text stringlengths 54 548k | label stringclasses 4
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The input parameters are taken as
{{formula:16650a28-4f42-4432-a7af-48caea761463}} and {{formula:f4e33755-cd0a-4241-b161-b513af1cfee4}} {{cite:9e6e6f8ceb883bb50f931f7f4d7187bc934ea5c3}}, {{cite:667039e3796cd381c4e557e07933cda114283ae7}}, {{cite:6c26b63f2199955a7a34138e581b3d16429bcbf2}},
and {{formula:9bfcbd62-c1a2-4... | d | 33d5ae03299b25b8bacd921bc12caa60 |
For all these SOTA methods, we use the public available official implementations to get the temporal grounding results. The results of the proposed test-iid and test-ood sets on two datasets come from the same model finetuned on the val set. For more fair comparisons, we have unified the feature representations of the ... | m | b14070c0ae83fc00af6d57f20f8850e3 |
The phase behavior for the vapor-liquid transition in this model in various space dimensions
have been studied {{cite:b7449be78aff1f3091dd13632d19ae44ff5869e4}}, {{cite:e64ce19e69e08e30d75aa4ebdd02e647eca44782}}, {{cite:cb50c90167f471f44729307665e0340c2552be54}} via Monte Carlo simulations {{cite:96ee47139565a279487fa6... | m | 3df53b683bb35c2f6f166937f76a8625 |
Existing studies from relevant literature apply various lightweight heuristics {{cite:e580fada600d6aa2dd9a3ebfaee3a4c89d344be2}} and query reformulation strategies {{cite:d61e748d43da063f91dc179218041c9eac9c5e82}}, {{cite:0cca3c05733ce3253603e38190fec71ae2e6e669}}, {{cite:b31eec18a8c8cad8c5f1421a8ee6168183f661a1}}, {{c... | i | 244ea5b4062d9220b1a8a6b163713dd3 |
In Fig. REF , we observe that LiRE{{formula:739f2a05-6765-44fc-a3df-132c8846f889}} OMP always improves support recovery over LASSO (except for the white region where the support is never fully recovered, with and without LiRE) and is also significantly faster since LASSO solver's complexity is quadratic or cubic in {{f... | d | afa7b45d760ebb34e619c8931d731bbf |
blackGiven a scene point cloud, we first use the backbone network PointNet++ {{cite:b037705a82da54abcaf882d0a2e02e2c3b211547}} to encode features, then simultaneously attach three parallel decoders: instance segmentation, 6-DoF grasp pose, and collision detection. These three heads respectively output predicted point-w... | m | 3db72eb61ca6f8ca945461ec296ab9f7 |
In addition, because the precoding/combining matrices are computed only once per coherence block, the complexity difference between MF and ZF/MMSE is relatively small (the bulk of the complexity comes from FFTs and matrix-vector multiplications performed on a per-symbol basis {{cite:d32ee6a9074f644b81536db3fc802e0b6bd... | m | def0dcc7fcfb0e008549ff8516b75c94 |
helps us to estimate critical exponent as {{formula:ee2e2217-fdbf-4834-b828-17e10cac741e}} . This value is again very close to {{formula:5c33feb8-2429-43f1-be55-2cf560df5c0b}} of 2D equilibrium Ising model within errors {{cite:9af7f80939380d103e4ffc5790cfc8dd7577e09b}}, {{cite:b8ed9a57b51dcd62a424c46f67dfb0cfffebff1f... | r | bbfd95fa509a6540d6003a2d5d770d12 |
Nyström method {{cite:68e6be51ab3e1ddff3bd7c3a7913977faca400fb}} aims to calculate a low-rank approximation for a Gram matrix. For Transformers, the self-attention matrix can be viewed as a Gram matrix {{formula:82cfa8b7-6aff-4118-a23b-2b4ccbec4705}} with a Gaussian kernel {{formula:33c3999d-a015-4c1a-80c6-aaee0360ed5... | m | 443b4bd0bcab8f3f98ee9551828b66a7 |
Are synthetic data rewards distributed to parties and their downstream ML task performances commensurate to their contributions?
