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We note the similarity of (REF ) to the approximate quadratic Hamiltonian of Fetter {{cite:ed405a25dd3ad8838d0b86db4917269b8151d697}}, {{cite:8f65cd48b0786c4089ef50f427e8bb7761d68959}}.
| d | 5b987ec01e862bd29a91fa9327dc2162 |
The bulk of the research on deep-learning super-resolution methods considers the use of a single LR image to produce an HR output {{cite:b2075be1a2d7afd4dac4ee7f755f05f8c9266a9f}}, {{cite:6fb47d68d0251e315a351f81b31b0b2e346124b7}}. In contrast, video super-resolution schemes exploit the temporal correlations in multipl... | i | 1f4c6296d73ab06a09b07ca78360fa45 |
Shots Aggregation Most prior work {{cite:1185c3ef79bf193de4412a760530f1b4e4ae6a59}}, {{cite:524910ea307f2fd2b61771d93e3dc61cb92bae50}}, {{cite:93f68ce706cbbab76b0dd15f37d18f4284420207}} simply average multi-shot support images to obtain a class prototype for detection head. However, this cannot fully exploit the littl... | i | 91a9cdb27c1a861e42aff6d91c9db57c |
Spanier {{cite:db33bda32b9cbe18586e2133dab07ac18a040993}} introduced a different topology on the fundamental group which has been called the whisker topology by Brodskiy et al. {{cite:61149024b9e0cf3f4f0b5967826acc7cfb0a973c}} and denoted by {{formula:f19d59c7-123c-48cd-b31e-b8b57eb67195}} . They showed that {{formula:... | i | 3363820684e46ac542f85dc2ac5fb6bd |
While there are a large number of alternative clustering methods available (such as STING {{cite:ad36327c6f8f7d08d279a9e919594cc95175ee81}}, BIRCH {{cite:296feb1ec0a82e653195e223441c420b7e42a961}}, CLIQUE {{cite:04d1d6226b9d2e1605eacf23ea97851058ebdf4a}}, WaveCluster {{cite:394cb1976a843016cde685d1b052236043c876ca}}, D... | d | bd201aa9df53c458447af84a54a58b47 |
Comparison to Slim: Table REF shows that {{formula:f582a040-e984-418a-be88-c1602e639959}} achieved notably increased accuracy compared to Slim on all the data sets. This suggests that dropping the L1-norm regularization as well as the non-negativity constraint on the learned weights is beneficial. Our analysis indica... | r | 4efdcecae093c28d33c32673864ed561 |
In this paper we explored synchronization of phase oscillators on hypergraphs with heterogeneous structures, generalizing the results in {{cite:346dd99d9a94e11d682a6de85cdd3a0c3da36347}}, {{cite:00a955a1eee53ee1bdcb495811277e81d86ff4b5}} to more complex scenarios. The mean-field approximation allowed us to predict the ... | d | 4fbd17cbe429e1a87be795356ea9a3ca |
Since the MNIST-360 dataset has no task boundaries, many methods that rely on task boundaries are inadequate for it, such as LWF {{cite:fa56e47f9eabda9fb58d3809064c260cd90fee53}}, oEWC {{cite:2690187fabeb1e5af54f41bee10e191e1913cf8b}}, SI {{cite:bc3d9631725c69a900d94a4c8dc991ec485dcdcd}}, GEM {{cite:0bab1d4ecd9b815128c... | m | 0eb659caa39edb3099129315e8bd994f |
Baselines: We have compared the retrieval and ranking performance of our system with some of the other state-of-the-art face image retrieval and ranking approaches including MARR {{cite:c4af31cb7f1c5d475fc2601d3aea4a4bb282a9d6}}, rankBoost {{cite:400911b40170ed1e59f5ec321ce98f0a3c14f308}}, TagProp {{cite:82429f94e00d35... | r | db448a8facc5ceb0fd5c41109e213d52 |
Linear optical networks are the basis of quantum technologies.
Many important applications of quantum technologies such as quantum key distribution {{cite:8ac10f6ef356d9410e6ef88ef93c3218deec819a}} and boson sampling {{cite:c8b03ace2d04d53623b613fff12005013488d219}} were demonstrated using linear optics.
At the heart o... | i | 5f5a3505e1e8772ac44453e9295b3da6 |
For simple models, sampling may be an unnecessarily slow mode of processing when the whole distribution is sufficiently encoded by the weights and hence by simultaneous neural activations in the output layer.
In more complex models, sampling may be necessary in the same way it is necessary for serial computing: to appr... | d | 675e9396dc1968d643556f459f4ad4c5 |
One potential benefit of the regression approach is that it allows for the development and study of non-separable estimators. We can incorporate covariates and structural information that are useful for estimating the unknown means by including additional inputs in the estimator. The need to include covariates and stru... | d | c3162a29facc74a9f9602602c4d3c0ed |
This paper presents a new embedding transfer method that overcomes the above limitations.
Our method defines the knowledge as pairwise similarities between samples in a source embedding space.
