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However, it is clear that semantic shifts are not always accompanied with changes in word frequency (or this connection may be very subtle and non-direct). Thus, if one were able to more directly model word meaning, such an approach should be superior to frequency-proxied methods. A number of recent publications have s... | m | 0f8446199c41c9b421b1c5661562471b |
Long-range multi-particle correlations in the final state have been considered as a signature of hydrodynamic evolution of the strongly coupled quark-gluon plasma (sQGP) {{cite:560908a2d9deb141a683d76e28d2d3816af73e20}}, {{cite:b3b6e8eb3a3f30f4f82d82c593724c33fcf1cfa4}}, {{cite:9b45e3001552e5d0de2fb5d3cecc2cbfccfb9c98}... | i | abd36ec7757844e6f8d109e8b57ac625 |
We remark that the space {{formula:422b0e53-bdd4-432a-a907-67b3bd347399}} is different
from {{formula:b69afc8a-39d7-4d8e-b1dd-64e66d9fb169}} introduced in {{cite:b79b69f6c83fadece5daaf28ad6bb0a97452c17e}},
where
{{formula:804c12fd-90d8-4921-a38c-3e90c8eaa550}}
| r | 1f12803eb2a25a9e8464a9d1249edff5 |
It was shown in previous works such as {{cite:17bba087d0c326f385573b7a9cd1222fd55c29eb}} that self-supervised learning usually benefits from longer training compared to fully supervised learning. In Table REF , we show the impact of different number of epochs for pre-training our model. We find that the accuracy improv... | r | be50f437e2a142f5316d08cc1c9cfa6a |
Wavelet analysis. We utilize the wavelet analysis to quantify the periodicity of the flare microwave emissions. The core wavelet software is provided by Torrence and Compo{{cite:59b9c6a6a7c259278e6ea38921cd28ab76090613}} and available at http://atoc.colorado.edu/research/wavelets ; the code for fitting the background p... | m | 4c6246d0041934477216b8a783938527 |
P()=23-6+E ,
where {{formula:4e54e2e1-158f-4b03-a5ca-d12dc52b10ab}} ({{formula:605fb3e3-fbe6-48b0-821b-583a272f2a83}} )
are the roots of cubic equation {{formula:a2f5918a-13f6-4b42-aa16-ab5936e67038}} .
The first integral in () is a table integral {{cite:561df22d5a3bf2a19cb2bb1b528a03985efe916f}}.
The second integ... | m | 80050a7006ece6f53def7fd424cf71d7 |
The deployment of FL over real wireless networks still faces significant challenges, among which the communication latency is becoming the major bottleneck with the rapid advances in computational capability. Considerable researches have been devoted to address this issue for both analog and digital FL systems. In anal... | i | 2db690bb101359e59755c1d82d57e043 |
Diffusion models belong to a category of probabilistic models that require excessive computational resources to model unobserved data details. Their training process requires evaluating models that follow iterative estimation (and gradient computations). The computational cost becomes particularly huge while dealing wi... | i | f418d9a4d1b0582a5cbe39c447da10a3 |
For the basic inequalities, like {{formula:6bd52bdf-b1a1-4cbc-ac45-64cca9d3d1f3}} and {{formula:878b8d21-ce26-4359-98b0-4662bfd13ebb}} one may look at {{cite:e8aa68ff073ef0735aa2a58e9e5fafc22790710c}} for a reference. In this article we will prove a very fundamental inequality in both discrete and continuous case. We... | i | a78560890b040e82ad70deef4b95c184 |
A common problem in objective metric-based Single Image Super-Resolution CNN models is the generation of over-smoothed images, regardless of high PSNR values. EnhanceNet {{cite:dc5b5b6c216e635cd7e40dce4371e801ab1660cf}} aims to address this problem by focusing primarily on generating realistic textures along with highe... | m | 7798ca9fd6297130762fcb2a95f4aaf9 |
Independently of the theoretical predictions of {{formula:9c3f7916-60d9-4014-a440-82e5d5690cea}} mentioned above, its value will be fixed definitely once the axion dark matter is
found in the future haloscope experiments, such as
the Axion Dark Matter eXperiment (ADMX) {{cite:3d417c81ff589ce1ca61377381771802816d4316}}... | d | 43cb8f8b79116b079b371b349ce59ac3 |
In Fig. REF , we illustrate the effect of correlation on the CSP for both scenarios. For the random individual mobility model, we have assumed all the mobile users move with the same speed {{formula:d10950d8-7e68-46f7-92e7-a2785d146d91}} , and each mobile user moves in a random independent direction {{formula:5dfa0a1b-... | r | 037bb108dda157f963aec2fac6c22fb4 |
The results of score-based attacks on MNIST and CIFAR10 are shown in Table REF . For ZOO {{cite:aaeb18c2c2d3ceeb262a610a0383282644f10fe9}}, the maximum number of iterations (Max.iter) limits the number of searches used to perform gradient estimation. The larger the value, the better the approximation. Therefore, a larg... | r | 150e5d4ffcac098eb23cab948031eb1f |
To train a compact model efficiently, knowledge distillation {{cite:8255348b19165b1c3ec4c7e07ed25972029c393d}}, {{cite:d71c72e626218cdacbaafeed9dc85f1ebfee506a}}, {{cite:7c85c57c0bc543f62db3734b22de74ce770046fe}} concepts can be tailored for LFM based neuroimaging. Taking the depth localization of neurons using LFM for... | d | 6e2cb211ff7a24cf9ba7282ae14a005e |
Apart from the results of interval analysis, we use the following results from classical convex analysis throughout the article.