We firstly assess whether our CGM framework can distribute synthetic data points {{formula:dcb8ad04-6e7b-4510-8c79-0687651779ee}} to each party {{formula:9ea867ef-0a8d-44fa-bf13-d2d6184cce7e... | d | 437e8d81d1a9cb8edb985a5357202a8f |
To test the generation effect of our proposed model when cross-database, we conduct experiments on the LRW dataset {{cite:198e563bc36b506976d2a576384d4bf355724111}}. The example of generated frames can be seen in Fig. REF . Our proposed model and CRAN {{cite:bb06865f30dc6bc2f3ee431c36a27171d13c462f}} are trained on the... | r | 8de6e292fbc3586c43d6de7a1e50ce11 |
We briefly outline the overall architecture of our model and then delve deeper into its individual components. Figure REF illustrates the VLC-BERT pipeline. Given an image with corresponding image regions {{formula:e7ecd9dc-35ba-4501-9726-597c903ef765}} precomputed using Fast RCNN {{cite:311f01a4714e79570b77920537b7e... | m | 66735fc2eded163a38fce169d1574cee |
This approach has been studied in various previous works {{cite:0fc5195f1380ba38cf30a0563332987382c65568}}, {{cite:52104f3e498318738374915ff4f46b75e2b845ce}}, {{cite:c2c6dab6565100d7bff871e158021076817bf1a2}}, {{cite:61ac21b717bb0b2c2ab4fa326e82296ab0895198}}, {{cite:89a524917b34cbd4dbee88e37a4bd13b80d98db1}}, {{cite:e... | i | decd9595707acb1504db28513e07c71b |
A physical object that can cause collisions. In adversarial machine learning, there are physical objects that can trick classifiers and object detectors into outputting attacker-desired decisions, such as a `Stop' sign with stickers that forces a classifier to output `Speed Limit' {{cite:b2c216364f3ce5d2363aedf258d0ee6... | d | 430639565ecc3014de05bb4ce77d20e5 |
The Stanford Online Products (SOP) dataset {{cite:4838599a6f1811f1ece31a4e1db76fd8b39b52a2}} does not contain ground-truth pose information for the object images. As detailed in Sec. REF , we obtain the pseudo ground-truth geometry information using LoFTR {{cite:d9f9fe0de0c6244f8c50369510f923b3651eaa02}} for matching a... | r | ee68dd4b5a639dea1e318a30ac2dc309 |
Relativistic quantum field theory is the adequate theoretical framework
to formulate the commonly accepted theory of the fundamental interactions,
the Standard Model of the strong and the
electroweak interactions {{cite:b33fe62cef9b38ab5e6c8db7d2d915348ab2f8e2}}, {{cite:a4e61ea4f14145285e6ec28b7432bcb172de9c83}}, {{cit... | i | 59b8d8f67e3082b504575cb9f3d6ab24 |
Representing filaments using regularized singularities is not a novel idea, as it was first employed for numerical purposes in the immersed boundary (IB) method of the early 1970s to model the flow patters inside the heart {{cite:f6358a90aaa5f123088c4c563c81785aea861c06}}. Later variations, including the force coupling... | i | aa8e8e673b7d576666996f86e21517b3 |
Various oscillation modes of neutron stars are gaining much astrophysical interest because they can provide various information on the stellar properties {{cite:3f23b857acbfaeec2aff19a5a0df96b85ca18259}}, {{cite:bd597d51960ef06fdc4e7093731bacb54a70f7a9}}. For example, the {{formula:5e889142-5cdf-4584-a5d0-0420e51e52a6}... | i | 852657637d3414b45a5f2c1646d9abd9 |
Strength of augmentation: In most exemplar-based SSL methods, augmentation plays an important role since the main supervision signal is that the augmentation should not change the embedding much. Hence, recent methods, e.g., MoCo v2, SimCLR, and BYOL, use strong augmentations. We believe such aggressive augmentations o... | m | f5c0402ef680c5fbb604a991c0b13a39 |
We conduct several experiments to improve George's {{cite:992c81906ebc094e75db68579c101d5961817600}} architecture. We have found that the output channels in our architecture do not give much improvement. Firstly, we add GDN block in encoder and decoder modules, which substantially improves PSNR and SSIM. Secondly, we ... | d | 77e9b9b8c5a270ab3e53a1651dce136d |
Lemma 40 (Multiplicative matrix Chernoff – Theorem 1.1 of {{cite:066eb94e0b5e1ec327046bc6ee935a64be1acc1e}})
Let {{formula:5aa433af-c3d9-4628-96fa-552121d5aed5}} be independent, non-negative definite {{formula:6a536abe-0eb4-4acd-82e7-cbfd7b6ebc45}} matrices with {{formula:baa21ce9-8ce6-49e3-9ce8-f53acaa3d32b}} alm... | r | 34afa588388dffbe12ee932b6e74bfc4 |
In order to solve the anomalies one encounters in calculating the effective action, one can apply the so-called conformal dilaton gravity (CDG) model{{cite:70207467fa54358740525b7dc911b225f8a9d57d}}, {{cite:c3f02777b3fdf07e41f3e10a2b7c3a361cbbf96d}}, {{cite:b23c0f6838bf37f9aeaf142614c353195ea5e5d7}}, {{cite:57395a5e454... | i | c8b5f7bec5c760ac5522a64ba948e2d4 |
In this section, the Ref-Net is compared with the SOTA methods,
including unsupervised methods (CAC {{cite:1a3d16210b1586b55bad43c9ae0f64e9c6bb75f4}}, ReDO {{cite:f9b323a9123dc93c5be71a2a8aacc94dbc31c261}}),
few-shot methods (SG-One {{cite:5f7cd70023976d4d4c806e124884f54d71b69be7}}, PANet {{cite:2b29bcd4c1eada88e8cfdb0... | m | 65e206a04be6a51dad030dff217fbcb1 |
Sections REF and Appendix REF highlight the predictive power of convolutional networks, which are capable of capturing spatial patterns in predictors, when they are applied to our data; we capitalise on their performance in our analyses in Section REF . A drawback of CNNs (and the recurrent neural networks we tried) ... | d | 2f3bf298e1720b90704cc16f20521266 |
To this end, we propose a novel pseudo supervision scheme, which
is leveraged to train the teacher-student network with distillation {{cite:8c9dbae092a16980a73610c39adae127df616856}}.