Pairwise similarities are useful to characterize an embedding space in detail, thus have been widely used for learning embeddin... | i | f805ffd26ee15d23b4dd1e9d92638cf8 |
We first report the results in Table REF . We use the FiD {{cite:1368bfc1c21e1340c563ecf4ae3ce0a1882fbfb8}} base reader model on Natural Questions Open {{cite:83ae428cce7731944da691be18a2ab63e5a8d8c8}}. To verify that the model overfits the top-rank passages, we purposely mask top retrieval passage representations base... | r | c6b753d4763f9804df0c2ae7d5036b98 |
The shifts of broad emission lines may originate from the non-virialized BLR, e.g., outflows or inflows. However, gas outflows can generate the blueshifts of emission lines, e.g., narrow forbidden lines [O iii] {{cite:2b0c14bfaa63b54b73866832aadc906db46ea645}}, {{cite:7f9e814da3bc75dbe13ba671dec08810e78afae0}}
and broa... | d | 1d2894897e692aea4a11ac3957500a37 |
Feedback Alignment (FA) The weight update (REF ) of layer {{formula:c3f86692-118e-4419-915f-ef301729f9fa}} requires the knowledge of the matrix {{formula:dae9a053-ef2a-44c9-bec5-a765040bc13f}} and is biologically implausible because it requires that neurons send to each other large numbers of synaptic weights (i.e.... | m | 8af516e10bb526f51d5fc4cc038c9675 |
Using {{cite:e3392111662844a552036c6d1058ed68ef8cb822}}, the sum {{formula:3a92950d-4cfb-48ec-886c-c9e57b7e3244}} is maximally monotone and hence, its graph is closed in {{formula:a02df759-c0ea-46d3-9d72-d8c19602d69a}} {{cite:e3392111662844a552036c6d1058ed68ef8cb822}}.
Therefore, {{formula:78e93f84-cf2a-4902-a71e-0bc... | r | 7aea6cf1f37904b151eacf28978d00ad |
Table REF shows the result comparison with three recent Transformer variants, ResTv1 {{cite:19992446946f1a7c4c2030ca40b788faf48e6aa9}}, Swin Transformer {{cite:567d7d49d3796290d0f28081e92875084707e0fe}}, and Focal Transformer {{cite:25fe03d566c1e4cba9de32288a69c2dc432f9967}}, as well as two strong ConvNets: RegNet {{c... | r | dd639060e61e31b205ca6898c83ab3e9 |
This work opens up four avenues for further research. An on-going extension regards the case of categorical and mixed variables, taking inspiration from discrete GANs {{cite:e15ff841597a28d6b809daa6cb521cae502435ea}}. Another perspective is to relax the causal sufficiency assumption and handle hidden confounders, e.g. ... | d | f804067366debdbc1afb57aec42df7e2 |
As studies have shown that VQA models can be reliant on single modalities {{cite:fb25ea8410f6e98db06fe32da136b3d3ee4b4426}}, {{cite:fc16b54d6c927ce911c9bcd17b7ab3ddcfec9199}}, we define a novel pool-based AL strategy for VQA by leveraging mutual information of the multiple inputs, image and text, individually through a... | i | 87a8b0a76caea59d86a58e64f6eb06ef |
Classical sensor fusion strategies normally rely on the handcrafted physical models and algorithms. For example, in the case of visual-inertial odometry, filtering methods update their belief based on the past state and current observations of visual and inertial modalities {{cite:673711cb8920a755a68e80f18f0dca5033eaa7... | d | 59f7ac03d2d9324c784f2197e4e70e46 |
This notion of solution was introduced in {{cite:abec768e8e4348646562ca841a111626c5ac871c}} for the case of the interaction with the logarithmic
potential, see also {{cite:9a16d78e188495bd5fd0fde628b437967ccded1d}}.
We shall construct weak solutions to problem (REF ) by applying the Jordan-Kinderlehrer-Otto {{cite:e129... | i | 023259578614163f425e3b60b3223378 |
Universality has been established for wide classes of Hermitian random matrices. We refer the interested reader to, e.g., the book {{cite:2c6698875cbda2bf4346209f5637a91a049b99a2}} for an overview of these developments as well as to the seminal papers of Tao and Vu {{cite:fa8849b9ac4106dba2e6ddc0c2578cbeb04a10f1}}, {{c... | d | 6dff39a8c4df8b2d3ec0542c80c6a3c9 |
Therefore, {{formula:b82459e6-f9ed-470d-b0d8-62d9d8d08d26}} is an equicontinuous subset of {{formula:ce412abb-cc6b-40c3-a3f4-fa3c5944f1f8}} . By Ascoli-Arzela Theorem, see {{cite:1140c2bbc2620ca2879b2340a41329191636b091}}, {{formula:6856d472-c291-4c13-a4fe-3cc9a5ba754a}} is compact.
| r | dc5cc4797fad285f5d84b3eba6cb7434 |
In this section, we provide additional details on the baseline methods and cite the implementations that were used. NOTEARS {{cite:8a311aa9eeea28ce6f8b35b032c03815acc86079}} was extended to handle perfect interventions, and to use a Gaussian likelihood (with unequal variance across features). In contrast to the origina... | m | 45ade146678cc11774c8e58e247d337b |
||Pt f||L2(X) (-2c1 t) ||f||L2(X), fL20(X).