(Projection {{cite:da02f386272af681b93d6d088a8792d873ca3a52}}). Let {{formula:45e287cb-c134-451f-a8e1-aae362314f7b}} be a nonempty closed set in {{formula:a6192403-b7a2-440d-b36e-454bcc1517... | r | da20ce02dce4dcac5125c4284c791c94 |
We assign these categories based on (textual) semantic relatedness of the phrasal modifier, and numeric proximity of the count.
For example, regional languages is likely a subgroup of 700 languages, especially if it occurs with counts {{formula:2390f874-d9bf-4314-8731-66db50c1b8ff}} .
tongue is likely a synonym, especi... | m | 6bdde66cf1ee9f8a6f2b900b68cd7b85 |
In practice, we rely on a modified U-Net architecture {{cite:e1d163843ab5d392c6a15926ef17ea5be2741684}} and on an extended 12-class density grid that improves the density resolution compared to traditional BI-RADS classification (4th edition). Compared to the state-of-the-art, our classification and segmentation scheme... | m | 647474ebffb529fedcaa3b8827644b62 |
Radiatively driven fluid jet in relativity has a very rich class of solutions. The `e'-type solutions
may have one inner-type sonic point, multiple sonic points, and shocks. While the `f'-type
jet is a low-energy solution, such solutions pass through the outer sonic point.
The radiative driving is the most effective fo... | d | c546cc5db4efc3a88553699f3d1d9569 |
where {{formula:6a4b26b9-e628-46d8-bf07-ec80086554a2}} is a normalised bond vector (tangent vector).
For the mutual (steric) interaction between the beads we used the Weeks-Chandler-Anderson (WCA) potential{{cite:7a8f5ca359f563ba9eb7e2ead59295dc3cfc7734}}, which for bead {{formula:03002de8-7972-4218-b6fb-f6536abcfe57}... | m | a65510783cfced81503f8094711c545c |
Moreover, to showcase the massive level of transfer learning that is happening via the pretrained models, we will show the performance of the three networks (CNN6, CNN10 and CNN14) without the use of a pretrained model, that is, initializing their weights at random and not making use of the AudioSet {{cite:b5414bb3f622... | r | 833f732dddbc6cea5efbb3e136c5695d |
Reaction-diffusion systems arising in chemistry often have temperature sensitivity, both through reaction rates (such as those arising from scaling reaction terms with temperature activated Arrhenius parameters {{cite:3dc95af74a45e8059a87a2e87db82c8d95212adc}}, {{cite:806aafe54f820ae49e573c76e20ef3b49fa73986}}) and tem... | d | 8cb1ceb2f757eae6ee92dc31625ebf22 |
In this study, our models are compared with state-of-the-art multi-task methods, including
three aggregated loss optimization methods, GradNorm {{cite:b33c16924ae98a17572c2d74cd81cd5c7c2dc872}}, MGDA {{cite:d8b457f46a23631bcd13933d44a61e4a7edf1e5f}}, PCGrad {{cite:2a01359c3da61bda04b19e7b96ac4838bef33d9e}}, and one par... | r | e632eb903e027c40ec8cdd6887647c93 |
Inspired by difference rewards {{cite:4eaa53ca9985db14d407da8fc7d778f774fb589c}} and counterfactual baseline {{cite:cf17877389178b74c9e37d7aa0a724f8431019a1}} for policy gradients, we propose a counterfactual assistance loss. For each agent {{formula:fd2af9b0-9f50-4f93-a137-2236dbefa14f}} we can use an advantage funct... | m | 72347a94b21712fa08603ec4871bdfb4 |
Camera calibration, or camera resectioning {{cite:9aede1ac000df48b1dc10a7fe3d3d63e9bf31736}}, is a process of retrieving camera intrinsic and extrinsic parameters.
Usually, the intrinsic parameters include the focal length, coordinates of a principal point, axis skew, and distortion coefficients.