Specifically, the teacher network takes advantage of the effectiveness of unsupervised binocular depth estimation to produce accurate di... | i | 07e06035de8fdc63c0f0ad96f97b36fb |
Deep learning models have been widely used in UDA. Earlier methods rely on minimizing the discrepancy between the source and target distributions by proposing different loss functions, such as Maximum Mean Discrepancy (MMD) {{cite:8ae8f8a87e0d35e1a9cc2a5dfa8241d65697e20b}}, CORrelation ALignment {{cite:99f3f448960aa596... | i | 0d6263421b67009b116e980cb4202fef |
We compare to pre-trained LM based methods that leverage the output probabilities of the LM to make predictions given the sentence known to express the fact. Two methods are considered: (i) LAMA {{cite:000bba5ebb696eeed1f3661cf0ff4e1b94ee1f4e}} leverages the input sentence without the tail entity to query the LMs, and ... | m | 9ec7dfcbd332f8a7f575ed429a7f8a3f |
We found that the best-fit model for this source was an SED with a two-component blackbody excess, with temperatures {{formula:bf382f12-da7c-4362-b501-59440d3c6762}} K and {{formula:2906f656-b65f-458c-9653-da8638169e3b}} K and a total {{formula:5316915b-3dc1-4b21-a12e-3dc3beeec353}} and {{formula:8394d7a4-1032-49d3-... | d | 532b143e81123a883ccf6c584a507f1d |
Our approach bridges the limitations of both PINNs (cannot be used when the structure of the DE is not fully known) {{cite:534d07da1e6918389aff0451cb77eea4f225fd42}} and UDEs (not robust to noise and requires lots of data) {{cite:c297c9c3311c0144259bb1b34e83c13af32bf892}}. To address this, we replace the hard constrain... | i | c0e780193e0e5cac24af3a686bde263e |
Another nice aspect of our results is that the multipole expansion in
biadjoint theory also matches that in the gauge and gravity theories,
for the wide class of solutions we have considered. This adds a
powerful weight to the observations made in
refs. {{cite:720bd16cf9e3bbbd13022eeb40c28c6ccf41391a}}, {{cite:714c86d7... | d | c9dd84d5fe8ef92ad45e41dfac5fea04 |
We have studied the role of nonlinear effects on tidally-excited gravity waves in the radiation zones of stars, primarily focussing on a new mechanism that could be important in stars possessing convective cores. Our work was partly motivated to study tides due to massive short-period hot Jupiters, which are observed t... | d | 0a29f56f4f5262bceb1fb2f6bc005f95 |
Table REF shows the mean and standard deviation over
different training partitions for two simple baselines, ESZSL{{cite:b696d6f47c66d1c2ed4c25ce237f8dfcaf6d512b}} and
SJE{{cite:0243d37711db3bf602779a4b6d3071a4b0687882}}, on two fine-grained (SUN{{cite:131022583240d6ac619bfe2ec5124422cd3cde14}} and CUB{{cite:2012d764b... | d | bfca87eb30c29dcd9c8c579fe4f806a3 |
Initialization: {{formula:50e4652f-9d01-45cc-92dd-d5c1529f87b8}}
{{formula:6ca67212-fb29-42b2-8b2c-6f123122b5a3}}
{{formula:0ac89c28-4c54-4c1e-b9f6-d4b34e723051}}
Generate {{formula:2c89dbd6-8d10-4697-9236-a8502dba11af}} training data samples at random
Initialize weights {{formula:0a739f15-d05c-45e4-a9e8-3da2c39db... | m | 6f123fbe44fbfc9fb818956a9dc18366 |
Hybrid vs. Conventional Attention. The hybrid attention module (HAM) in the proposed DA-DETR plays an important role in achieving effective cross-domain alignment, where the visualization of attention generated by HAM is illustrated in Fig.REF . To demonstrate how HAM helps to mitigate cross-domain gaps in an effective... | d | 6d6da2861d18b2d29d87fad84d9fff6e |
where the intrinsic H{{formula:772afa82-72f2-4773-89d2-6ce6f4039383}} flux is in units of {{formula:915a03d1-4321-4f41-8ad9-da0f0c045ddd}} erg cm{{formula:62dfba8a-50c7-484c-b9c9-e6941c265501}} s{{formula:4dffbef8-6fd8-4ae8-b0cb-78681decbc25}} and {{formula:bd40ed91-d263-42b4-808e-f3eac35f5fb7}} in kpc.
