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Our results for the form factors of {{formula:4d135bec-1c7d-4213-8978-cfcba924b8ae}} and {{formula:14e9a11a-ade3-4cc4-aeab-3ca3730372f6}} transitions are collected in Table REF . Our results {{formula:9a81a55b-3a4f-4273-a4c1-140e4bd4f032}}{{formula:725628f1-2155-4156-b483-20f2db472193}} (S2) and {{formula:1a0611f4-d8... | r | 4781deb277a62848b7b150fd968ecb75 |
There are several versions of the EnKF. In this work, because the state space has relatively small dimensions, we use a classic implementation called EnKF with perturbed observations ({{cite:d586a47c692091893d1be7b22c566a7cab9fd808}}). Because of sampling errors and unrepresented model errors in the prediction state en... | r | 7cad18fd0cfa9b9d6b5c7e925b1b710d |
In this paper, as down by {{cite:11a65c7cfb4204d52733a34a5ede374ce166654b}} and {{cite:e5fa92d4a48ee8b42dde6fe9d1050e5bfd581856}}, we set {{formula:2b055818-1ff6-4841-99a1-d4cdc7d7009c}} for {{formula:67a9881f-9904-4c2c-81c7-f4104e79b95d}} and {{formula:216278d2-6a2d-482a-bedf-2795698d1f87}} for {{formula:f0556c4a-3... | d | 96bd792b9121377156f54bd4fe9aadc5 |
While the higher accuracy on observed languages could entail an overlap between LangID and contrastive loss pre-training, the robust performance on unseen languages suggests a more broad rationale. We believe instead that the observed 'critical layers' are locations where speech sounds (phones) become most distinct dur... | d | cc04608baeff806d3fa4dba31b6e5279 |
In this section, we first revisit the FedAvg {{cite:14658adce1c6471da085d8c4fa9e462da807027e}} and FedProx {{cite:f0bb2cdf52fcf895280f8139d683c826bec53ebc}} methods which are widely used in FL tasks. Then, we adapt two optimization methods from the multi-task learning literature to the FL setting: dynamic task prioriti... | m | 83d9556cc6d6cb1ef1c8eca386bc2848 |
Metrics. To measure the anomaly detection performance (classification of anomaly slice and normal slice), Area under the receiver operating characteristic curve (AUROC) and Area under the precision-recall curve (AUPR) are adopted. In addition, we also provide the segmentation performance based on the dice similarity co... | r | 642bdeb6867023d94fa5fcdf90277b30 |
CNN:
Proposed by {{cite:7b24098dd4445e5023cf90ba8b059e66a7e905e9}}, we perform convolution and max-pooling operations on Telugu word embeddings to get a representation of text that is used for text classification.
| m | d59b05b4bb3132ed797d9f8bc9240f7a |
Seemingly strict limits pervade cognition, from the so-called attentional bottleneck {{cite:b20fc8a2c0dfbf1ea3f71cac345a2a3a7f93274f}}, {{cite:da7560d97846cf9e9dbc7025f64af5e1ec4b20fa}}, {{cite:bedfc23e33329e920c232250f73f0f20f3598f9f}}, over working memory {{cite:7c26350f1bd8831baf297ff98a28c7b5a21c08e7}}, {{cite:8680... | d | 4959d78eee7c1a1b13192bf932913204 |
Besides the classical macine learning metheds tested above, some popular deep learning
methods are also tested.
(1) Seg-Net is an open source project for image
segmentation {{cite:26d8659fb166da9f0847bcb993f9e39bb4087e8a}}. The network is identical to the
convolutional layer of VGG-16, with the removal of the fully-con... | m | a2ecb95cca828d75b7905c3094271b5a |
with {{formula:3ed75099-36dd-4211-9b66-d57b80104344}} for a total surface density (stars + gas) {{formula:7e4b0522-c58f-49cd-86c1-2788bbada335}} and {{formula:c4e8a483-167e-4fc5-84aa-a84e97f2a869}} otherwise;
the metallicity derived from (O/H) by using
{{formula:8e1b3f80-05b2-40a1-aee9-95743308a66b}} so that {{form... | m | 6781f9894158c97f4b3e63d6e3cfe7c4 |
paragraph4
.5em plus1ex minus.2ex-.5emBlending Pasted Objects.
For composing new objects into an image, we compute the binary mask ({{formula:4f41c9a3-f94e-4b9d-90f3-7378b12d1357}} ) of pasted objects using ground-truth annotations and compute the new image as {{formula:00ce27c3-3866-455f-acfc-6de2057e8149}} where {{f... | m | 62b8babf668afe1981b68eaa5ad86326 |
First, we calculate the key rates of the asynchronous-MDIQKD protocol with a short time interval {{formula:7567e62b-4a4f-4894-a039-30eae062ff7e}} . The detailed formulas for simulating our protocol are presented in Appendix . The statistical fluctuation analysis formulas are presented in Appendix . Fig. REF shows a sc... | d | ee176b5e137bd764f0ff76314677a4cb |
where {{formula:e23e176d-631c-40c8-b4cd-7a02f086eaed}} is the density of a crystal. The calculated value of density {{formula:950e3de1-683e-4b21-bec7-a78ff1e14541}} is 4.94 g cm{{formula:be5f43c9-d431-420d-b8d6-f086b7835764}} . This value is in good agreement with the experimental value for pure CdSe {{cite:3ed9ad722... | r | 9e8ff5a45ed178b565f66be48abcf445 |
as the corresponding world average, which has an amazing precision of {{formula:36db8a17-8dae-40e3-871c-77437d3eddf3}} parts per million.