Extrinsic parameters c... | i | 087e92586e7e61934f8f876cd5c4e8d0 |
The se method, based on the modified equations (A3) and (A4) for the
estimate of t{{formula:070c6ca2-2ef1-4403-aca6-b1be6c3e4f0d}} , which includes the logO{{formula:a1fb7bdc-d9a6-4c16-a56a-e05d2eed2fce}} term,
allows us to remove the systematics inherent to the original se
method by {{cite:3cdb8892a2c8d96cd1ae01db6f6... | m | 115700d9fbfac2e5fa056c0d2690d0ab |
Results of the two scenarios under study indicate that synthetic data can prove useful for training DL models, particularly related to UAV-based aerial imagery. This evidence is backed by related work, listed in Section . Nevertheless, we need to be cautious with these indications, because the DL models were optimized ... | d | f6950be1e8d01b8e80f2939d7c553631 |
Why ViT is better than ConvNets? From the results, we can see that the Swin Transformer{{cite:7ed88d6969ec0b8c8b0c534f994045b14dd469a9}} based achieved a significant better result than ConvNet based on both with simCrossTrans and without simCrossTrans, although they have a similar computation complexity. In {{cite:5342... | d | 6dee6ca58c63f7a1751b1d08730c31ad |
Discriminator (D) Network:
We further use a PatchGAN {{cite:2dd647090fda29d180b896eb5e3ec2732d927a93}} based discriminator network to distinguish foreground and background on a patch with scale of {{formula:12615a54-803d-41d0-89d4-ab8b070f3c33}} pixels. The proposed architecture is shown in Fig. REF . It is designed b... | m | 23a82705b442d0f394f8e58e1eee22bf |
Surprising observations produce filepath and emulation models. Both models individually perform relatively poorly, especially if compared to ember FFNN. A potential explanation behind this observation is the ember FFNN training set consisting of 600k feature vectors from the original Ember publication {{cite:22e26955b6... | r | 7830a5602e457aefc9ec6503f661cdde |
We observe that most existing methods are trained on static images.
Directly applying the image-based models to videos (image sequence) might lead to unsatisfactory results since they fail to consider the temporal consistency across video frames.
To conquer this dilemma, a large number of approaches have explored utili... | m | 9022e0d5df3ba205e0e689991b51f872 |
Quantitative Comparison. The comparison between our PoseVQ-Diffusion and the competing methods is shown in Tabel REF . The row of PoseVQ-AR refs to the vector quantized model with an autoregressive decoder. The row of PoseVQ-MP refs to the vector quantized model with the Mask-Predict {{cite:37dadd89b4916cf71f9cf9ab9692... | m | 759649958ef3ef7d4f372193fa49e5c1 |
As pointed out by one reviewer, the original envelope formulation uses a decomposition of the variance of the error term. The independence between the material and immaterial part is only guaranteed under normality. The null covariance only guarantees that the information of {{formula:1e2fbd28-510f-491c-9762-7ef31d713b... | d | a5776cf80c1702905f79962f4b0c92de |
Several previous approaches in the DSL shared tasks have formulated the task as a two-step classification, first identifying the language group, and then the specific language {{cite:21ec399485a5bb9a53d88b86ccc015e5d090a475}}.
Instead of taking this approach, we formulate the task as a multi-class classification proble... | m | b740ccd7f2bd9b33754d0b0132d0d30e |
Traditional GOF methods, mainly inspired by {{cite:3bdbed92a61447ae22a888b153f54512bf30c13e}}, construct orthonormal polynomials on a case-by-case basis for each parametric discrete distribution {{formula:9cbb17fb-02e7-48f5-aea4-ea52e59fc534}} . This is generally done by solving the heavy-duty Emerson recurrence {{cite... | m | 2b99ea0d2ccb479ab9b80b0613a6650d |
Optimal control problem involves finding controls for a dynamical system such that a certain objective function is optimized.
Traditionally, most research for solving the optimal control problems for non-linear dynamical systems uses optimal control theory which, in principle, finds optimal control by deriving Pontryag... | i | 7d675db384a3f1b558debb0b9e4196cf |
Let {{formula:fd684254-ca73-44f5-aeaf-027b3cd29e45}} be a simple, connected, finite graph with vertex set {{formula:4e40e94c-096e-423b-a7a8-3c92a51b2da2}} . Let {{formula:7331b3b0-05d8-4147-be41-ff50be82baf9}} be the {{formula:2031e481-e284-4458-b8a6-bdf40a0044be}} -adjacency matrix {{cite:dd729f4cbd6d149d62196ece655... | i | 28fa9f6ea2346207c0420c05763b1fc6 |
In Fig. REF we provide some qualitative examples,
illustrating cases for which the QB-Norm model correctly retrieves
videos that are not retrieved without QB-Norm.
