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In this work, we propose to solve the challenge by adopting differential modulation at the devices and designing non-coherent multi-device data detection at the AP. Such design can bypass channel estimation. The implementation of the transmitters is very simple, where each device is assigned a spreading sequence and tr... | i | 02edf65bd41e07efb48e4ed554b8c431 |
SIMPLEX uses the simplex method of Nelder and Mead {{cite:018d16796a21811783be961d38ff90ea71711d1b}}.
MINIMIZE minimises the user-defined function by calling MIGRAD, but reverts to SIMPLEX in case that MIGRAD fails to converge.
MIGRAD, undoubtedly the workhorse of the MINUIT software package, is a variable-metric me... | m | d411d7a790044ea1a0d8e0c3403ee8da |
To apply the visualization tools described above to sparse functional data, we required an appropriate data fitting and depth for data ordering. On the basis of MFPCA ({{cite:4619bd3067f18076a1b382bba3c1f584f2e63cdf}} {{cite:4619bd3067f18076a1b382bba3c1f584f2e63cdf}}), we improved the fitting of data through the iterat... | d | 6a2b4491fb3c36d526128be2e9e11238 |
The final critical exponents ({{formula:fe734ed6-e96b-4ce3-ad3c-4e87335723ae}} , {{formula:c814d47c-5483-4f2c-8a77-77ee0294dbc8}} , {{formula:9d045912-484d-475d-b6b2-6f0cea2461bf}} ) are in agreement with those recently reported by E. Clements {{formula:e61af911-5c08-4b86-84a9-4dc02c938365}} {{formula:fe508e7e-0cc7-44... | r | be5b132252502cd9f9037e9ffec19d01 |
The Spitzer data show the presence of dust throughout the 2006-2009 period, even though the {{formula:9fc0dd79-8db8-41a9-aaa6-30d7066be64f}} m excess disappeared in 2007, only to reappear by mid-2008 {{cite:28edcc7a7a64674fd0f8881d0bf1e9602b893b20}}. This could be due to variations in {{formula:16cf62e6-1c7e-4aca-af68-... | d | 870527ac8f86906ff7099740fca63ca3 |
HMM-crowd. This is an extension of HMMs {{cite:41202de09dda5400c586398c9587f83bd0884ce7}} and LSTMs {{cite:0d3e62cdc9b7e726a28dc582e64c97986907e5af}}
in which crowd component is introduced by including additional parameters for workers reliability.
| m | 8e7f5e90843c562532afd7d8420f2ba8 |
Assumption 1
The function {{formula:579c11aa-3a42-48ab-9704-50a09fd09544}} is {{formula:a861f037-d877-4758-8ffd-fe727795e673}} -strongly convex, i.e., {{formula:d05e8673-af46-4696-afce-514c2ff38cde}} , {{formula:2cb7d880-cdfb-4b49-8d26-2bd1c629c40d}} , and {{formula:58f06abf-26be-4be6-81aa-abedd7cfa76c}} , we have {... | m | 3a66815a597f96afe0e9053a6a633f7f |
where GRU is a gating mechanism function in recurrent neural networks, introduced by Kyunghyun Cho et al. {{cite:e23fec3b11d73c8da2210845d0868bafd76af523}}. Another related approach would be similar improvements based on the LSTM architecture {{cite:16e97ca895baaeb7a536492219bc127e8c4916d4}}.
| m | 6bc209a63bc5053b0f36791de67958ff |
where {{formula:1692e9d2-f504-4db7-bde3-7a6411ae4a77}} is the training example, {{formula:1e468440-612e-4ac2-accd-92adc78e16d0}} is stochastic augmentation function reevaluated for each term in the summation, {{formula:eb2e542a-e998-4546-b6b1-df086e579ad1}} is a temporal moving average of model weights used to gener... | m | 190e6450f87ee3fa7828229c9a6701b0 |
It is easy to see that for pure states, the LN reduces to {{formula:b1ddda87-5fe1-4716-9de9-23b5d5b40e45}} Rényi entropy{{cite:ada0de5945e80f1efa4975dbc088bff7e2bbf098}}. It is worth mentioning again that LN is expected to capture only quantum correlations and has been studied previously in several works{{cite:7f4cad3... | i | 5bdc2edbee7cac21381177bcfd414e29 |
which leads to a {{formula:2c0f5c64-e739-42f6-b0bd-4d32c182abd6}} difference between the Planck result and the result arising from local observation. Since the Planck analysis is based on the {{formula:93294f4f-85fc-4222-af62-ed7a4fb173d4}} CDM model, the discrepancy
between the high {{formula:df10e936-8e2d-4fba-8d6d... | i | e08a11b2801e45b258620c957b14e3a5 |
vd Bergh-Hagen 176: Originally reported as an open cluster
in {{cite:7aa94e53db98fe5eb90dc5e6fab0bed4402ee2b8}}, this is a very interesting object that has been
profusely studied over the past almost five decades. Much of the interest
comes from the fact that it is still not settled whether this is a metal-rich
globula... | d | 332333128d802a820d4232289a27a5e0 |
Fig. 4 shows the NMSE of the cascaded channel vs. the bandwidth. The curve Method {{cite:a7b113811125413ef0924ea33c3689bf622c5f97}} denotes the NMSE of the conventional compressive sensing based method proposed in {{cite:a7b113811125413ef0924ea33c3689bf622c5f97}}, which assumes that the range of equivalent angles is {{... | r | 19ca4abeea52c12854348a64482067d2 |
Can the logit normalization improve existing scoring functions? In Table REF , we show that the LogitNorm loss not only outperforms, but also boosts competitive OOD scoring functions. Note that all the OOD scoring functions considered are originally developed based on models trained with cross-entropy loss, hence are ... | r | 0bd585393ebc59451bd9ba98f8bf53e4 |