On the other hand, the outcome of the Muon g-2 Theory Initiative for the Standard Model prediction of this quantity {{cite:629b469d55832c500fe66840c025e5b6325d65a5}} This result is ... | i | b189e60f38ea116ab7018549826b2e61 |
The same as the classical Gauss hypergeometric equation, the Heun equation has several confluent forms. Indeed, there are four standard confluent forms when two or more singularities merge into one or more irregular singularities (cf. {{cite:ca3093ce4dfa226a780a36e5f71b34891d586ff2}}).
| i | 3a54fbc4585b1a5a118210d6c3db286f |
The Intra-Modal Regression module leverages CatBoost {{cite:8663fa17d9504cc3d0d8b530468a76b7e9ff90e4}} that uses gradient boosting on decision trees for intra-modal test query regression. For each model pattern, we use all available spectral bands as the input features and the output query band as the regression target... | m | 6c02dc859ac27d0aec17f592d0fe0f8d |
By combining the two aforementioned directions, we believe that the active–interactive segmentation model is the future which aligns beautifully with the life-long learning paradigm {{cite:a244b239afa7490696a4dc7a959f13297ab1cba2}}.
| d | b4b23c67e400433a8457ebab0b78d45a |
Is PALMER less expressive than standard deep Q-learning: Two important premises of deep Q-learning {{cite:e2bb3b4e2e0fa581c24f19efdba8ed46c6aa931f}}, {{cite:64b1773b169e5d90d106f8af7c3313315e7fa493}} are: i) minimizing Bellman error through temporal-difference (TD) updates can restitch observed transitions in new optim... | d | 89bc5b6ca92514cf26744d5d47f31688 |
We report the results of the models trained on MS1MV3, and tested on various benchmarks. The results are shown in Table REF . As observed, on LFW which is saturated, our proposed methods achieved top accuracy along with a few other methods. On the pose-sensitive dataset CFP-FP, our part-fViT has obtained the accuracy o... | r | 9d38ad987ad87bfafcabb707c19ab440 |
To this end, we have conducted a large-scale psychophysics study using a fast-paced animal vs. non-animal categorization task. This task is ecologically significant and has been extensively used in previous psychophysics studies {{cite:6b531a8288ef558dbc8d93020baf8f1640f5e192}}. We have considered 3 state-of-the-art de... | d | ba738b0348d5e66da4bfd0faf4c25554 |
Copy methods stem from Pointer Networks {{cite:058503dd118d090b50cd0bb30db51ad2b5bbf4be}} which use the attention distribution produced over the input sequence to choose an element from the input at each decoding time step. While at its core Pointer Networks only allow copying elements from the input, Copy Generator Ne... | m | ea80434e2ad65f8fcb98444e672c2eff |
Here, we demonstrated a possible connection between the near-critical branching dynamics of the NALSM liquid and the edge-of-chaos transition (See Appendix REF ). The critical branching transition has been extensively used to model critical dynamics in brain networks {{cite:e7c5ab3944488961cbd28d21f2ecd10df1405a34}}, {... | d | af40c53a0783196c521a306197788761 |
The robustness of modern NER models has received considerable attention recently {{cite:90e730dbc8efd60d89d9fd4980eec3a0652480a7}}, {{cite:5e71eec322d953da140fd180ecd34780bf82db2a}}, {{cite:dbba66a33d5d3029234b54ced25bd4294d0c7aa9}}, {{cite:63f93fc00045277bc86aea84aa3a798f8dcdb23a}}, {{cite:4f44b1970d5e902f68cf39e3d5e0... | i | dc64271077a3ffca8cf2608b0488a5ad |
That means non-local correlations are certainly relevant whenever spin, charge etc. fluctuations are important. An obvious regime where this is the case is the vicinity of a second-order phase transition as already mentioned. Here the magnetic, charge etc. susceptibility diverges and significant changes to the DMFT sol... | i | bd5d79697943e1888115a4c64fac504d |
Since the quantum average power {{formula:f5563265-0559-4e85-aa7b-62adc7495cfa}} , in all approaches, is expressed in terms of the population and the coherence correlation function {{formula:aee8b272-daf0-4e5a-9815-aa0e09e59551}} and {{formula:4e558177-27ff-4ab5-89e1-fffeda7a3a79}} respectively [see (REF ), (REF ) an... | r | f41f1fc066fde5b003328c7f948b6f04 |
Estimating the generalization error of a pipeline {{formula:b30dbd20-3c8e-4671-879b-17e38c46d31b}} practically requires to restrict the CPU-time per evaluation to prevent that one single, very long algorithm run stalls the optimization procedure {{cite:7c2c990940db8a22dc26b5ba05c856ab7faf9d4a}}, {{cite:84ba19040d03ab2... | r | d1116c975010fc260d23a145bd3588b0 |
The 87 datasets and 99 low-precision configurations in experiments are listed in Appendix .