Examining failure cases, we found qualitative examples for which
the retrieval ranking produced with QB-Norm was more “reasonable”
(as shown in the bottom ... | r | cc572081b7d75bb3b93feaf768289b92 |
(3) TMM {{cite:6832f008fde0c6775fb4e6b31852e430d22d683c}}. This method supposes that all data points are drawn from a central Student’s t-mixture model (TMM) and utilizes the EM algorithm to estimate the TMM components as well as rigid transformations.
| r | 4490e7abd67d4b36bebb588a7220a40c |
Discovery of the giant magnetoresistance{{cite:cb80535cb95cfd7a18797c43d5f3b96a8ab39564}} greatly facilitated the longitudinal magnetization, enabling to accelerate the development of high capacity magnetic storage devices. In 1960s, the inverse Faraday effect (IFE){{cite:e0d567aff227188aba255e12407acddc6c8fc5d5}} was ... | i | ebf41164491b6017a66d85425ef9ee89 |
The hypothesis that the starting point of iterations {{formula:6ea0f255-60ee-42f5-b0ab-1ddc9c5db61f}} is such
that the ball centered at {{formula:8616a282-5ee3-498a-98aa-b6a2ea88f974}} with the radius {{formula:e2e38c27-5506-4569-a3c9-7c0189606c2a}} is contained in {{formula:35b851e2-7b04-4a04-b9ab-21b175d79de2}} ... | m | ca39d6d5a8c4c9ea922055dc94a689b9 |
Defense Algorithms and Mitigations
Mitigations of poisoning attacks have been studied extensively in recent years.
As the major focus are put into neural networks {{cite:a1ca2b6731d63354cc8087f07a0b756dd389e149}}, {{cite:c84916f8ba824491de77b4e77ba357bc739a239a}}, {{cite:18f411c60733e478c5376e028c1bcbf713dc3651}} and c... | d | 969db368acf0a47972a94db50901e778 |
A method for unsupervised deep-learning image synthesis of medical images has been presented. To synthesize new intermediate slices and thereby recovering spatial information, the method exploits the latent space interpolation ability of autoencoders. New intermediate slices are generated by mixing the latent space enc... | d | d5eed94483de44a0906e623329f593f7 |
When trying to understand the content of a video, it is essential to consider the video from a wider perspective to learn contextual information. To achieve this, some prior works {{cite:feafcb3899b790178b32d245d7e202ee8d367222}}, {{cite:f002aeb473a6c6c4010b7e4546c012d3668e7ce9}}, {{cite:323f4e9e8cca616a422d7e96c430f34... | i | f92443544b6d533cae2b1bfa98d6375d |
The proof of this theorem, however, does not use the explicit formula of the operator {{formula:7d7d77c4-ef6c-4451-939a-6feda658fcb2}}
and relies only on the fact that the operator has orthogonal polynomials as eigenfunctions.
The operator {{formula:ac5b8c6c-7efe-4085-a273-8097b061151c}} in (REF ) contains a factor {... | r | 07c859d0186aed1d47b33a58d156670b |
There are diverse number of studies utilizing quantum CS or alternative approaches in more practical manners for further reducing the number of measurements and the required resources such as online learning and shadow tomography {{cite:006ff258d6572a46b13e36269f5b0d58ec9d1e8b}}, {{cite:759fafdef7ef4d333c389ace689b4ff8... | i | a66e54a3121bdb07f3f0e4e10e259e3e |
We report results on the same 10,000 instances for each TSP size as in {{cite:6c9410d95707617f7c1cb097fd2b609fcc981e83}} and rerun the optimal results obtained by Concorde to derive optimality gaps. We compare against Nearest, Random and Farthest Insertion constructions heuristics.
and include the vehicle routing solve... | m | cf06d63fa5d258aa9ca08c5317d703ea |
This problem is well suited for an interior point method {{cite:0be63faf0dbc45bbbdb6a53a698e6b8060eaf4e3}}, {{cite:71b47dd9d2140cbddcbb33952c2eeccfebde9eb6}}, {{cite:e11ab1df1a1d4abd8d21242100897c7d5bfe8a8b}}.
First, we relax the inequality constraint {{formula:e603dea0-ff38-4179-b551-ed0368582222}} via a log-barrier ... | m | e88eedc44de080cdd54a0507a4ac800d |
Motivation offers a framework compatible with other methods in machine learning, such as R-learning, goal-conditioned RL, and hierarchical RL (HRL). In R-learning, {{cite:bba6b01a5451a6d4b4d5f1cffea899f47ffbf75c}}, {{cite:fa766842c2f67566fd378f11a5b781b713649d13}}, the cumulative sum of future rewards is computed with ... | d | d415a64886e24fbca1f0a53fac47fead |
For tumor section, the techniques adopted to detect tumor inside WCE images are mostly based on SVM {{cite:b689f1cba4f92bad99e14c5d8dc9f16a82d61ef4}}, {{cite:12961dfe4b985db0793bfee42c46e017cf27c9f5}}, {{cite:33ff10f932f0a42aa46439fa6b8b09ff16e3e20c}}, {{cite:9dc7e6563a55b9d04a5de1fe45df8c6eb572fd7c}}, {{cite:3ed5d138f... | d | 6ace4e20d40368380dad2eec9c8ab1fa |
As mentioned in {{cite:573ede106e9e9007cbf4b6ba3c7d004037c415ee}}, InceptionTime networks can exhibit high variance in terms of performance between training and therefore can benefit from an ensemble method approach. Consequently, this work also considers an ensemble of five networks for mood-state classification for b... | m | f062826ad71c660c50d7fd9475ddc36e |
The following moments bound on Gaussian mechanism with random sampling is proved in {{cite:088fc87c25b0e04f1bf0a5ef470627663d807b93}}.