In addition, time relaxation property (REF ) is equivalent to {{formula:a2212919-6a3d-439f-825e-04ba74dc3620}} -mixing of the process
{{formula:acc6c83a-9be1-4801-845a-e121fbc77e82}} , {{formula:de98a84b-3c94-4c52-9944-3f17e8df9b3b}} . Specifically, let
{{formula:69ec1b6d-... | r | 273293ffe4f509887f82cc10cb3fa1b2 |
PartNet Semantic Segmentation. We present semantic segmentation results on PartNet segmentation task. We evaluate our MulPro final model with post-processing technique {{cite:4738bf6871961f19e9535e767279d64039b15f95}}. We also compare our results with WeakSup {{cite:4738bf6871961f19e9535e767279d64039b15f95}}, the large... | r | 88adf4bf5d4b2b09030e8b0b4ff562c5 |
It is important to note that a Deep-RL agent is trained for each proposed environment. We compared our method Depth-CUPRL with the CURL {{cite:83c0eb4bcbaffc93dce6f525327381041fa2e2fc}} having as input the raw pixel image, and with an own version of the CURL called here CURL (Depth) with depth maps as input but without... | r | 83d79954bab9aca409c840dfff82e6e2 |
Corollary A.1
{{cite:0b3fb6bf9848f4061ec9a60ea2a727775eb0e137}} If {{formula:4f0cf63a-fdba-4688-8fdd-81609d35d1ea}} is a Hermitian {{formula:013ce770-6cf5-4062-947f-4bcee40f81a7}} strictly diagonally dominant or irreducibly diagonally dominant matrix with positive real diagonal entries, then {{formula:e27c899b-ee88-... | r | 36540a2b8e6c834979218a10d1dd63a4 |
A limitation of the QCCOT-GAN algorithm is the uncertainty brought by the gradient approximation using the straight-through estimator. Differing from VQ-VAEs and VQGAN whose approximation only applies to the gradient with respect to the encoder parameters, QCCOT-GAN has to estimate that with respect to both encoder and... | d | 3319ec9d9135ef8a5ceda224ce7dda86 |
BadNet-SL {{cite:42a2ed2920a283cf17e5c9b1e8420aec0a33e498}}: This attacking method follows the same data-poisoning and model re-training procedure as BadNet-RW, but in this case, the trigger is chosen as a long neutral sentence to make the poisoned sample look naturally. Thus, it is a sentence-level attack.
| m | 094ef2852cf901867a74ddd77ffc28ee |
The derivative {{formula:91133a7e-47d0-461b-8a94-15de79c24a77}} of {{formula:7eeb3eff-7dd9-45d7-8735-7d805a48124f}} in the sense of distributions is a vector-valued Radon measure having total mass {{formula:858f2cb7-ec3e-4660-a1e3-0e8e70be828a}} see {{cite:7a8cdff0b82500fe4deec61f4ebe32d5a3633439}}, {{cite:294b4ba... | m | 0831530389f4f3ff32c0f3698a2e8d76 |
We reduce from Positive Not-All-Equal 3-Sat which is a {{formula:3035c398-06dc-4374-bfd5-5d8f0a238744}} -complete variant {{cite:1acb80e2f0106e4dad17d7184215a138ea75ebbc}} of the 3-Sat problem where given a formula in which all literals are positive, the problem is to determine whether there exists a truth assignment s... | r | d131a870247328a51bd41d23d07ac12d |
with the three errors coming from the variations of {{formula:d3cd8e4c-4306-44b2-8d66-94c51f36df5c}} GeV {{cite:f322496936240faa9b4e637edf7fd81f8ecbf7ab}} or
{{formula:6631a392-a067-4475-8903-af59538ba4c3}} GeV, {{formula:e5189f16-9330-4a01-96e4-a9e847869876}} , and {{formula:3c331fc0-d02c-4d11-acc4-320bb8b72374}} G... | r | c700452fc30a02fa700c6d551020fce8 |
The 1d case of the random geometric graph has appeared in three major places. Firstly, as random spatial models in the physics of complex systems, see for example the 1d soft random geometric graph {{cite:e38a1f620e90c6eeddcf70c63f30c7b198010952}} used in complex networks by Krioukov et al. in network geometry {{cite:e... | i | 760ccc53d7c0c94c77896c2646335933 |
The results in this section are for {{formula:d51ff6ab-1192-4bf3-88cb-2ae852ba0e28}} REs, {{formula:8c7fa4aa-a78b-48bc-bfc9-f6ca5148a9bc}} Monte Carlo runs, {{formula:cc7090e0-3639-4254-bb68-0d602f639e04}} , {{formula:989da863-1db9-47ca-9af1-8e1c50c3924c}} , {{formula:8eeb2493-4a2f-4497-8591-c911e13d1f9e}} , SINR thr... | r | 0aba0f3175bd4271f93ebcc7db656942 |