The datasets consist of natural and medical images from various domains.
Apart from CIFAR-10, the datasets include 20 CIFAR-100 partitions from mutually exclusive subsets, based on the superclass labels.
They also include 50 sub... | d | d906503acae020d0574db2a148f750ba |
Conventional approaches {{cite:6e71af80bed2ac9a533925d9518abe477e077236}}, {{cite:77ff43683cf52f280bbaf9567a5b14f97c7bd52f}}, {{cite:48e9678fc6f018e08bf2fea6ebc2c5cd13b740cf}}, {{cite:7b7105f7ca9bb00879c4edd906077ca4b1068c34}} typically discard unnecessary channels from the original over-parameterized network. Distinct... | m | 4787e9fb1c0212e314439b57ea100745 |
In this paper we provide a general framework for comparing generalization bounds for deep learning, which complements two other recent large-scale studies {{cite:041497d970d8d1080790a5996fb6f63390ea36af}}, {{cite:02459685121bae71483bde1427b4b6221aa19fd8}}. Our framework has two parts. Firstly, we introduce seven deside... | d | f4d441238f2e77a71d5ac17c92303da4 |
For more details see {{cite:4f9f9524e7e90a398a8f2484817c06d6f1ee6f36}}, {{cite:5b1c55afc38a3207e678b9693f0b9ae0a101b728}}, {{cite:d19a8bdf94b3b2eba3030e5dab5eb9268daab793}}.
This proves well-posedness of the WRM for {{formula:c86b127a-74fd-4345-b11b-8f3395b67941}} and {{formula:149b79bf-bdd4-4b43-8502-6765617d5ea5}} .... | m | 7d116700fd02606cf91e455f1ecef796 |
Generally speaking, our results imply that, even with the truncated heavy-tailed noises, the function {{formula:6f72ce19-bb97-484e-a367-8b3bfbd6aa14}} needs to satisfy certain regularity conditions to ensure that SGD iterates avoid undesirable minima.
This is consistent with the observations in {{cite:fdb7e51c6a32111c... | r | 03aa2a3f24693e6fa78f59504a81f745 |
the minimal dimension of the Koopman-invariant subspace that approximately captures the limit cycle attractor for all three {{formula:a473459e-edc0-498f-8ff6-089afcc4218f}} that fall into laminar vortex shedding regime {{cite:f5660d6765467b646809048081c66ca66fa9cedb}} is five, cyanwhich is consistent with previous mu... | r | dc6dc596949a9660a86bc8eaff76ee98 |
To estimate the empirical upper bound of the classification accuracy, we approximated how fully-supervised
models with access to all data would perform. To accomplish this, we used all the data available within the
datasets from which the few-shot tasks in the meta-testing phase are sampled and trained one classifier p... | r | 001e5ac090d782275a1225eafa3022dc |
Lastly, we investigate how the structure of the SF network affects SOB. Specifically, we are interested in the influence of scaling exponent {{formula:62c1825d-892e-4910-b239-1de4a1fd3547}} since this parameter regulates the prevalence of highly-connected hubs in SF networks. The latter units play a principal role in ... | r | f950dde2f0a68847468bd3d2c6cfb893 |
We consider different combination of ID and OOD datasets for different architectures. We compare OOD detection performance of PNN with existing works including state-of-the-art approaches such as Deep Ensembles {{cite:481ad2c2800d8dfae1e779ac9153662033afe383}} and BayesAdapter {{cite:041dcccc53f9affdb83a8122f3f7355a5af... | r | 80a9171b99a8c49add165ad96a27695a |
As we mentioned above in the phenomenological
approaches the parameters of the model, {{formula:a1a2d116-75ed-450a-b860-5b856ab48fdc}} ,
{{formula:24ed91e9-614f-49d6-94b5-f756d9d34b71}} , {{formula:952cfff2-4e2f-4294-bea0-39f8f504019b}} and {{formula:47bb0ad7-1105-445f-b0cd-7c2b60a0e528}} , are fitted to the spectra
o... | r | 9188f093b4a91fe4b5d8545f55317cbf |
The explicit derivation of {{formula:b1421ae3-ae36-4d20-82c1-4f47b0a1dfcb}} constitutes the primary reason for studying the complex zeros of (REF ). The main device for treating {{formula:d760d8c4-ee64-4923-b99f-17febae21d51}} throughout the complex plane is the Rice formula. This remarkable result provides a represe... | i | 6ad33c413672adaa21b992bad42f35a2 |