The intrin... | d | c33dc93a8f34d4b4c2a76652859b3573 |
Our proposed featurization for persistence modules is rather naive, but well
adapted to the use of lattice convolutions as a data processing method. The
lattice convolutional neural network shows promise as a method for classifying
features arising from a multiparameter persistence module. The algebraic
perspective on ... | d | 0834989b0073c945b0bb52ee3bcd21a6 |
Value-based methods learn a value function to estimate {{formula:fe74db94-8738-423d-8cea-927366b7d6b1}} given the current state, {{formula:bf7b2852-f997-4163-8b1a-ffb7a84b46af}} (state-value) {{cite:1393f70aa1ef9bd8a51ce8c22725a44512f90f60}}, or the current state and a potential action {{formula:7b588d71-c883-4415-b1... | m | 964828b35147f845ab4786a45cf127ed |
Cleaning up the training datasets is a necessary thing for the larger models to learn good generalization power instead of the labeling errors and clean validation sets can verify this power. However, the hidden benefits of mislabeled samples cannot be overlooked. Although these samples have wrong labels, they contribu... | d | 33fba247220b15b573b182351971a13e |
Another branch of explanation methods for CNN models is gradient-based. Those methods can also
be used to explain ViT models. As baselines to be compared with ViT-CX, we choose three popular methods, namely Grad-CAM {{cite:2657aad718d4f222ed8c09b44231b56dc2c8ce16}}, Integrated-Grad {{cite:f38e7b9ef3fbbfa91272177aabde17... | m | f107a3728cb7cf1a3d3c4f87a94bfc31 |
paragraph40em-1emA note on augmentations.
In this paper we have solely used the hyperparameters that come with the methods, and as such, have been optimised by the computer vision community for the last few years on IN-1k.
Augmentations play an important role for self-supervised learning {{cite:7a205240ccec8ee632ec14b4... | r | 8676fd6812f96ebac5a492a4e15b8e9c |
Finally, as noted in section vision transformers (ViTs) have emerged as promising architectures for visual representation learning. Figure REF compares recent ViT-based methods against ReLICv2 using a variety of larger ResNet architectures. Notably, ReLICv2 outperforms DINO {{cite:960272a3c012a1f77677d260481a7ff860f5... | d | 994faf5d42f8a268419ffe9f5d0cee47 |
A systematic comparison of fusion excitation functions have been carried out for
systems involving the target nucleus {{formula:151d3d63-348d-463a-8922-df41b2acd490}} Ho and different projectiles and is
shown in Fig. 8. The systems involving {{formula:4f38c641-a501-463d-9fa6-0bf2c0c29b5b}} Ho target nucleus, for which ... | d | e99d55b98cb6fd98d95b8ab558f95e87 |
TransUNet {{cite:f95d4201e4c82feeffd24b3ada0bba524e3fc7e4}} incorporated the CNN-Transformer network. The CNN layers capture spatial information, and Transformer responsible for global feature. It has u-shape, where extracted self-attention features maps upsampled to be merged with varying CNN features skipped from the... | m | 4a9344853971ad6a380d01ee68f80452 |
We will derive integrals as indicated in the abstract in terms of special functions. Some special cases of these integrals have been reported in Gradshteyn and Ryzhik {{cite:e5a59a0532fcc7f3121662f58939b770f04af1fc}}. In 1867 David Bierens de Haan {{cite:e236f1746b8ecedb73878258fd5d85f74cc56310}} derived hyperbolic int... | i | ccbcf6d8e6f634f1eae0400e40d46224 |
The NPR is a significant diagnostic tool to characterize the localization transition. The localized (extended) phases are characterized by {{formula:8ff0f679-3f7b-4efc-bf1c-de7fb6ded6e0}} ({{formula:1553b136-75a9-4711-a2e8-40502459c6b3}} ) in the large {{formula:755ca8aa-4402-4633-b74b-daceb954331f}} limit {{cite:2a6... | m | d438ef4c860beba9b652aa0e5f879a69 |
This work studies to what extent voice can hint face geometry motivated by recent studies on voice-face matching and cross-modal learning {{cite:595ef0793a6cc897873d03ce39e499b83ace8ac0}}, {{cite:60f2505e8b7aa53a0fb2baee2bef419ec679a7ff}}, {{cite:3f24578ddd74fcdfba5d39b3effeb8b9053dc911}}.
Many physiological attributes... | i | 4b8eba88c978f360ad747b075410df68 |
Our luminosity functions can be compared directly with the best fit
models in the same redshift ranges derived by {{cite:6e631beb328acfb7846c13fa98b38194280f160d}} and
{{cite:1a81cbbbc087976edb7fdc7c03e7df237c744e66}} in Fig. REF . As {{cite:6e631beb328acfb7846c13fa98b38194280f160d}} and
{{cite:1a81cbbbc087976edb7fdc7c... | d | 65013ac911c50a4f1e1d1ca2aa0162f7 |
For an RW starting from an initial node {{formula:83be59ce-f417-438d-aa0b-153b0f44e7e3}} , the
first return (FR) time {{formula:569b289d-858f-45c1-a2bc-47351e992a89}}
is the first time at which the RW
returns to {{formula:8a6a6a0c-6358-40e3-9519-b5880009b3a2}}
{{cite:3d7518f7fa2977bf9fbfb61d531fd4d8c0eec99f}}.
The fi... | i | 2f09b65291623634f2c693e753c5ac10 |
Besides, we compare the inference speed in terms of inference FPS in Tab. REF . Note that this metric has a bias. It is more favorable for methods that use a high input frame rate (, 25 in R-C3D {{cite:9b9db5b110cd087a59c67fdd95a8a7a10d98166c}}). Therefore we also report the speedup ratio, the ratio of inference FPS to... | m | facc83a9ec6a4c32ef0817c51f66d1c2 |
In three dimensions, we perform similar measurements to obtain a lower bound for the fidelity of our state with respect to {{formula:6261e888-1d15-4956-a70f-6ae57f579fa3}} . By measuring the correlations in the standard and DFT bases, we certify genuine three-dimensional entanglement by obtaining {{formula:3243ac56-36e... | r | df0f6f4e20474edeb21ff333954ecfcb |
There are caveats to our study of SMM-Cs.