Graphene is an ideal candidate for such a composite structure due to its unparalleled carrier mobility. This allows for extremely enhanced and tunable electromagnetic response spectra when doped with other plasmonic materials, or fabricated as a component of a multilayer structure. Monolayer graphene has a response spe... | i | bae593e3e8684b267f631063e779d4f1 |
The training process of the Faster R-CNN keeps the first two convolutional layers of the VGG-16 network fixed {{cite:a69a37486084ac8e576f548285218b8833e3299f}}. Changing this, so that the weights of all layers are trained worsens the results by 1%-2%. A similar effect was observed when a dense-sparse-dense training {{c... | d | 49183dce191cfd9c92276e0bd14833f3 |
Consider now magnetar MG J1647-4552. According to {{cite:bec1f68715aae204018536a8ef6a7afa60bb7227}}, its radius is
estimated as {{formula:3b43dfcd-0991-4125-8ba2-1ff418e8c6c3}} cm, the period of its rotation is 10 seconds, and
the magnetic field
induction on the surface is about {{formula:7917c8c5-69c7-4222-b65c-18bd0... | d | 07d93e73ffc92f810648ee0111073ae7 |
We reported, in the previous sections, on the long observations of the bright quasar RBS 1055 with XMM-Newton in 2014 and with NuSTAR in 2021. A {{formula:76d826ae-1fe0-4d15-801a-d512d566f052}} drop in the 2-10 keV flux (1.38-6.88 keV at the rest-frame of the source) is observed after seven years, from F{{formula:10b3... | d | 3b37b7d2fb7829085e732be6d21843b6 |
In terms of the peak-to-average power ratio (PAPR) of transmitted signals, the
former case corresponds to the worst case, i.e., the highest PAPR case that is
{{formula:53df85d7-0967-4fdc-a4b0-60655171daf5}} , while the later case corresponds to the best case in the mean sense, i.e.,
the lowest PAPR case that is 1. Afte... | d | 3de499dd0c0ebc4ed6430049455c62a0 |
We also show that the Gelbrich risk provides a finite-sample upper confidence bound on the true risk under the population distribution if the radius of the Gelbrich ambiguity set scales with the inverse square root of the sample size. This finite-sample bound is dimension-free in the sense that the rate does not depend... | i | f6de63df7e506bb1fb6c88600cc71e96 |
In this section, we test the effectiveness of RDA on feature extraction tasks and classification tasks. RDA is compared with four variants of multilinear discriminant analysis (i.e. HODA {{cite:f8e63ab9e887a8f1e9fbe24790d245a46e7a897b}}, DATER {{cite:df7679504bed96e16a9b0baef7821aceae76fbdc}}, CMDA {{cite:89ed1330f591a... | r | de1a23da8ce3cda7b1e8a3247959eab4 |
Furthermore, two split-merge algorithm {{cite:7461b5805c885f7d3dc53f3681f65394260f3446}}, {{cite:e43633e0485be4d6a4013c11b481001ee49921f6}} with unknown number of initial clusters also posted in Table REF , with more details of algorithms to be found in Appendix . For fair comparision, the inital cluster number k is s... | d | 6b08c555ced6dc142580146fddf66e93 |
see for instance {{cite:92d10a6a546efc3685221ba3123d156627391934}}, {{cite:36802b2fcab019035c2e11b26af57897d9d996e3}} or {{cite:147e3f0fa2e561e7d9b7512fbe7e9485eb109609}} for further details.
In particular, the derivative is uniquely defined, up to an additive constant.
In the rest of the paper, we will use the notati... | r | 5f297130bcb43acfef6dcf70d7876b6f |
Recently, the versatile CLIP {{cite:8105dd8efa36c5f02b098db2042182bdf1f1886c}} has been proposed for zero-shot 2D image classification, which is pre-trained by large-scale image-text pairs and obtains strong open-world recognition capacity. Inspired by this, PointCLIP {{cite:afe3493a21f58689fe632dd1814ee08e9637e40b}}, ... | i | d04dc2d36178bbd096b8e1c9a26ae0bf |
Solving the ill-posed linear inverse problem of estimating the subsurface reflectivity through the classical least-squares formulation {{cite:9de5a239d354080abb0a42fb46d75076e889fcfa}} leads to nonuniqueness issues arising out of a convolution with a bandlimited wavelet and loss of low and high-frequency information {{... | i | 2f46cb7e566bba62a39bac8b1dae2b75 |
Our method has two parts and is shown schematically in Fig.REF . In the first, slow, part we initialize the parameters to zero and perform Bayesian optimization using Gaussian processes.