For the CIFAR100 results of average incremental accuracy and average forgetting are presented in Fig.REF . Three groups of methods are shown: non-exemplar based (FT, LwF, EWC, MAS, E-MAS+SDC), exemplar based (iCaRL-CNN {{cite:bd30fa2f9c00023434427c37223bef6a1491e6f5}}, iCaRL-NME {{cite:bd30fa2f9c00023434427c37223bef6a1... | m | ce8aaa9b10bf12931150398b836149a3 |
{{cite:624437488dd825c3f0deb25165411f7ba9f961d9}}, based on 22 years observations at 15 GHz found that the jet of OJ 287 is rotating with a period of {{formula:7b400c51-2fad-4b68-ab78-b453730cbcfa}} 30 yr. Modeling of OJ 287 radio data showed that this rotation can be explained by a combination of jet precession and nu... | d | bc295f663f9ee12834f5499b3f80ac66 |
There are some interesting and surprising technical twists to our results, but given a definable hypergraph {{formula:83ff342d-705f-4a35-b366-d2b8d59caafe}} in finite fields (or in the difference fields {{formula:14882735-53a8-47cc-8f72-4f5fb99a7bad}} , as discussed below), we find some {{formula:8583821c-9cdb-49b7-ad... | i | 772e2eb590bc077a9c9db36ae2126068 |
Without loss of generality, let us assume that {{formula:5f7d62a9-cf1f-4704-b015-2867c0c58be0}} . As in {{cite:abe1afcb9c9dff58f5d99d90a7c6a55d9d89c7a9}}, the input image is then divided into {{formula:71304b8e-54df-42bc-ab10-b173631722a7}} patches of size {{formula:f50112b8-0b16-46ca-932a-2a076e0557a5}} , where {{for... | m | 3645807b047777f92d5f7bc9bbafb8fd |
We have investigated the possibility of detecting these objects and discriminating them from BHs with current and future GW detectors. We find that ET and LISA will allow one to detect and potentially distinguish exotic binaries from BBHs (with total mass {{formula:ce809479-65da-4098-b4bc-014ea6220811}} and {{formula:... | d | e83c9519b0441b217c2ffa0286d6b234 |
The reference values for {{formula:93ddfbb1-0e32-4178-b404-c7cbd4a3da2f}} , {{formula:6c38425f-138b-46fe-b736-40b684007020}} and {{formula:1e0ce80b-4798-416f-8f9d-cbc5eee8ec85}} are assumed to vary freely between ({{formula:05001f4c-cc2b-46c3-b968-8a5e871f496e}} ). The reason behind this specific limit ({{formula:511... | r | be29ac97880cc854500710115c7dc1b6 |
For a robot to grasp and manipulate any object in its surrounding environment, it is essential for it to estimate the position and orientation of the object relative to itself - often through the use of its vision sensors. In recent years, advances in deep learning based approaches have used powerful convolutional neur... | i | 7cd8f7b16401fc868ec7875a0dd76ea8 |
Humans usually read text by row or by column, but how do you “read" a 2D image?
We first look at the area of greatest interest and then the other areas or patches.
And how the DNNs do with the image and text?
Arguably, regardless of the text or image, many DNNs read it as text. Recently years, inspired by the Transform... | i | 2ed88794aae007aebb0264827524d3ce |
Let {{formula:0e88f79b-fee7-4246-bddf-aea903b6b3b6}} be an unknown joint distribution over instances {{formula:54f7014d-6436-4aae-b77d-6a6270c860b1}} and responses {{formula:bfc7d2b9-357a-46f2-acc3-6c8192bd7023}} , where X, Y denote random variables, and x, y are their instantiations. A common goal shared by many pre... | i | 271c492a48d30bb8c10a04859504bb7d |
Static nonmagnetic random disorder is most simply characterized by a broadening {{formula:c5d76ec1-799e-4b51-a5fe-32f4fa60d617}} (where {{formula:8d3548da-0dd0-4932-af61-7fa48ae02fdd}} is the Fermi energy) of the electron spectral density {{cite:b035551e89c18f7b6c9269eabe0bca59d5217e83}}, {{cite:8fc28b6ff2e99bfbaf86c... | i | a670407d1910466b444165e6518f6a3f |
The programs FeynArts 3.9 {{cite:4f23d82201d26fe264fb6b59db4e3709972a2bbc}} and FeyCalc 9.3 {{cite:8b8986d5a77a7814a224938a00330753f0eb0b45}} are used to generate the amplitudes and do the algebraic and numerical calculations.