We have only demonstrated SMM-Cs on robotic domains with smooth dynamics, while BBNNs have been demonstrated as useful model parameterizations on simulated contact-rich domains {{cite:e8a8dfcdd037a4910c7372d944569382e1776447}}, {{cite:037383ab70418520643f8849dcc7b42e1ddcf2ea}},... | d | dc6cb6e3d815fb54e11eed6a4fde7b79 |
Recently, orthogonal time frequency space (OTFS) modulation has been proposed in {{cite:ae5dfad3af053742c5da5f2bfca5dfdf94c28a28}}, showing significant advantages over OFDM in high-mobility environments. OTFS places information symbols in the delay-Doppler (DD) domain to capture the channel geometry that models mobile ... | i | 18fa4c67fe23b7d02027d32ceeb61518 |
The results depend far more on specifications for covariance matrices, and how a large
covariance matrix is estimated from a relatively small ensemble of model runs.
We used likelihood and Bayesian approaches for estimation of covariance
parameters; a variety of alternative approaches exist in the literature
{{cite:169... | d | 9bb3fdfa4c24f201476f786e0d49ff82 |
According to BIMA observations {{cite:cb61f2a9c91ce1238d8dc38929c9cd06f050d498}}, glycolaldehyde is greatly extended in comparison to the ethyl cyanide and dimethyl ether, which are largely confined to the Large Molecule Heimat source (LMH) {{cite:aec109eb89aacae8d4f0efb6eacdaed0b604a2ee}}, {{cite:24d0ae6b4d85c6589da6d... | r | 6104a7eba4ac576a8d6309ad8a4ec44a |
Our quantum algorithm prepares a pure state {{formula:63c9bf1c-a585-494e-acff-1b9195f207d4}} , a purification of {{formula:ba48510f-fd26-4965-a708-835b965b4d5b}} , and {{formula:7ac6c02f-6b65-419e-aa8b-0666b12d6bb9}} is obtained by discarding or tracing out {{formula:12397b80-d60a-4bd4-b912-96b458aff812}} .
Working wi... | r | 581fa14fcbfa5dabd7582e3905f3b18a |
Our work focuses on policy improvement methods, an algorithmic framework that was first introduced by {{cite:cf998e383dab879908746479dedb5d7a50f8206c}} in Conservative Policy Iteration (CPI). CPI guarantees policy improvement by considering a mixture between the current and greedy policies at every update. The policy i... | m | 1c4daeeaac2057b91f828045b8c1800e |
Creating large-scale medical image datasets for training neural networks is a major obstacle due to the complexity of data acquisition, expensive annotations, and privacy concerns {{cite:f0f7d8eb49f7c02f7a0e93042def8256cd79dd87}}, {{cite:2387ce538bc8a2acb72cbfdc6d40107b848fd20b}}.
To alleviate these challenges, a conve... | i | 4628bf6a8d78f71f4b5307b558d3bd32 |
Inspired by Lin and Och {{cite:a9bef9c9f8a9ec911557c086a8860ace8ac7322a}}, we proposed an LCS based post-editing algorithmExecutable source code is available at
https://github.com/SourcecodeSharing/CWSpostediting
(seen in Algorithm REF ) to alleviate the negative impact to CWS.
In the algorithm, we define an extended ... | m | 333b7be288bc1599077ba2915c11502a |
To evaluate the performance of the proposed method, we examine the rooted mean squared error (RMSE) of the changepoint estimate, the empirical coverage of the credible interval (CI), and the length of CI. For each setting of spatial correlation and SNR, we run 100 simulations. Different locations, changepoints and func... | r | b0bfad3295920328939558a53cfa451d |
In the future, besides improvements on the sub-PeV Galactic diffuse emission modeling and
measurements, ALPs – and more in general exotic physics – searches will
benefit from observations at higher latitude, where also the Galactic (diffuse and source) emission is suppressed.
Interestingly, Tibet AS{{formula:0463314a-0... | d | 715c547bf32817d868e32b5de6dfb47a |
If the dimensionalities of input and output vectors do not match, the vectors are padded with zeros during inference, or small scale Gaussian noise during training, to encourage the network to ignore the additional padding dimensions, as done in {{cite:c47ffb681bc1deeb776c1da9754db0a5a7d556a8}}.