This method is well-suited for this task, since querying the QPU is expensive and results in noisy outputs {{cite:fb7b65e7bc2c9bf57a2... | m | a7c1b06686d047b37719266e376a23a0 |
Our main motivation comes from the Eigenfracture approach to brittle materials that has been developed in {{cite:ca49cb4102b0ea7cbdd06f7557ddb6e4f014e8c3}} and further considered in {{cite:217756cc511934cee1e5b448531f299fe2c7dd62}}, {{cite:d9b4b02926331e36d8f8f9cf4968b057a229b758}}, {{cite:ddc49175ce4aef76518e0a3af9a77... | i | 8fa86292c27807b3c7448b9e71b95ba8 |
The phonon spectra for the T{{formula:a41c4be8-1acd-4a13-b01d-2f5af3c8e7c8}} phase of ZrI{{formula:0d6303a2-93b0-462a-ad2e-fc4c12875a2a}} were calculated using the method of frozen phonons as implemented in VASP and Phonopy {{cite:976c7b63a973af2256b33fa7c155713215ec8e08}} and density functional perturbation theory (... | m | b789c0cab54c35f0ecefbc8464867c1b |
Since the limit in (REF ) holds in {{formula:4f6dea18-4b4f-45ac-84de-7bd8e090ddc9}}
(see Stein {{cite:9388e27ffd927205b407c3c36c0fbb43de4f3963}}, Chapter II), we have
{{formula:32045bb1-983f-44c7-ba9f-d3f27b728284}}
| r | 6dc560505bf07ded3a7e8ade57523aa6 |
The training and attacks performed were similar to those in MNIST and CIFAR10. Table REF shows the results obtained without regularization, with adversarial training, and our proposed method. There were no analysis on this data set in the Input Gradient Regularization {{cite:2dadd503331d3bafffaa93b69f1dede1d27a7607}} ... | r | 116ea67eb1fce53ed32a4ae83510f0b1 |
In our model, we have two free parameters, {{formula:314b0c2b-cbd8-46ca-ad18-da2e7ad2acaf}} and {{formula:88d7db3a-21a9-43e4-bf91-2940a148b49f}} . {{formula:d877a472-8559-4985-8c31-fdd2f5aec11d}} is a global factor and its value does not affect the shape of the {{formula:2603ef17-72f4-4ba6-87ca-e640b23647cd}} invari... | r | 05e72bf649b782bbd30fa14316ad0169 |
There has been a great deal of activity recently regarding correlation functions of local operators in planar {{formula:85fcb86d-4646-4f54-894e-204c765aa2e0}} SYM and in its holographic dual, IIB superstring theory in {{formula:6c78a5c1-fea2-4c49-8d98-610dbf8dcb81}} . On the one hand, building on Mellin space techniqu... | i | dba215a4e98609818f90cbfc395705fc |
The statistical uncertainties of Eqs. (REF ) and (REF ) have been obtained through bootstraping, while the direct substitutions of expectation values would yield to 3.3 of gain over the shot-noise limit {{formula:816dad7a-dc85-408a-9bda-7deac4d5422f}} .
Based on Eq. (REF ), this proves the presence of metrologically us... | m | f8e181a67e47baf0ec3dfcdf6bfd110f |
where {{formula:25d2db35-d1fd-40c9-8d1f-fcd6e40ebcab}} is either a uniqueness or a minimal function. Furthermore, given a time-independent barrier function candidate {{formula:dfc8d01c-8155-421d-b60e-96d73c2d5d85}} , according to {{cite:6ad8080e832438e205d9a44fcb56b26ca86f13a3}}, {{cite:bcaa74d894d826cf558a9d7478311a3... | r | 13254f9ce85bcdd421c58fea5e5b68a6 |
Traditionally there exist two different pictures on random fields in space-time, one results in space and time being treated separately {{cite:0a80652b0f1530adb98ef68ec89ab8c2e07ac2b7}}, {{cite:8406b46b98164a38d1dc914cda8780a9f8d75cb0}}, while the other models the field as defined over a single space, namely space-time... | i | 40bf00293741f56ffd494bc6fd485029 |
The Momentum Contrast (MoCo) {{cite:407dfeb7038334ac16065838d9e0f4646725d09e}} is a memory-based contrastive learning model. While the SimCLR {{cite:dd9bebd64129e0149218f0f6e56f6cf0b81e4062}} treats other instances over a batch as negatives, the MoCo instead uses a memory bank to store negatives queued from the past it... | m | 985c048ec2315fadcd15ac79b7d51231 |
In the quantitative experiments, all compared methods generate diverse images except Pix2PixHD {{cite:6ee24f5ba11136528eadb5eb83b2fbfea813cbb1}} which does not support diverse generation. Table REF shows experimental results in FID, SWD and LPIPS. It can be observed that IQ-VAE outperforms all compared methods across ... | r | 60b67cdf72b9c624a499483891051123 |
We believe that our model should prove useful help in navigating data collected on complex networks. There are still ongoing discussions on how the claimed scale-free networks must be characterized {{cite:b129fdf766585dcaf56a28c33d12186d77a78e88}}, {{cite:e9512d74194249ba47e02770c1bf1ffda63df1e9}}, {{cite:b5b5cebb442b2... | d | 885bd78e97f601915f8979550a52ad48 |
It means negative value of {{formula:496c75ed-0acd-4b96-b5f5-1a11db472c5b}} microcanonical corrections {{cite:963fffa6bfe2b7ed9f97ae09a2ee979e93e908c5}}, {{cite:6a6da970cdd7f9fd133e5dd681983fbb47652f8e}}, {{cite:0c77c850692223f928747396b0d21cb9a3ee2fe7}}, {{cite:c287c817d1887190e7eaa6f705bef75982457c82}}, {{cite:70993... | r | fb72ad88ca4c83854613c6587937bebb |
Although both models should produce the same output {{formula:1462107f-8ebd-4362-bb81-e1af8572b230}} , we use {{formula:e16e99aa-6688-41fa-8618-b19d1d5d1025}} and {{formula:125d9afb-6fed-49a9-a6bb-33c001cee872}} to differentiate their outputs. To train both models at the same time without introducing extra parameters... | m | 8f61663a6476a670f90fbe82cc2bc23e |
Table REF summarises the comparative study
in terms of their theoretical and computational properties.