| r | e631351d46d819bc8230fbe913eff3da |
One tempting question: is it possible to reach the maximum mass of approximately {{formula:e51286c1-e90c-4106-b91a-65de98d06d61}} while keeping the results still in agreement with the canonical mass observation data? After systematically studying all possible combinations of {{formula:1668df3e-b717-469e-8887-0aff1fcc2... | r | ec2cab10cb48a1e469db8dd3f018663b |
One important feature of stochastic gradient-type algorithms is that the noise drives SGD to escape
from sharp minimums quickly and hence SGD prefers flat minimums {{cite:f91ad3c62d75e7e1c892e03d3924c82c051f53ea}}, {{cite:3d07444477bc85b472447e2dd9736befd97f8cc4}}, {{cite:e7277cd014b2a6332ad7e9d2da93661e4118ada8}}, {{c... | i | 2f46ac28630a99bfd6e3541318ecc62a |
We have found 113 cluster within GAMA. For this sample we have estimated positions, cluster redshifts, velocity dispersions, cluster sizes, and cluster integrated luminosity. Our algorithm has been tested using the GAMA mock catalogs ({{cite:0e10bf13f537db0e384ad6e1b9d7897cf72b698e}}). The calculation the cluster lumin... | r | 7181e74586af2bbe13d6400c04fe8eaf |
Also the fermionic definition of the topological charge, via the eigenmodes {{formula:8b61b6c6-30f9-4eef-a444-62e4bfc82557}} of a chiral Dirac operator, has been used to explore the IR structure {{cite:988bf7db6964019dd09af84cdb1f72a2bad3c394}}. For this so called Dirac filtering one truncates the sum in
{{formula:a1d... | m | 98c75bc3c15fce8faf4331791a893026 |
Designing submodels based on the main CL models to regulate information transfer. The CLS theory states that the hippocampal system exhibits short-term adaptation and enables the rapid learning of novel information that is played back over time to the neocortical system for long-term retention {{cite:28a95a3bcc74a40eb... | d | 76956b383083660bc0d74bfdb96af5e0 |
Formally, in computer vision, given n images ({{formula:92516a72-17a6-4e59-b51f-cab1cfe978cc}} ) of size {{formula:f6486e76-dab2-47b1-9fbb-279f37c94e5b}} , k kernels of size {{formula:4bf49490-1839-4158-bddf-406cdd968b78}} are set. For each kernel patches a small image({{formula:0c632fd8-9ed8-4e1f-a579-5fb757c38e7b}} ... | m | 1dfb1f321a92a27b7334e4e020a3d402 |
The relation extraction (RE) task aims to identify relations between pairs of entities that exist in a document. It plays a pivotal role in understanding unstructured text and constructing knowledge bases {{cite:188e04322b1548610554ab57f593b131158716fe}}, {{cite:fd5846a9bcf7d9b7e895634ddd5d61c9f943208e}}.
Although the ... | i | a29baf4c57aa69c1d1fbc57fb0ce654e |
To the best of our knowledge, most of the SSL SOTA methods are validated on multi-class downstream tasks and only few SSL methods {{cite:e4d54a6228cf41829d77c0f6430e9bbf7c159f72}}, {{cite:de524e473774722bb8e36a3cf545c359edb8a36b}} involved multi-label classification as a downstream task where simple CNN architectures, ... | r | b669b00853dae9bde3f7c881ec65bf22 |
For the ShanghaiTech dataset results in Table REF , our MIST far outperforms other RGB-based methods {{cite:a5e347b737e293caa8967e2a557af75b243017d8}}, {{cite:81a20768173a8046739090638661510f0132c860}}, {{cite:69c061757987f83c92d87626c446cad9b268c708}}, {{cite:f664080c61a78803bc078d1a280aaaa43263dba7}}, which validates... | m | b108f4624467441174a0e70282de5d51 |
The classical mirror symmetry summarized in Section applies to the so-called (families of) lattice polarized K3 surfaces replacing the Kähler cone with the ample cones {{cite:390fbdc020f7e838c4b182fe56e048f48af68c6c}}. In our case here, we consider a primitive lattice embedding {{formula:f8cfa69c-ce8c-4c18-a1cd-d8282a... | d | fead537a2f93cb2736b2998bfd3c4be4 |
where weighting parameters are set to {{formula:3173ef59-7515-4f4f-a272-25121ebe23a8}} to achieve a balanced training. We use a decoder-encoder network with 9 residual bottleneck for our generators, and a 3-layer CNN for our discriminators. For segmenter, we use a default setting of a 5-level UNet with concurrent SE ... | m | 67694dd7e5bd1a648517bb13aa63f767 |
Further, the new practical version of minimization using (REF ) mentioned above will be combined with (REF ) to establish a new ANN framework, based on deep residual-type architectures, for the approximation of solutions to (REF ). The approximate solution will be, therefore, expressed as a sequence of ANNs {{formula:e... | m | 42b175931a2acc91223d509c7ca605a1 |
As we discussed above, the collocation points for residuals are usually randomly distributed in the solution domain. Furthermore, all randomly sampled point locations are generated using a space filling Latin Hypercube Sampling strategy {{cite:0cc4b1308411c4f5a4b6fec6b53fa0d0c4bd2f74}}. This sampled method works well f... | m | 10c3f95957c47f949a12eb99fc2df0f9 |
Using lower weight CNNs (less than 5 million weights) was necessary to prevent over-fitting to the small training set (less than 500 images). Regardless, due to the large number of weights in the CNNs relative to the small training set, the two fully connected layers before the output layer required a 95% dropout to co... | r | 7a0531c33e4621441f0ddebc2112ea39 |
We experimentally demonstrate the reasonableness of this formalization of side effect regularization. In the ai safety gridworlds {{cite:40d114ca90f694c052e5c412ae4d66655acec9b8}}, we generate several held-out “true” reward function distributions {{formula:13a753f4-f461-4303-8aea-9460814b2b7d}} We consider two gridworl... | m | 769bdf24a58bcd8c67760bc0357dfd97 |
Of particular interest is the characterization of ABP in the dense regime, see e.g. spontaneous flow {{cite:9113caf16372917fe8611dba39d7f121dc6a3ee1}} or glassy behaviour {{cite:aeaa7594efa12f1d8a7c7760570f75c6a3d655fc}}, {{cite:d751418d6d2f3a6ebd47dc45dda5dbadb4c5e34a}} in biological tissues, biofilms, cell mono-layer... | i | dd42bb1041a0f8d21485ad4ab9903bdf |
We also experiment on the YouTube-Object (YTO) dataset {{cite:2702d1d3e04afc51889a451ee1b0724fba2110d4}} to show the effectiveness of our method in segmenting objects from videos by simply evaluating the results produced by SONet.
