH procedure {{cite:7388ca0cb45180661e8ba8ac7da3394ed11bc6b6}}
oracle: The oracle procedure {{formula:c01b9228-29e8-4833-9498-db44eb3bfaea}}
SABHA: Structure adaptive BH procedure with {{formula:e98c311e-8313-4afb-92ee-e27f398cab71}} , {{formula:870eae45-1b52-4051-9d29-fceef9ec02e4}} and stepwise co... | r | bcc9c389324b9930f5ae0b42a0dd8685 |
We make comparison with the latest document representation methods, which achieve strong performances on tasks like web search and question answering. 1) DPR {{cite:6a840cd9e1f02fd656ee11562af5b53ef6cbbd6d}}, where in-batch negative samples are used for the training of siamere BERT based document encoders. It achieves ... | m | 3a80390af9daa4e205784b6ae6f9677f |
The recent discovery of coherent elastic neutrino-nucleus scattering{{cite:6474a91fb37257029a7903b89250f26d33009c94}} (CE{{formula:77b7e549-a1df-4d98-8647-652e024c25d2}} NS) offers a new intriguing detection channel for SNe neutrinos. Driven by a Z-boson exchange, CE{{formula:01c4a903-53c3-4121-ac47-a8bae611dfb8}} NS i... | i | c5372c6d428adc61e9fe6918b3897229 |
Recently diffusion models have outperformed other generative models in the task of image generation {{cite:4cbf714c245eed47325752a09db8e78d06448674}}, {{cite:29f38a99110dd10eb5f3805f59ec2d3b487d24be}}. This is due to the ability of diffusion models to perform exact sampling from very complex distributions {{cite:06710b... | i | 7491cdbcccab9a87091017c09260440e |
In conclusion, selected tasks are on a completely novel dataset and are sensitive with respect to lexical and syntactic information. Yet, pre-trained Transformers seem not to be able to solve these tasks, although these Transformers are able to deal with
lexical and syntactic information {{cite:890cbeec5ded470e650112f2... | r | cc36d2d9ff713979e173fc2e3401fd07 |
HeadPosr is compared to state-of-art-methods as shown in Table REF and REF . These compared networks are as follows. 3DDFA {{cite:8a85809f02a9ba8450747fb2153f61e37c1fec8c}} utilizes CNNs to correspond the 3D model into an image. It is shown to work quite efficiently with occlusion scenes. KEPLER {{cite:438ddc9d0e3b704... | r | f97d05573e8bae927aaaaa18f4b26a94 |
for all {{formula:474fb044-0b98-44e6-b5e7-7f24fe93f81a}} and {{formula:e44d169e-4194-4f30-a2e5-2dee5ad9d582}} , where the notation {{formula:0422fc53-5139-4534-a109-8f61e00511b2}} is used to
denote the ball of radius {{formula:48a25c64-1b19-4728-98f2-bb2abe261adc}} and centered at the point {{formula:f02ba8e8-250c-4... | i | 72632cffb5f0e80cf253fe00ef710796 |
The ratio of the theoretical predictions to data based on jetphox at NLO with different PDF sets, including MMHT14 {{cite:06e93ebab647abfe41e13ff8e1fe3b5f1f621597}}, CT14 {{cite:8ce537cc149864cc8e82b08d941471c70e61aac2}}, and HERAPDF2.0 {{cite:58740988448079b827df0f3f4b49557688689ee9}} together with NNPDF3.0, are show... | r | 770a9616846cd104661e5a96a22878e0 |
The canonical form proposed in ref. {{cite:4bb5bdce6d9abc1c8326d2f41f9e16ac03f30f7f}} not only implied an epsilon-factorized form for the differential equations of eq. (REF ), but further imply that the distinct differential equations for each kinematic variable are joined in one differential of dlog-form {{formula:640... | d | ad270563944263115e33c38f6082eda4 |
In classical PINN, a second-order optimizer, L-BFGS-B, is used after the Adam optimizer to speed up the PINN convergence, thus requiring considerably fewer iterations {{cite:d5e5e98db0975bc939f89af1e32137977aafe001}}, {{cite:f3aec1d8ed96af6036d3e9076d60c904ad025239}}. In classical PINN, L-BFGS-B is not used from the st... | r | 641f5787ec9cfa92bc83ae47c4ec0407 |
Approaches to “information augmentation" can include customized data augmentation, as well as transfer learning from related data domains. For this category, we choose MixUp {{cite:6fcec70c39dc0b54ac968f055faeed2f99a6f576}} and Balanced-MixUp {{cite:770982933d810f18deaf356ace54a430532fc1ea}}. MixUp is an augmentation t... | m | 2f18bd0a1799fd4eecb8f102a6db42bf |
{{table:09906beb-6535-4f24-b8db-357803c948ed}}{{table:1cf5e844-d41d-486e-8249-fb5bc88f43c6}}{{table:48356abe-4f20-40b2-a205-6c0cb86489ed}}{{table:8b8fed3d-9757-4627-b608-63b01249edd7}}{{figure:3b36c442-a9c2-4da2-a939-5fbade123e90}}{{figure:fa58d29c-c050-40b0-a21f-8ac9206606e4}}
Additional Experimental Materials
This... | d | b9ffdb14b7230380583503745d28c883 |
Proposition 1.2 (Propositions 1-2, {{cite:42886aff56b0aa6aa526461937fd9ab65c8836b3}})
Let {{formula:b18d3a65-d57e-489f-9707-397d2553e2f7}} be a cycle set. Then the pair {{formula:74ea8062-13a3-422f-9893-f1b9c1b539cc}} , where {{formula:8cf06615-0076-427b-a2f3-615cce4ae963}} , for all {{formula:bb947874-9b21-4dc1-972b... | r | 8eac4964a110efb17976282eecebe85f |
where {{formula:75935b66-9476-430c-acc2-236520dc0abe}} is a normalization constant, and {{formula:dff60e05-5e8c-4bd7-a626-944f14ed555d}} is the
weight function, and satisfies the following equation {{cite:20d67d6eaed0d36bda7b9bed723ac5de4927e02c}}
{{formula:485d3c02-8c4a-457a-814b-4e066eef3037}}
| m | 80d99c75f76ca8796bde88ad498b4c02 |
Replicating real physical spaces in their full fidelity in a digital form is a longstanding goal across multiple areas in science and engineering.
Digitizing real environments has many future use cases, such as virtual telepresence.
The combination of replicas of real environments with powerful
simulators such as AI Ha... | i | 55bea2d55ede8570975b58cf1da5a161 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.