Denoting by {{formula:cb466a45-6d57-4d00-8d1f-a22d3ae98dfd}} the size of change between the {{formula:f65bfaf1-eaed-4f37-8114-5cb9b63d7179}} -th and the {{formula:0e70890d-b767-4359-8d90-942d5b4435f8}} -th segments
... | m | 034644f2ac84e757b062eab843386351 |
Results are demonstrated on the IAM, RIMES, and NIST offline handwritten datasets. The IAM {{cite:5f3fc38e2d00696d6832755bfade1189c4de6c76}} dataset contains 115,320 English words, mostly cursive, by 500 authors. This dataset includes training, validation, and test splits, where an author contributing to a training set... | r | 3a18ae29388fc736f180ab01653837f0 |
Another class of approaches apply machine learning to model predictive control {{cite:be93bd3394fe63064fc73082232b9169744deda1}}, {{cite:54867263c786a0b89d63ae273479041a16a73366}}, {{cite:f00fcaf243a0c36f2159c35cc5df29b6bd18ab69}}. These approaches however either use simple learned primitives for modelling dynamics, or... | i | b1811fb4bad674051e35d24ca0d15e2c |
Universal conformal symmetry, requiring local Weyl scaling
covariance{{cite:80a85595125a01f8b89a63701d2e45263319d426}}, {{cite:99d13d90db08b3de94546966109b6aa3f29ff5d2}}, {{cite:5a94256f5c0fac52870573015b334d03375e68a3}} of all elementary physical fields
{{cite:a5b33b015407984a7e9dfaff4269676780623859}}, offers a parad... | i | b3e9fd6c0f3714efa8346643d2356643 |
While an all-multiplicity proof could be found using the methods employed in {{cite:6b61560a73ecff5fe8cd9fa96f59e5ef59821076}}, which relied on position space Feynman rules, they soon become cumbersome. From our experience with the tree-level amplitudes in the usual plane wave basis we know that there should be more co... | i | 79ccb1953c6276f0b9082dc59abc53b2 |
Similarly, Figure REF shows the class-wise vulnerability of the EWC online {{cite:29d78f2d1cc7634fc207c0bc4b4d7a4efbb61dd4}} against the FGSM, PGD, and CW {{cite:cceff7ffb4d3b29c215d08507950b6a5e69f61c6}}, {{cite:c3fccf0ff9253b92c42d2bf0bfc9f109ecfd1a1a}}, {{cite:3519d381d3816b9d84b2d65e2bb04bb34bae094c}} adversarial ... | r | bd26a835e3b1a2002f3a0e204c124c9a |
However, given the limited availability of resources on many devices, performing FL on such devices is impractical due to increased training times {{cite:c2def3048e210e223191322bce6cbfe7e2145f72}}. One approach is to leverage the computational resources offered by edge servers (located at the edge of the network) for t... | i | 64e40728e9d6852e866f2f626892965d |
While not mentioned explicitly, our evaluation toolkit is also useful for evaluating matching methods {{cite:08deae46f36217cf17eb4396842d994f126f9f15}}.
Matching allows the estimation of counterfactual outcomes and is therefore applicable for outcome evaluation.
It can also be considered an integer-weighting algorithm ... | d | 45a20e7140f450b2c0f0fde3cd0270fa |
Background.
The transformer model {{cite:431fd494742f3627971c33b53ea2f2addb8505e6}} has revolutionized deep learning research {{cite:fa49e5ee46995812de5c40ab88b396ea52277324}}, {{cite:1983be73cbe3a4017c7fa76e37ed60c784087d47}}, {{cite:bdcf25c38b33ada086b5517ba44edb9ea89ccae8}}, {{cite:4758ddc2f71ad3005420960fddbd92d20e... | i | 5ff99858fb15bb438723dcd96cd5baaf |
In contrast to ParCNetV1 which applies large kernel convolutions only on later stage CNN blocks, we unify the block design by mixing large kernel convolutions with local depth-wise convolutions in all the blocks. Both types of convolutions are operated on a fraction of the input feature map channels. The exact portion ... | i | 5f5cb807c1be60a655d54db1809eebaa |
After initially validating our models on several toy datasets (see Appendix ), we focus the bulk of our evaluation on four RL tasks. As running experiments with people is costly, we use the standard RM approach of generating synthetic preference data (here trajectory return labels) using ground-truth oracle reward func... | r | dd344b2f3e8ab14e762bc1f9b887dc19 |
We
started our simulations with a burned central region which, in size, corresponds
to about 1 to 2 seconds after the thermonuclear runaway in models of central
ignition.