| m | c4879a8f9c73721b3e1af1bbf9172357 |
In all three cases and all datasets, the posterior of {{formula:55050add-9c2f-462c-b15e-4980afe12d1e}} has two distinct modes, namely, the SI mode characterized by a larger {{formula:813b19e0-d9ab-4df2-88df-4d2d2c9a2484}} , and the MI mode with a smaller value. The origin of the SI mode is explained as a result of de... | d | 14011c76c3d5d123cc6a869f98ea0450 |
Timing and speedup.
DeepTag is implemented using PyTorch {{cite:5ce066abe6b25130559ba737fdb6b6899c3f82b2}}, and tested on a PC with an Intel i7-11700K @ 3.60GHz CPU, 64GB RAM, and an NVIDIA GeForce GTX 3090 GPU.
CNN prediction can be accelerated with GPU while postprocessing on CPU.
In Stage-2, multiple ROIs are parall... | d | dd11017c9e32ca8d1be6dc5abea4d2c9 |
Besides, our framework mainly consume time on the bucket statistics aggregation, best split finding, and preparation for data to left or right branches. We only take into account the time consumption of MUL because it is the main computation. Our implementation of MUL can execute element-wise multiplication, thus is mu... | m | d34986d2f88dde4097176233d00fb5ac |
The benefits of label smoothing have been recently highlighted by {{cite:3ce51e327567453570a1851075bd68ac016b2a55}}. We were able to shed light on a severe limitation of the approach, which practitioners were currently unaware of. Similar to LS, other easy to apply calibration methods are also damaging in practice. We ... | d | 682fced7d21d7451acf008441b772115 |
Nevertheless, one can test the effect of the HOMO-LUMO gap opening in the absorption spectra of a finite (5,5) CNT using the Aharonov-Bohm effect{{cite:cfe3f685f221efa7524fb6efce4f16543fe93271}}.
For that we add to the normal DFT/TDFT calculations a static magnetic field parallel to the CNT main axes with a value, for ... | r | cb8975d8b5e2c9017bb7034ec42648a3 |
[Proof of Lemma REF ]
(i) Suppose that {{formula:5e4ac122-13a0-4ed4-920e-492820094ab8}} is bounded below. Then the operator {{formula:eb104a7b-07d0-408a-8763-ea6982318637}} given by {{formula:79695bde-9183-498c-8537-1ccfd75a16ba}} , for {{formula:6c05559b-33ec-4294-b574-c1a0bac62539}} , is a continuous map between Ba... | r | d46b58e115d7fd5359da5d2b13c5a360 |
Symbolic dynamics associated with horseshoe-type structures:
In connection with the previous discussion, subharmonics can be also detected by
applying to the Poincaré map and its iterates various methods coming from the theory of dynamical systems, the most paradigmatic being the celebrated
Smale's horseshoe (see Smale... | i | 04ec843e38f4147fdd19f67a9fc6d17e |
More specifically, we show that the stiffness maximization problem of
trusses subject to unilateral contacts can be recast as a
second-order cone programming (SOCP) problem.
This is a convex optimization problem, and can be solved efficiently
with a primal-dual interior-point method {{cite:b505986b08cca5b1a470ac2baa0ba... | i | 02f63de9dabc66d6b598431096525272 |
In this section we compare the proposed approach with t-SNE and U-Map. In order to provide a quantitative comparison, we cluster the output from Sections REF and REF using K-means and compare the results using three clustering performance evaluation metrics, namely
Purity {{cite:0a0c742f5858fa599f8aae6d02743e5ba9e3f9... | d | 76e91686248f1d2c78df7033aa121be8 |
Automated WS (AutoWS) methods are a best-of-both-worlds solution.