Following prior works {{cite:f27e453e89edc20e65c8eaba88d1f9b4a21c38d0}}, {{cite:ffb8511ec... | r | 9ede83c9df62d609c2b3db01db5efba1 |
When cooled down fast enough, a liquid can avoid crystallization and form a solid with a disordered structure. This phenomenon, termed the glass transition, has been widely observed in many natural and industrial systems but still lacks a satisfactory fundamental understanding {{cite:0a0cbc82b171e342f3eea17d0f206ef5713... | i | fcdda9b0db870509d917f2013b052d26 |
To ensure global convergence (i.e., to ensure convergence to a solution from any initial point), suitable modifications of the Newton method are needed. An example of a globally convergent variant is the so-called Levenberg-Marquardt method {{cite:6d69f6556243edc69386ad60a5eb61eb687b0808}}, {{cite:e35f9be6173581bb2b657... | i | 27741dfacff4becd6357dcc43ab58071 |
Overall, our study can provide a characterization of the topological order through the response of the entanglement spectrum, on a local scale. This may facilitate the experimental sought of topological order {{cite:595dcf85354cfa4bb29e7b10cf72142783d75775}}, {{cite:d8055124aaf83be8f2f2147288d623bdd2a31727}}, {{cite:0b... | d | 561c803ef176a5f9f4344df64b1a75af |
By (REF ) and using integration by substitution for operator-valued functions {{cite:59eefdeb418b48ab503ddb8d45755eca19145a88}}, we get
{{formula:bbabe653-298e-4720-9427-b29e811b8f38}}
| r | 10f8dad5af6beefbacee33ee2f6991bf |
Polynomial neural networks {{cite:880260a45ffcc7c0fc81d06aa56a187128531aeb}}, {{cite:3774d5a92b858c7e97833767e5b5309cb11ef7be}}, a special class of NNs-Hp {{cite:a7956b15e708ecdd8d495e98c8b3026506cb4dbe}}, have showcased remarkable performance on a broad range of applications.
As a step for analyzing NNs-Hp, in this wo... | i | 1072b1bd83380c8e6e5e22593280afd0 |
It suffices to show that {{formula:64b12741-5690-4f49-bfd0-fd35e409fb97}} is free over {{formula:f8df62d3-600b-4f53-b1b8-f036c6c4e525}} since free modules are faithfully flat modules. Note that {{formula:98c5473b-5e82-4b93-86a6-8b20e65a2d8c}} is injective since {{formula:08310cff-d9ae-4656-b542-93a0492cc3d6}} is a ... | r | 8b24d0bb6697d5441b04b97e88bfb9f1 |
It is interesting to compare the results obtained in this paper
for the cover times of RWs on RRGs
with the corresponding results for RWs on regular lattices with the
same coordination numbers.
For example, the coordination number of a hypercubic lattice in {{formula:665254bc-79c0-486c-a668-5da12c16e565}}
dimensions i... | d | b62dce6646e4c759df545e06d5acfe49 |
Synthesis vs Analysis methods. Our result could also inspire new ideas in estimator design. There are two families of methods in non-parametric estimation. One called synthesis framework which focuses on constructing appropriate basis functions to encode the contemplated structures and regress the data to such basis, e... | r | f464263e06f5e39e9aa55dbaef19d281 |
According to the BEAMS with Bias Corrections (BBC) method {{cite:15ff6c79f8a36d3a99499515244e10a49bfe35c2}}, the observed distance modulus of SNe can be simply given by
the apparent ({{formula:3ff44625-b580-4cb3-a5ea-10d7da58209d}} ) and absolute B-band magnitude ({{formula:8fc1e061-8782-414d-9b46-6bce7b0cb4fc}} ) as {... | r | c1c9889b8617175150c37cf43a6d0ac4 |
We have investigated two holographic models of evaporating black holes. First, by perturbing the 4-dimensional black droplet
solution associated with BTZ black holes on the AdS boundary {{cite:8a847f84aab94ece04baff2fdcf5f89c3441b840}}, we have constructed the bulk
geometry dual to the boundary field theory in a time-d... | d | 3107ce7100f4e13867f9f2a96e559b7e |
From Figs. REF and REF one infers that the radiative corrections
decrease rather fast, by about 10-15 orders of magnitude, with
increase of {{formula:32a68b6a-2756-4579-8272-8f4e13f8fccb}} from its smallest to highest physical values. It
means that the contribution of the heaviest {{formula:660d9fc4-629c-4f6f-bf2e-2... | d | debeb80ef70647fb2cc5149349946a28 |
taking the VEV as (REF ), which has also been mentioned in
{{cite:76eb09ab7b1d90e0f8783820dba18cd7ba64756f}};
rearrange the gauge field as (REF );
decompose four complex scalar (spinor) fields into eight real
fields.