| m | 0246b799455845fe19b92db1f6e1cf7d |
Observe that if {{formula:0f819c1c-4bca-4153-8f76-26a8adfe906e}} is a finite Galois extension of {{formula:7e8b3996-6d82-434d-bfda-9be706348c8c}} containing {{formula:3290cbae-fcd6-4cb2-9982-faa5c89db320}} , it then follows from the transitivity of the norm maps that the surjectivity of {{formula:d2502574-abe4-46ab-8... | r | e6fbfda598417d46d7d4faa7f15b1040 |
The bare cross section including final-state radiation takes the form {{cite:d2c367e67353bbd72242ac89e25dba37c26c9b36}}, {{cite:3772081d62d6dbf66dfcb9361a0513b38df207d2}}, {{cite:a76a796fa666b2df524320d8e6886ac4af46b28e}}, {{cite:1048c45561df7af56c359d834b691e15041484db}}
{{formula:823d4634-9ffe-4f58-a573-98e86c1434e8}... | d | 94cd6a6d059dffc93561923bf45292bc |
In order to verify the effectiveness of the proposed MDRN, we compare it with more than 10 classic SID methods, including BM3D {{cite:981e1536c1ae9c28836746bc99607ec6a1505c22}}, RED30 {{cite:597e78e49738831dfa449f330f6a9a8f0ab92400}}, TNRD {{cite:d28a8116d50c2ca1bf8249636160d3aed8b91ec4}}, IRCNN {{cite:79d9eb8f5248e23b... | m | cb8ac68de66b0716044619804b2d683d |
Evolutionary governing equations via VO-RL derivatives of constants: A particularly interesting property of fractional-order Riemann-Liouville operators stems out of their behavior when applied to the fixed-order derivative of a constant. It is found that this fractional order derivative is not equal to zero, unless th... | m | 16254a78f0b841086c973575db8221de |
Results for Normal to Foggy Weather. Differences in weather conditions can significantly affect visual data. In many applications (i.e., autonomous driving), the object detector needs to perform well in all conditions {{cite:93f27e8df888ad2e60b931e40859be7e89c38880}}. Here we evaluate the effectiveness of our SNR and d... | r | 0fb9d7a7eb4ca059df9bf7111a7fd801 |
Let {{formula:ea3a9478-4c1d-4121-a048-26d35d4a2b53}} be the Banach space consisting of continuous functions from {{formula:3230c9af-4895-4ac7-8cbc-ce77e070b86a}} to {{formula:94fa31e5-b701-4480-8401-2f43944f87b9}} with the maximum norm. The following proposition is due to Henry {{cite:d47d704da2619ba4916ec3f3c952dbf... | r | 4158f7a7c0004c8a6b15fad42126b699 |
Our model of Indirect Active Learning (Figure REF ) assumes that the relationship between {{formula:781af1fe-256d-485c-816e-01cd9e79f673}} and {{formula:6de12eaf-ad49-4f92-be4a-e636d2f3efdb}} is unconfounded given {{formula:ba97445f-7307-4150-8fbf-f69171a9b6a8}} , i.e., that {{formula:aacfb189-fa4d-4eff-82ba-e3701c02... | m | 3039481c0ba5fb1b72d07d9006d6a0c2 |
Thus, Weierstrass Theorem {{cite:e76b2b0c36af55844a6eba10d268dcb839faa466}} assures that (REF ) and () both have a solution. As a consequence, the splitting method above is well-defined and an infinite iterative sequence {{formula:5847539c-b8c4-418e-a8c2-7750863d816d}} is generated. Moreover, {{formula:2e7be240-0990-... | m | 3cb154ab19617af6daaf62e98870a95d |
Physics-informed deep learning methods {{cite:ba577e6603e4c781f4bdd22a83fb639695893fb1}}, {{cite:68a2614be2acc8a85c6868b9c1dda24b7ce0d6cd}}, {{cite:e9ed77292d61f3e36676c57a219ec4e9e5f952bc}}, {{cite:64be13dd4a5119398dbffea0e1aaba068e50efa3}}, {{cite:ab1f0ef5cfb31abf5205ea17ddfe05640d2bcc85}}, {{cite:40be6152d516370190a... | i | 2072dc40969f1cad43fbbae9c61c0c91 |
In this study, we present two deep learning-based methods to align audio to phones and achieve comparable performance against several existing forced alignment tools. These models can be combined as a pipeline to bootstrap phone labels from naturalistic audio data in the wild, which are massive but remain under-exploit... | d | ab4fd189e7cefb9d275882a5d145bc81 |
An overview of the results in Tables REF and REF convey that the proposed CIE-Net, employed within the incremental instance segmentation framework, shows neat performance improvement over standard models such as Mask Scoring R-CNN {{cite:90dd5e72cd96b89fb7051814f90bfe0d687f88ec}}, Mask R-CNN {{cite:75e1e6f9bc857234a2... | d | ceced6d4ab27ef100724aa0cf8261864 |
Pre-requisites:
Without loss of generality, we focus on the SOTA multimodal contrastive learning model, VATT {{cite:abd108d0faac79298e5f3131eab3b6767ffe48de}}, as our default subject of study; and we follow its modality-agnostic single-backbone setting due to its compelling performance-efficiency trade-off, and due to... | m | 0cd9cd541287a6a163c6f6200235e9e3 |
Creutz applied the lattice QCD simulations with the Wilson loop to describe the interquark potential between a
quark and an antiquark {{cite:91a673837c98c74c264f3f58f42bd4e85f4d74f3}} after that
a large amount of effort has been devoted in lattice QCD to
study the multiquark force {{cite:91a673837c98c74c264f3f58f42bd4e... | i | 0fdabb4f544218ea48254faf41d96f46 |
Instead of using only {{formula:e7594918-5424-4c7a-a0f3-54b1942336bb}} and {{formula:afa56060-16f9-4f29-8104-24c5dfb80125}} orbitals for the even- and odd parity layers, one can also think of using {{formula:12425c30-0b54-4c9b-93ac-d8804968ec56}} and {{formula:2e7d6e44-5696-407b-8257-70f9162771a5}} orbitals, respec... | d | 9f68ca3fcf6bebdf1245a2d33b0725e2 |
Firstly, we consider mmWave point-to-point MIMO systems.