These techniques generate LFs automatically by training models on a small initial set of ground truth labels {{cite:696b7fb995275743e850b10bf600a94bb1d7634d}}, {{cite:c22eacae22ec5e04fd652bd963f04c3e709a7d27}}, {{cite:c7a4f1d2fe44235398560fdfedb78446ca01... | i | 6e9a129ab4a328514d5a1454f188c85d |
The goal of the Retriever in Retriever-Reader type ODQA systems is to search and find a small set of relevant abstracts which are then fed into the Reader. Therefore, the performance of the Retriever influences the performance of the entire system significantly. A variety of methods ranging from sparse lexical models t... | m | 1e2dbf69e3703a7010d636fda448b6dd |
We use the Dice Score for our our final evaluation on the extra testing data, to be notified, all of the testing data does not join the training process. For the comparison, we run five settings, the source only: model is only trained with annotated source data; Adv UDA represents the method of {{cite:3e4c54c2bac620af1... | r | 8de1198059cad1ca8a478c794fce8fa7 |
In this section, we explore the performance of the detectors derived in the previous section through numerical examples. Consider a narrow-band MISO communications system with {{formula:c9358cb9-8b7c-4ef5-9df6-8e9bc8327571}} transmit antennas and a single receive antenna, whose carrier frequency is 900 MHz. The durati... | r | 178e105ae3c20b1de15287cc31f9ec28 |
M101 was chosen for this survey because its nearby distance enables its properties to be studied in detail {{cite:29d51e6f39c83cab47dd06780c0cf6d8954d54c5}}, {{cite:2a48cdab15f19be1459a9bebc2b9d7b119f7fda5}}, {{cite:f7c4bb9116c34659b9648b5dcf6e9d2bff481b23}}, {{cite:5dc0c7e973cd0eee126b2ed73ff68c6464e98837}}. M101 is a... | i | f2318d156126524c850fe0cd3c556baf |
In this section, an overview of spectral difference methods for fully compressible Navier Stokes equations for an ideal gas is given. We begin with presenting the governing equations, followed by discussing the discretized solution evaluation methodology specific to unstructured hexahedral meshes with straight-edged el... | m | 6f70afd7f69dee3332145fa7a95c321e |
Imitation of human behaviour was central to our training approach. Unlike other domains, like language {{cite:db51296f9fe0254250726391baf0e4deae365880}}, {{cite:78cceed3db55ec9fb3240d8cb281bd251d28e7e3}}, {{cite:1c3ae557c54f7604190b95402d577597f00a25bd}}, human behavioural data in 3D simulated worlds often needs to be ... | d | af282175d57c9e5a6d8a9bfea6fdb984 |
Spin singlet physics in 2D systems has played a large role in cuprate HTS, and similarities of nickelates to cuprates have encouraged extension of this concept to superconducting nickelates. The dominant concept has been that of ZR singlet {{cite:3b4ed36dfc5a17b2cb66ff48df0dabbaa3ecc2c4}}, building on the characterizat... | d | d02b7b8119b217c4bbeba599a25781e1 |
This paper focuses on whether the actual sequence of updated strategies converges to an equilibrium, i.e., the last-iterate convergence, which is inevitably a stronger notion than the average-iterate convergence.
A series of optimistic no-regret learning algorithms is proven to exhibit the last-iterate convergence {{ci... | i | bdd1c9672cce4ba859088be0f581d51e |
But sensitivity analyses predicated on such sensitivity parameters suffer from a number of shortcomings. First, users of grid-based approaches must explicitly state the granularity, range, and even signs, for plausible values of the separate (conditional) associations between the hypothetical confounder and the exposur... | m | 2b9cc8d02fb1ad8102e7c7afe7d9a1be |
Deep learning models are widely applied in medicine. However, these models require more interpretability to simplify the steps of the prediction process. Despite advances in XAI models, there is a huge need to consider XAI with DA and FL when the topics are related to medicine to achieve the goal of personalized medici... | m | 0240dab29a5c7b9988056485233f9030 |
After the decoding, the BS updates {{formula:7272d3ca-4d7e-4ff6-b148-53006bfa8294}} to {{formula:0d4d2b55-748a-4d5e-a7fb-ed4ca5765c2d}} based on its optimizer, and iterates above process until Eq. (REF ) is fully maximized.
This paper updates {{formula:810a9caa-0c05-4379-ac7d-c6ef2592f54a}} based on multi-start loca... | m | a449ee8e65791fdf5e04de6ea4421037 |
Sparsity approaches have proven successful, but more complex models with additional structure have been recently proposed such as model-based compressive sensing {{cite:ceaed1ef1a283f04791c3b467faadc9e33d0ab1b}} and manifold models {{cite:495325f7ffc376cc845390d5c4bfc8d726de188e}}, {{cite:4701553ca145fd8c66f984a3a2a1f8... | i | 7c87e64043edf930fb526dc07a3a15b0 |
We train two models for each calculator: one with lab values and one without lab values. Missing values are imputed using {{formula:27298837-220c-4370-a684-8b2f17685f9b}} -nearest neighbors imputation {{cite:52d44b8f5601d102bb0a1b271703de87b0d39720}}. We exclude features missing for more than 40% of patients. We train ... | m | 1d6b735bf95cdeae4036f76b148fd50d |
As regards the identification of the absolute and spatio-temporal analysis, the 1D velocity profile extracted form the baseflow at several streamwise positions is considered parallel as in the local stability analysis {{cite:2c654b67a8e90ce13ef28b7e8106710c972f1978}}. The resulting parallel linear equations are then di... | m | 3633f18973d8f66d0219e8a7ab420e25 |
Blind-spot networks offer a solution to the predicament of requiring noisy-clean pairs of training images for deep learning denoising procedures. Previously utilised in applications on the likes of natural images {{cite:ca35a2fb237ff0099fc487cc0838d4aa72ca8ca6}}, Computed Tomography (CT) images {{cite:72e0e8f0a53b2915a... | d | 0be873243adc21f67c94cfcb5ec16332 |
In this paper, we proved an operator extension of weak monotonicity. It is interesting to note that our argument also leads to yet another proof of strong subadditivity {{cite:5dfd4a118bfdae8a90e5de49c0d38d6abb3e7bd2}}. What is notable about this new proof is that the strong subadditivity is proved by first proving the... | d | 017cf7245382924f9d479fa93a50cefb |
The neutron pair-distribution-function analysis revealed that the local CDW fluctuation occurs at {{formula:ed074dc1-6d63-4ea4-aaf7-218bbf0e12fb}} and disappears upon further cooling {{cite:d827f6835318f7ee5ac85f0e1c9ea15322aa62ea}}. Temperature evolution of the electronic structure is shown in Fig. REF . The photoemi... | d | 05bbe0741cf4a5634a568b2855b8deb7 |
Graph Convolutional Networks(GCN) {{cite:67a68b79e16a28bbb1173888b61f35b353a0950f}} borrows the concept of convolution from the convolutional neural network (CNN) and convolve the graph directly according to the connectivity structure of the graph as the filter to perform neighborhood mixing.