| d | 62f3313cf3f2462387e2831c7761497c |
We consider the problem of rtMRI synthesis as a sequence-to-sequence problem with input text sequence of length n and output rtmRI video of shape n x 3 x 64 x 64. As shown in Fig. REF , we use a transformer architecture to represent the input phoneme sequence. Transformers are an ideal choice since they have been shown... | m | f1fbf61649c8ee30c5fa001d7db1f6a3 |
Uniform stability is a representative technique to derive algorithm-dependent generalization bounds based on the algorithm's stability with respect to perturbations on training data {{cite:6348d13f42f48c3127c057db54cc62f129718b2a}}. One problem of the application of uniform stability is its dependency on the {{formula:... | r | e39d2f1f0a1f5a00402ed6982200a3d3 |
The embeddings themselves are character-based, trained using the skip-gram negative sampling method with 50 negative examples per observation and trained for 20 epochs. Implemented using the fastText framework {{cite:3f35274296da8c713788376613463f6af44a61db}}, these popular embeddings have the advantage of being charac... | m | 46d30087fd614fad9b7dcfff73460289 |
We implemented several machine learning algorithms commonly used in the field {{cite:a0cb37724207eb3bbb8e9369937a3732cd66c98c}}, {{cite:34efdeb6dc35d50bba9d28679ceae97efa4f93c3}}, {{cite:056555bbd24a7985073873fa67a460f4143e09ad}}, {{cite:aa961311f13eff186ea1b6efc9cc182976e7fb1a}} from the scikit-learn {{cite:fc10c68548... | m | 924a9cd55f94ef0f1ab404d2475fef36 |
Recent deep learning applications use a semantic distance metric, which enables applications such as face verification {{cite:4bb0700ec9320f374c265ce9b211bd0c86b0774c}}, {{cite:de6a53da75a6f7d06ea56a6ec3b821bb3e897ca6}}, person re-identification {{cite:c905b3df443dc4d2cdefc479537a3196dc7b9975}}, {{cite:a3c578a7ce2a53dc... | i | 84bb537f247397347aa12261f34c3e5d |
Using (REF ) directly as a boundary condition is the classic Nitsche's method {{cite:284cd81d9e5afda88680c0ae811ed4c9d5b2d49f}}. Multiplying {{formula:1d635e1d-2e14-4bf5-aa06-e88fb0976153}} on both sides of equation (REF ), we have
{{formula:42af3b65-cf8b-4019-89bd-e9dbfd61b436}}
| m | 55a0c46f31d28696633c28fef38f23c4 |
Very high flux emergence rate can be used as a precursor of strong flare activity of an AR in the future. Our estimates suggest that flare-quiet ARs do not exhibit flux emergence rate higher than {{formula:a3c085b6-4d08-4411-9194-f59b6e2ae224}} Mx h{{formula:37ea6712-cada-4ae6-b958-4cb805191e7d}} .
However, the number... | d | d473efc7be0cb250f8a4bc8d0a7ab248 |
Our results, {{formula:3f96c1cb-6c4c-4cf5-a989-0d52b8f30520}} , might be useful as a comparison with the fixed-node Green’s function Monte Carlo results
{{formula:a9cb32e2-1899-4fe7-898f-ce158f733c9e}} {{cite:2c49fa31a202141f421ab61029e5f531783b0570}}, {{formula:9888735b-c32d-435c-8833-1e9f15b74960}} {{cite:ab1794199... | r | 2d42c2332ec88d5cf2b3ec133c6da47e |
FMNIST Only DCCS is able to surpass our method's ACC and NMI performance. We emphasize that using data augmentation in DCCS causes a significant improvement, since selecting the right type of augmentations for a specific dataset can reduce much of the intra-class variance. However, augmentation with GANs is challenging... | r | 932c70282353a3dc1c03b8f1daf768e6 |
Faced with an obstacle in proving global well-posedness in case {{formula:438c7bed-d74c-419e-871e-af0cbe83fb5e}} or 3, a natural strategy is to investigate its criterion following analogous works for the 3D NSE such as () from {{cite:657de669fdc2de2a6ae2886671ff779d7426dd63}}, {{cite:339da6706f52cdaf78eec3db3537b2feac... | i | d07c631a778b515342346c27c47f4373 |
Determining the shape of an inhomogeneous object by minimal/optimal scattering measurements has been a longstanding problem in the literature with a long and colorful history; see {{cite:b27d2dc3c42196dd9b7e35d6dd6e3e7530da195a}}, {{cite:9aee92145e4875ade42a65fac885cdc84030bfb0}}, {{cite:2afab873576d8c043f8e0e8125d092d... | d | d1f5ec960e7b548156f73ba67639ac1d |
More about Big Model training.