The transmitter has {{formula:f8f7278f-637e-44d6-b3de-ce5de2e9de1e}} TAs and the receiver has {{formula:391e65ea-436f-4b73-b6bf-945b25a9df57}} RAs.
The number of RF chains at the BS is set to {{formula:dcd0533d-67d8-4400-b3f4-aa4c7e69b61b}} .
The number of data... | r | a2f31068c3048cefc3c2e52113ebd9f2 |
Non-parametric algorithms feature a key quantity known as scan statistic, for example CUSUM statistic of {{cite:37ad07c57e0649a4371d073f65eed06d61739439}}, which is required to `scan' the dataset to identify the change points. We propose a scan statistic based on influence functions proposed by {{cite:bb4c7258f011089e... | r | 15d6a599b8d5eff34f57d48ff6d4ef50 |
Let {{formula:9e50d849-2403-44e2-aaff-4692a0498871}} be the sequence generated by the algorithm (REF ) with the primal step-size {{formula:ab245868-735a-4688-97e6-bc2638171335}} .
Following {{cite:c9f1c07600d91c542065afcbe541c5446587aef9}}, the update (REF ) satisfies that
{{formula:04b407b1-d7da-4684-88b9-760c7a30077... | d | c4caa3c54b57fe94b1623da314432b6f |
Attention Model {{cite:e678332a349bd7c30c0c5f75d81a8bf738dcd1e6}}, {{cite:fa579b4c01fb7e665a4556dad469f6c241d08c92}} was first introduced in machine translation task and the attention weights were later widely used in natural language processing tasks as explanations in neural networks {{cite:09ca82bfee670d39c733535789... | d | 61e3c26a5b0863c158f03a6b32c174b9 |
As shown in {{cite:7ec696b931add09f37ad6ce7b32238769e5758ad}}, {{cite:d0063e9675000b6a365264eda3c26f145e31dc20}}, the norm of the Moreau envelope — {{formula:2f1c11c1-d311-46d6-90d6-4e984c6713ac}} — defines an alternative stationarity measure for problem (REF ) that is equivalent to the natural residual if {{formula:9... | r | e3bf2f2159ff2a0be12262a4a32d156c |
A number of authors have used this technique {{cite:fc7a1e70a1e6df34b7dd03f28bd69faab8073a27}} and tentatively found results consistent with those of {{cite:a3170010ad80cec831c836f373abef355814fd0a}}, i.e., that in giant elliptical galaxies the mass to light ratio is such that one must have an IMF that produces less li... | m | 515b3f3110f18c0de3910eb578c44a0f |
ReLICv2 demonstrates for the first time that representations learned without access to labels can consistently outperform a strong, supervised baseline on ImageNet. In terms of a like-for-like comparison using ResNet50 encoders, ReLICv2 represents a substantial improvement over current state-of-art.
This is a direct co... | d | 88a2c77a33e915fe0e21d55345e2b243 |
However, these methods have limitations in extending to other applications such as game graphics or metaverse. For such tasks, a canonical view of a target should be known, whether it is given or predicted by the model. In reality, a facial image given by a user is not always in frontal view, but rather from an arbitra... | i | 48141eaf45f91fc776933d36f9f83a00 |
Vision Transformer (ViT) {{cite:e1b1a7310f1dcf82e9828ac21b116cd17fa55428}} architectures have recently gained traction as an effective alternative to CNNs for computer vision tasks {{cite:1753e644f4369512c4dcc797287e90cd9131bcac}}, achieving impressive performance despite fewer inductive biases.
With their in-built sel... | i | c7541af1cbef6a81c645b3f7abf40c4d |
Our experiments for texture recognition were performed using the Materials in Context (MINC) Database {{cite:f87693424b7e3fa6e3348df03b039a09b696ecaa}}. Specifically, its subset MINC-2500 is adopted. MINC-2500 contains 57,500 images of 23 classes. Each class contains 2,500 images. We use the train-validation-test split... | r | 63d31a7c93a44d6dd2d5f7fb6fd9dc3b |
Despite the fact that Fields' results have achieved the so-called uniform reduction in
the sense of Olver {{cite:4b742516f9a1ab85449394069316f64b5ba2079c}}, they are found to be too complicated for any practical
application; see, e.g., Erdélyi {{cite:a7d584e7ac9608f8319d0b75b582cd280a797ba9}}, Olver {{cite:4b742516f9a1... | m | 25974fcc3337fb80ebc0681b2112b86e |
We compare the pre-trained models of GraphSage/GAT
in {{cite:43fdbaec0eae8dfb89d4fd50dde81dfc6f7c5934}} with GraphSage/GAT of the same architecture (5
layers, 300 dimensional hidden units and global mean pooling) trained in
2STG+.
The pre-trained models are fine-tuned on the datasets.