GraphSAGE {{cite:ae5458b... | m | f9c3607f736227e4a3fb80ced87e9d52 |
These properties are reminiscent of the behaviors sought by the line of research on Dynamical Movement Primitives (DMP) {{cite:49ada89df42c9356679174a31324e8532a620733}}, aiming at modeling attractor behaviors for autonomous nonlinear dynamical systems. The essence of the approach is to define a simple dynamical system... | d | 24ee871bffe9b9616c15826d8ab8834a |
On the other hand, determining the order of {{formula:99441b03-326d-4b4d-8d32-fc52f243e7e3}} -cables of a torsion knot is much harder. The only known result of this kind is given recently in {{cite:e51bd62cf731dcfdb5d994501e6b49b3af78b766}}. They showed that when {{formula:3c337fe7-8beb-4e55-9345-3e792a078383}} is the... | i | 7793be3e628165a51d284b3897cee588 |
In this section we have described the performance of the Pre-Trained (PTR) model on the datasets described in section REF . The model is pretrained on time direction of subjects from Human connectome Project (HCP) {{cite:c486b97680eeba654d02bf6c833407017b6d9e93}}. The pretrained model is then further trained on four da... | r | 209ec5a6eab4c882e55b0b1d7610d001 |
In this paper, we have proposed the new kind of the Chandrasekhar transformation for the Teukolsky equation
in Kerr spacetime, which reduces to the original work of Chandrasekhar under the limit {{formula:ec3d1f7a-47bf-430d-a7ef-a8aba63b8426}} .
The original Chandrasekhar transformation had been obtained from the
point... | d | 7de0cd79c79908c1da3d5da9d5556f34 |
CIFAR10 images were also rescaled to the range {{formula:e590a74a-74b2-4e36-9200-1197d0a6e1cd}} and normalized using the mean and standard deviation of the training data set and augmentation methods were applied during training. For CIFAR10 data set, Resnet 9 {{cite:29738695b1f552acea578664aaa65bf16e8320df}} network a... | r | 9bf3ba888dc5700e470f239b3bacd29c |
Mahalanobis distance (Maha) {{cite:03691e4d3da8903ecd8b7c2f83427c3375e49685}} which measures the distance between the test input and the fitted training distribution in the embedding space. The training distribution is fitted using a class conditional Gaussian. The uncertainty score is the distance.
Relative Mahalano... | m | 6f0da43266c48dc5b715c684a64146de |
In Fig. REF we illustrate some of these properties by using DMRG {{cite:6d89e6bd94a9c51128433d11a86d3b1f6d8d5a48}} to determine the ground state and its correlation profile for several open-boundary quasi-1D Hubbard lattices with {{formula:6b90ce26-f2a1-45ab-8c2c-79bec4fa8517}} to minimise any boundary or finite-size... | r | 2c8f1bc0d34676abbbd1b370f50c7f96 |
Theoretically, in addition to {{formula:75bb8e0a-1c69-4eca-bce4-0e4d2b7d7a79}} , it has also been possible to obtain the spatio-temporal behavior of different thermodynamic quantities in the hydrodynamic regime. The variation of density, pressure, temperature, and velocity with spatial distance, {{formula:758ae57b-f478... | i | cd4c4f09076d692584c91bde8ae57f3f |
Our approach is depicted in Figure REF . Two losses are computed to optimize for both content and style in a coupled manner at each iteration. We build on the drawing representation and the content loss methodology of CLIPDraw {{cite:63b9518b2197240d7a64ba4bf7886457e086e5e3}} for which we include brief descriptions for... | m | db74abf89ae7473feb8c3ee822278135 |
In this section, we present some results that are needed to prove the main theorems of the paper (in Sections and ).