We can see that resource requirements for Big Model have grown far more than hardware improvements over the past generations.
To facilitate the next major leap in model capacity and performance, it will become increasingly important to co-design training algorithms, models, software, and ... | d | b52e6007cb7665777e40750b2215bdea |
Traditional deep learning methods cannot be applied to process graph structure data, because of the complexity of it. Graph neural networks are the hot research direction for the majority of scholars. Graph neural networks can treat graph structure data as message propagation among the connected nodes and then the depe... | i | 79841a17147171f4dd943f26bb3dad34 |
The interactions in the lattice gas model originate solely from the statistics of a MSA. They are therefore not directly related to physical interactions. However, it has been demonstrated that the {{formula:e964c2e9-2bf8-4532-934b-fdefd86c66b5}} matrix as used in this study is a good predictor of physical contacts in... | d | 5ba45b56787efee940eabc64e1714412 |
In the case of SDSS J1212{{formula:7dc92c8f-b2d5-405c-bf89-4d81fa499cae}} 0136, early studies suggested that the brown dwarf was under filling its Roche lobe and was more likely to be a PREP than a LARP {{cite:172bf1985859ce169aec8eceeabca442639887c1}}, {{cite:076a55b20cbe7f8df302396479874433554fa14f}}. However,
the X-... | d | 4add48c9c688671d08e379bc699d3c34 |
where we introduce a coefficient {{formula:b1c5fee8-d334-45e7-8ce7-2f8bd7f36261}} , if {{formula:38fa05f5-b6e6-4398-910e-53bf337a4b5e}} ,
the QCD sum rules can be saturated by the scattering states
{{formula:73a3e98f-94ea-49e6-83f9-6778923b789f}} and {{formula:3b8f160a-922a-4a9b-8ea8-026f0bc13d20}} , respectively.
The... | r | a70a6d9d0f6de6decc6955dc02c886cf |
In conclusion, for the first time we have shown that the anomalous intracellular transport of endosomes is described the spatio-temporal heterogeneous ensemble of FBM motions. We find that endosomal displacements and increments both have strongly non-Gaussian power laws distributions. Analysing local endosomal dynamics... | d | ecdc2408cf96708a951d84b182b4ea64 |
We present the results of SeTHPose (both SeTHPose{{formula:28a59e4e-1997-4842-a9ce-4b1996df5fe1}} and SeTHPose{{formula:ce1926ca-cd7c-48fd-a4f5-9895685d9cb0}} ) on MuViHand dataset and compare the performances against {{cite:ba5162179ffd263f162e0892b23916b6e59a0ece}}, {{cite:1d66ca745c35b3027a4a9f5383cb4d9659ffad99}},... | d | 3926cc90c105a829728c8f6deaef781f |
A deep neural network method is proposed and verified to approximate the solution of the Stokes' equations.
The method makes use of least square functionals based on the first-order VPV formulation (REF ) of the Stokes' equations as loss functions.
It has less regularity requirements on the solutions compared with othe... | d | 810be3cedc0a4d379bd23e8134f16442 |
The evaluation metric for the Toxic Spans Detection task was an adapted version of the F1-score {{cite:b325f493f1d9442bd20252210e748483a1995df9}} that takes into account the size of the overlap between prediction spans and golden labels.
| r | f160cf98f68c8542322929e59fd62389 |
Natural Language Understanding.
We evaluate the performance of PAD-Net for MoE on various datasets from the General Language Understanding Evaluation (GLUE) benchmark {{cite:1bdb9a4a750bb67e63058126cb2a7096913225d3}}, including linguistic acceptability (CoLA {{cite:a0d2bbeb5ec7184f128a913c94e4e857699f3e80}}), natural ... | r | 793738efa22362abb38fbfd9782b8b22 |
The generalists: A fully-supervised framework for training SE models defines a large set {{formula:8a4ecfd8-183a-444a-a01d-e6162f123f8c}} of clean utterances from many anonymous speakers. They are mixed with various noise signals sampled from noise corpus {{formula:90e48f38-7e46-417e-9b2c-bcaee4e133d5}} at random sig... | m | 8f8a2440e07d6a38603a1fb0ccf564d8 |
Several variants of the linear ICA model have been applied to fMRI data including square ICA (with equal number of sources and sensors) {{cite:03621595bad11d824d129ad3e580b28bee6bec4e}}, non-square ICA (with more sensors than sources) {{cite:591ee423463e3c66d81781809b832359dba47fe9}}, and non-square ICA with additive G... | i | ae4b28d7ba4d8d09531dfdcf43466f2c |
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