The mean and standard deviation of... | m | 4240ba0633b2a17311f32340783fdb85 |
We recall some basic notions, introduced in {{cite:c6c718178a88df1f4e65e1b9473d68afa01ed23b}}, {{cite:2f9c23017a2dbae12db784bd11f45cfe929f0836}}, {{cite:5961aed91a7abc81baa51985abc9274a6ad24274}} related to Hom-algebras and while dealing of any binary operation we will use juxtaposition in order to reduce the
number of... | r | dc081de1ea2196df1b969a82b9f95899 |
The seed binary is treated as two sink particles with a sink radius of {{formula:d188c16e-e87f-456a-a51c-8384799a6365}} ,
following {{cite:b9283f6fb9ddcf66eadb35c765141b52d6d5e63e}}.
The SPH particles are removed from the computational domain once they
fall into the sink radius of each seed or reach the outer boundary,... | m | d28f447c4db433b8493ea75c187bd133 |
There are several interesting avenues for generalizing our theory. The proposed inference as control method can be extended to the more general case of observer taking external actions in addition to the internal predictions, in order to maximize external rewards while minimizing both external action costs (e.g. moveme... | d | 1b37dc39ce43b69edfb5203f34f827ce |
The set {{formula:18db4856-6400-46f3-964f-35c17a11a9da}} is the (disjoint) union of two sets {{formula:62ed8839-d1c8-4761-9907-333723405a20}} and {{formula:86dccd22-3fce-4d02-b75e-3da54d6791a3}} , consisting of rank 0 elliptic curves with ordinary and supersingular reduction at {{formula:a1af83ca-b505-41b5-ad03-00a93... | r | 8f6e3cd574aa3a779347b7f1b801a296 |
Under genetic drift, the eventual fate of any allele is fixation or loss. Although drift may seem an additional complication, it also has an interesting effect, namely to allow access to parts of the fitness landscape that were inaccessible from a given state of a deterministic population {{cite:41d78f2a332c539ca213157... | d | 92267c62230e4995e22f161fed73b679 |
Bayesian optimization (BO) offers an efficient alternative when the tuning objective can be effectively modeled by a surrogate regression {{cite:c3aa28040bd5e6598603df5dc2bb940578b204a3}}, {{cite:10cfbdbe3434ef936d7eb51e8ce5893f13138f7f}}, or when one can take advantage of related tasks {{cite:645a9396dbf7b5bdc3a13751f... | i | b9248e602077b0d653680f953a0f0338 |
We have proposed, analyzed, and implemented methods that accelerate planning performance and optimize solutions. The key is to quickly compute bang-bang time-optimal controls using analytic solutions, to produce both metrics and steering methods. Although the study has been limited to RRTs, we expect it could enhance o... | d | 86fd9ca132161df36f024d0a662f7de6 |
where {{formula:8cdc1344-495a-410a-992d-4e0777f0d876}} is a polynomial in {{formula:9e28b9d4-9877-4e80-87af-707ae21f75e7}} . For fixed values of the constants of motion, the spectral curve, {{formula:1a52c7b0-6fd2-4481-b024-c0f845004f66}} , defines a Riemann surface. A link between the initial phase space and the Riem... | r | 2192d895452b3e4a08f35575cd130ed1 |
We use faster r-cnn {{cite:8f58806319c6825eb82ab6f00988db6abf65fad4}} and mask r-cnn {{cite:1b5f1a753bdf5878defe296a733fccd422540e5b}} as baselines for detecting graphical objects in annual reports. We use publicly available implementations of faster r-cnn {{cite:ee53abcfaec02d4c1927b612113316c0417e167c}} and mask r-cn... | m | 674925dbc3c34cf3e86049fbd62be179 |
Carrying double different flavors, the {{formula:a281f088-10fa-4268-8976-3f6be97d1ef7}} tetraquark states can not decay into a heavy quarkonium plus a light meson via annihilating a pair of heavy quark-antiquark. There is only one kind of two-meson strong decay threshold {{formula:88167817-5145-40d2-93dc-6592b0d93864}... | d | ea00453c19c24a119c4254c97274c71b |
Neural SDF {{formula:020599d8-bd74-4f66-8596-49eef4046cd1}}
maps a 3D location {{formula:a0ab8ecb-3081-48b9-9106-973c3143ffb0}} to an SDF value {{formula:c42195e6-2c08-47ff-a1df-c8096a1f1af9}} and a 256D local geometric feature descriptor {{formula:3db90ebe-baf9-48d2-897d-65a751a51689}} , as in recent works {{cite:8... | m | badc1c522243ed13d9ba11bdbf296846 |
Results: The quantitative comparisons between our proposed method and the others are presented in Tab. REF and Tab. REF . The first interesting observation is that the widely-used CRF Loss {{cite:00b802948937a5420a81e98e16db7d973b99fe8e}} achieves the worst performance than all other methods. The reason may be the CRF... | r | 5c5ddde4d62dbe1d5a2e3989f4c94e5c |
Previous results have modeled the spread of information in graphs and diseases in populations for either single-type graphs with general degree distributions {{cite:74841e7c294856157f3c0ad64678c8f00af9d04c}}, {{cite:d99813248ce272500c89ad7725315a53ceb1b5dd}}, {{cite:86f9d4333643654b6bfe8f3cdaa17136c39b2883}}, {{cite:a6... | d | 0b1a500b96679a8755babbfabb852c4a |
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