This is the onset of RT in models with central ignition {{cite:c88c66a74f33804b4deda7cfb659df1025a8904f}}, {{cite:1e6363eaee36027ff7e4188f4f7ef83a5d8... | d | 6297e808b3b22f888f3251c936873a61 |
With fully error-corrected quantum computers it becomes feasible to define kernels with a strong bias that do not require exponentially many measurements. An example of this kind was recently presented by {{cite:b769cb1b7dc8fe221e472a765647ea75430be5f6}}: Here one knows that the target function is extremely simple afte... | d | ceec4b6713fd294e9dccbd6ad6db3a15 |
In future work, we plan to extend this work to the setting of multiple variable groups similar to {{cite:b1ced0da8c4af0bed81426bb4c8aa04b83e7608c}} as well as to use partial dimension reduction techniques. We also plan to relax the i.i.d. assumption to better deal with autocorrelations following {{cite:7ac40d2aa97a4d23... | d | 4467038cb3c125f2dd0c692dbe39357c |
The measurement is carried out in
a wider {{formula:7293a296-454b-489d-9803-ba79a32f72e4}} range with respect to
previous measurements in pp collisions {{cite:dff053cdac1caeb4a193dbe7af2e8fbc37c8c415}}, {{cite:f504105074950a863f508e27d0d0e5e492a12fc1}},
the {{formula:934eafa4-102a-4f98-9cdf-c5d096d19964}} reach
being... | r | 6fec73295472a5ce8f9be5e72ebdc3cf |
Since FST is intended for post- and pre-processing, comparisons to other post- and pre-processing methods are most natural as they accommodate situations a)–c) in Section . For post-processing, we have chosen {{cite:21aed915a28406fd3632c635f2c871d5a05483ab}} (HPS) and the reject option method of {{cite:1a21ce2f99e3f14f... | m | cfdcc34ad2708f500471071aa872c876 |
Experimental observation of the space and time-resolved plasma emission based on Phase Resolved Optical Emission Spectroscopy (PROES) {{cite:0f4202b35f060a6259f32d5c49a167688267e74d}}, {{cite:3311636f171946fea274d677c4724786a09c0ef6}}, {{cite:2c697c4e19b6d1ef0bfc10d3fb3b1a0310b52d84}} provides invaluable information on... | i | 1a5b922a9dcae174f225eaffae3b8b77 |
Experimental set-up. Our colloidal model system consists of silica beads of diameter {{formula:49d1d8bc-8045-4219-bddc-079a2450eb6b}} (Sigma aldrich, 44054) suspended in a {{formula:289fd186-40d9-4263-bfd1-49be6dd2210c}} solution of hydrogen peroxide {{formula:e03ed85e-eede-46d4-b648-e3dce8b31b9a}} in deionized wate... | m | 6c27760a8cff3d55d0b67f44a839a595 |
SHAP. Lundberg and Lee {{cite:7ada4801ca775943f515f11ad1bbe0823094bd28}} present a framework of Shapley additive explanations (SHAP). This framework builds on Shapley regression values, inspired by the game theoretic concept of Shapley values {{cite:5311096384ed67913d627643e466174b8cde7577}}. For the {{formula:00acc3... | m | a54d555b1fa13f663c70c9ab1abc8367 |
Another limitation of our approach is that, because we only considered the effect of intervening on one treatment at a time, we cannot directly address how to select combinations of medications that would be expected to optimize the probability of treatment success. Previous work evaluated the causal contrasts between ... | d | a3f24d1d2114e61d0eb5ab0d9a5514fe |
Li et al. {{cite:6f0168d0f9a5d4559fe1295af48c84b61817d06a}} proposed a Faster-RCNN {{cite:cf7c0790b1225874abe418aa20c6219308891edf}} based convolutional detection method and reported a performance of 0.93 F1 score in the TableBank dataset. They have used Resnet-101 and Resnet-152 very deep neural networks as feature ex... | m | 9b9634ef3e68c98bbc1a960db2b23acb |
Denoising Diffusion Probabilistic Models (DDPMs) {{cite:c335059ab0d5defdacd9eae36d8de2428a4cb1cf}} are a class of generative models that have received growing attention in recent years,
due to their promising results in both unconditional and conditional generation tasks, such as image generation {{cite:455171601491d0d... | i | 3a2c457f32b6849250b1007a0c3b6682 |
The term {{formula:cd3ee48f-17d7-44e7-a0ed-375b1f58a24e}} is the usual single relaxation time Bhatnagar–Gross–Krook collisional operator {{cite:5d40f8237b5bdfc91e776c6d85bf96eb70b976c0}}
{{formula:581f2671-16d9-4569-b9f2-6528d52fc96a}}
| m | 82c19c82dca843d103f3475745d6bd4a |
FGSM {{cite:576cc15bc25ea88ce86ace5fd75d91e5710bfbef}} perturbs natural example {{formula:784806b6-c84d-4520-a4d8-65a6a9df9178}} for one step with step size {{formula:e7b6a5e9-ca5d-443a-bbe3-b7d3ad81fe72}} along the gradient direction:
{{formula:56952cf9-8654-49b0-9b55-18a61fc6f724}}
| m | 9ce046316c4ce54c444888f00767ed61 |
We implement and compare several PINN-based machine learning approaches. These become very attractive numerical schemes for PDEs as they are very flexible when tasked with solving parameter identification tasks. Something that is notoriously difficult in the context of finite element methods as for these schemes a care... | i | 5e3863b9f31dbcecffc55e477c045264 |