We first provide a block anti-triangular decomposition of Hermitian matrix pencils. This result can be proven in the same way as the analogous result for symmetric matrix pencils {{cite:123779c894cfdfc0... | r | 25dd0abf6764da673f39166f22165624 |
The flux drop in the radio or optical/NIR band usually associated with the hard-to-soft state transition is thought due to the jet quench {{cite:71067cccbd6c6b2cfa29f0ea21612ae70859bba8}}, {{cite:17f87b908d1723047b2081a9b5d3eadd4f870147}}, {{cite:cd0218d837ef5904b9e1f1f06f0db6b3fd2e783f}}. In the observations we analyz... | d | e6c6c18b1bb8f4d09d8ad43ba4ffd405 |
The direct searching for the right handed boson sets the lower bound for the mass of {{formula:f886f2bb-0dfd-4c3d-98a0-cc6218191376}} boson as {{formula:4b72f096-8adc-4baa-934f-2b6daa197f89}} {{cite:c015be4214067e79d58312139233b1dbe10d1203}}, {{cite:9701d094737e5e0846617bb8546e0ec45a855a90}}, {{cite:ad3ec88ee0fc5f1ff... | r | 792cdeaec93b2d4240b7c7ece11bf1b3 |
Through a Python tool, the memory gains of the proposed synapse-compression technique are quantified.
The tool calculates the synaptic-memory requirements of a given cnn for the proposed method, the naive lut approach, and the reference hierarchical-lut approach {{cite:095a8511c6b27eaab3ff915f7918e83e12aa5031}}, {{cite... | r | 03f5971d6eee842a455eee88a086a7eb |
where {{formula:ed61c79c-ec43-4fa6-abea-2b66c4137114}} is the vacuum pressure. From {{formula:607e8105-1541-4055-8826-9fb24860189d}} one can determine the energy density, {{formula:9d3e1f32-a93b-4355-8f6c-63156c71dcfb}} , the trace anomaly, {{formula:accd52da-c868-426e-9e48-745d5fdfc363}} , as well as the conformal m... | r | aae0ea4456509357e99e752d5830917f |
In December 2019, a severe respiratory disease emerged in Wuhan, China {{cite:f1c6685442f40c839ac5c9ef9a22ed735f65012a}}. Since the first identified case of COVID-19 was reported on 8. December 2019 {{cite:ef379ef5f6baf047350e10cf9a2e23755845ae93}}, {{cite:6b5fb07d361867e14b952027d52ddeae1a17f7d2}}, the virus has sprea... | i | 5d0cfe670d1be58a5b1c223d376a5ceb |
Human-made 3D shapes, such as chairs, are composed of a set of meaningful parts and exhibit hierarchical 3D structures (see fig:partstructure). Extracting multi-level part instances from the point cloud is challenging, especially for fine-level 3D instances, such as chair wheels. Existing studies independently performe... | i | c2fd304b072d1b0136625a0ecc9a37a3 |
Here we compare our method with textual-based methods XLM-RoBERT {{cite:0eec3e7260a920ac37dbe5903d6a1599156dd0d2}}, InfoXLM {{cite:8a0fd7bef719cd60d46970e5cb7c496923dc0dce}} and LayoutXLM {{cite:b00234f8b4f3ebbeba5ce8fb1c6339919ec7d306}} on XFUN. The results are shown in Table REF . From the table we can observe that X... | r | 98708b82e95b62b708be1051fe2d3e72 |
To introduce the notion of translations on the Cayley tree
{{formula:ea9e371e-2a66-4cf9-818c-3d5813dc84c8}} , one uses its group representation {{formula:ce8a50c0-5120-4eb8-9bb2-329f369acf6b}} which is free group with
generators {{formula:8766e901-edf2-437c-8ed9-6b36f5676ae5}} of order 2 each (i.e.,
{{formula:26de46a... | r | 2ef9a815afc83e6b533a1c2d3abcddae |
The nonperturbative inputs used in this work include decay constant, Gegenbauer moment and form factor. Unfortunately, for the {{formula:62a5d443-883b-4af1-ab6f-d2e703e34c49}} and {{formula:9636013d-254e-48f1-b221-94b18fb18958}} decays concerned in this work, some of these nonperturbative inputs are not known. The st... | r | 5d69c16736430ea526820f3af5d03be6 |
After computing the energy deposition, the ionization and the thermal balance
are solved in NLTE {{cite:d73a9249f9363174e09607ed6b753bee023e660a}}. Ionization is assumed to be entirely due to impact with the high-energy particles produced by the deposition of the radioactive products, while photoionization is assumed t... | m | 8263dc7e6833768c8e9a83eec5464587 |
As for the method proposed by {{cite:61ff1b7cd62578481851585e4381508fcec01e69}}, which we present in Algorithm REF , its second order Edgeworth expansion was discussed in Section . To make the statement more precise,
let {{formula:667ea1b9-3506-4772-8269-9b494cf0d80a}} be drawn from Algorithm REF , and {{formula:bbe82... | m | bc81ea85eb61421cce6656d0f2e957e8 |
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