Given the wide popularity of {{formula:d88966a2-bb0d-4e88-8426-bda60947e9a2}} -AT, in this paper, we propose SNAP as an augmentation that generalizes the effectiveness of {{formula:04c24927-e412-4edf-8cd2-5cf597a511ff}} -AT to the union of {{formula:5c311bcb-6f58-4804-a38d-02dda7d8126e}} perturbations. SNAP's strength... | d | c54b0cb35d3724532a06370f392d9de6 |
To do that, first let us observe the fact (van der Vaart and Wellner {{formula:fc9c49bf-6236-4305-be63-223c767e801c}} {{cite:34b5fccb4fea991606dd7f65223f2eea3c37dd0c}}) that any appropriately measurable Vapnik-{{formula:bbb29e85-2325-44b0-8f5f-5b1a1696655c}} ervonenkis(VC) class is Glivenko-Cantelli(GC) provided its e... | r | 765966721b2c092a5b9910bf1ec63155 |
(i) From {{formula:c68475a4-4fbe-447d-bc22-61ff6cdb03a5}} and {{cite:6f18bc683b1447536ec5fee1ffe983d8ddc96ede}}, it follows that there exists {{formula:920f2300-0c52-45ce-8440-fb48c7f173fa}} such that {{formula:0b43e5f7-c557-48d0-949a-a58114782829}} for all {{formula:3a40cbd6-3f18-4650-9d46-5a07bdd58d55}} Since {{f... | r | 7298900b662c781c65fca1239008b875 |
For implementing NGP, we used different neural network architectures such as self-size estimating feed-forward network (SSFN) {{cite:995d5035ec70485c8cd2ad85b54e8fe9280c16b3}}, multilayer perceptron (MLP) {{cite:2d9906508c7685e7e1babec8ba3bc6f7cd2f0fb7}}, and CNN to show the universality of NGP. We used a single layer ... | m | 32d85a9696577710cf236ca873d05532 |
The optimization objective in eq:objswmtl is nonconvex with biconvex terms in inequality constraints. Moreover, there will be a huge number of constraints when the numbers of tasks and features are huge. There is no existing efficient methods that can handle this challenging new problem with theoretical guarantee. Alth... | m | 1938391aaf8eecb908adc56270761747 |
Ethical considerations.
SLIP faces all of the same ethical considerations as CLIP, both in terms of the harmful applications it may enable, as well as the potential for amplifying and perpetuating problematic behavior in the real world.
CLIP's ability to leverage noisy and minimally filtered data scraped from the open ... | d | a537113ed3142d877239a765d0bcfad4 |
In this work, we were able to write the full anisotropic metric as a function of these critical exponents, which correspond to the respective scalings of each coordinate of the holographic dual field theory, together with a flowing structure with respect to the {{formula:b04bd305-e6c2-49ac-8c78-ae95809ca3e2}} paramete... | d | 88f7b18719df477338b54151ae06beaa |
In addition, the strength of the N-Jet representation lies in that it can learn filter sizes, and thus the receptive field size, during training.
However, recent work has demonstrated that the effective receptive field (eRF) size of networks can be considerably smaller than what would be expected from the kernel size {... | d | 70ae6171aab362a6b32ffebd4cbeec21 |
Domain independent fusion.
Inspired by Wang et al {{cite:582338c630879b8ae1fd7ecc79d953b7bf84b3c7}}'s work on bias mitigation, we perform domain independent training, where we treat the real and synthetic output space as separate. To do so, we create a new set of classes that contains tokens from the synthetic set only... | m | 374d0e472934b1cc814c87fb4f5e1b7f |
In the end, we compare the performance of our proposed method with the state-of-the-art methods {{cite:a4f72552aed0440df9b7a366be09212e5e94fc0b}}, {{cite:c3f0d7d7f108e7158663193a656282d710a46993}}.
From Table REF , the baseline model has worse performance than the state-of-the-art methods but it achieves a competitive ... | r | acb0e1938273ee2430bcb5b3c7fc1a6d |
as subsets of {{formula:13565b40-a62e-40b4-a4df-00e7f972bd2b}} .
Our approximation analysis considers the above bounded hyper-sphere determined by {{formula:bd4ff23f-7b44-4579-8f7f-92006d96bf4e}} instead of {{formula:f70e7bbf-626b-4cb1-b713-574ab10094da}} used in problem (REF ).
Following {{cite:a42b7ea30ebce82d52ae7... | r | a17984af4c63637db1041270255b9f8d |
Asymmetric Architecture
Another line of work exploits an asymmetric architecture {{cite:a3c9faab7e7f288f0ee88579464a3c3c142a7daf}}, {{cite:7c4f77845c54f5c9697a29c4ad6bea7b9439199d}}, {{cite:00705b4d4a24c561cf9e2a018c75a5645947bf1b}}, where the high-to-low process is heavy and the low-to-high process is light.
{{cite... | m | 74e29d5d37a4cb04a813ceaa60e40e79 |
In this section we present the results of the forecasted sensitivities using the methods and experiments detailed in Sec. .
These are shown in the {{formula:f85c0b43-1ef0-45c2-bf97-55b35b556e0d}} plane for DM masses {{formula:cd186304-b237-4bfc-8c1a-6d5cf380125a}} GeV. This region is interesting because ample areas a... | r | 6b10309ca96982de5f2f904d72f1fa22 |
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