Another way to test the spin polarization, is to couple one of the
{{formula:4308d305-9d2f-416b-b295-9d3995e4707c}} -nodes (Fig. REF ) to a side quantum dot, that is in a Pauli
spin blockade region. {{cite:0095664c312207a77d229479b8d06bcbf2c9d07d}} After a while, the side dot will
capture one of the polarized electrons... | d | 513b32979bac6028a5d14aef1a7f03f7 |
Recovering “watermark" or “watermarked signatures"? Recent studies show that DNNs can “memorize” some training examples in various ways {{cite:d1f854a85f875768c5a946b7e25d93f8ce817e65}}, {{cite:275c4487ea30e0a162c5e1b4228bd5e3f0391e9e}}, {{cite:67263143f989e148fb4b51307da6c515d0cffe63}}, and one can recover certain mea... | m | f18d6e79dc76fdb1561ad370f602b345 |
Our model-free approach directly exploits the nonlinear time-embedded dynamics of observational data, and implements autonomous detection of target information without any prior models or training. Starting with noisy observational data {{formula:7d228909-b19c-46d8-a8e8-2ed3ce3fd8b5}} (see Fig. 2-a), it involves four ... | r | aaa38584bcdc60edf251cf80f073cf32 |
We assess our method, denoted as VesRec-SSL, in two DR grading scenarios: unsupervised domain adaption (UDA) and conventional classification problems. In the first case, all UDA methods are trained using both supervised samples in the source domain and unlabeled samples in the target domain. The performance is then eva... | m | 745bd19c9a4afdd9620b40f047a5b281 |
Causal convolution blocks are stacked with a nonlinear activation function and batch normalization, with a residual connection applied for gradient stability. These blocks are stacked in a layered architecture to form a deep, overparameterized neural network as in {{cite:c265d1472ff8938c48b24248f44c2ca706c0a206}} and {... | m | de29d620ed578f898e5845bbd4a02310 |
Further, our approach can adopt the three types of clustering analyses for time series data {{cite:1ee870c779f1e035951857ae5972bac6c29acd21}}, {{cite:2a0365537992758c49da61566b2b3c0c630e0bd1}}: Whole-time series clustering for clustering individual time series based on their similarity; subsequence clustering for clust... | d | 571502145489444238a8435a06c03ba3 |
Proposition 2.8 {{cite:1263a11c3c016f01434489381ec767cfa74e2345}}
Let {{formula:fb05d0a8-8cdc-4202-ab18-32d2086c9377}} be a group of order {{formula:e805e5b9-8962-415d-8e17-2abfff0da1e3}} such that {{formula:8198cabb-2418-4cd6-bd45-184aa57a192e}} is realizable. Then {{formula:f1866aa3-58ce-4c14-95f7-a9611bf25931}} ... | r | ed699bebf259cb3614237ad08243b73f |
of size linear in {{formula:c992178c-c9f5-4e60-ab66-e9498b79ed7b}} (for {{formula:a2b85d15-588c-4d37-9509-867efeb12dec}} ) and arbitrary metric space of dimension {{formula:1e33c8a2-f085-4ba8-9ec6-1f47d5bff04e}} . Current coresets that are subset of the input have size at least cubic in {{formula:a9285ccf-ed3f-4623-b... | r | e31beae2cc32eeb6d49a27de8510897f |
Attenuation of wave energy over distance into the MIZ reduces the wave steepness ({{formula:1a5e9649-af56-4ae6-8791-57923ebb949e}} , where {{formula:860db560-baed-4b73-9ba6-96f3127abec4}} is the wavenumber associated to {{formula:13637aa5-e14d-4d25-a893-1fe9bd08579d}} ) from {{formula:c9161496-5a77-4c29-afac-bc2a523a7... | d | 098308bd2879c5a7e303b775d4528767 |
The focusing problem is more challenging. In one dimension, it was addressed in the earlier works {{cite:698e4034be29c2d376d55f19c91fbe608d018864}}, {{cite:96372950a206c9305e7a08711bd35b9fa3d888c9}}. The one-dimensional problem was revisited recently in {{cite:1965742e4b88f9264ae019b2148b389e504348dc}}, {{cite:886c9de9... | r | bc55a44a78e856093cd394da58252eb6 |
The expression (REF ) agrees with the basic constraints derived from the structure of Euler fluid equations, except the one of reversibility (which constrains smooth solutions but not singular ones). Irreversibility in the first term (REF ) is reflected by the fact that the product of absolute value of the vorticity by... | i | e56fc6fa2b36e2593b2e07c9c8fb080a |
Fig. REF shows the total processing time of each task when using different approaches. As illustrated in the figure, our proposed approach outperforms all other approaches for all tasks. Particularly, our proposed approach can achieve {{formula:caaca138-e7ea-489d-9a10-8f6fee96911a}} that is up to 90 and 10 times lowe... | r | b8464f8ef31b548282545c419844821c |
Retinal Disease Identifier (RDI).
To identify retinal diseases, in our RDI sub-module, we provide two types of deep learning models based on {{cite:27c7f05ab71178376c49eabb6d8500657f80c525}}, {{cite:20e6dbf196315b63ca8a0b07e188793cb780abf6}}, pre-trained on ImageNet, and then trained on the proposed DEN dataset. From t... | m | 9082298bd140a1cb5ce66bb0715e06a5 |
A fundamental dogma in distributed computing is that a distributed algorithm
cannot be deployed in a real system unless it can cope with faults.
When it comes to recovering from transient faults, the agreed upon
concept for fault tolerance is self-stabilization.
Introduced in the seminal paper of Dijkstra {{cite:a922f4... | i | f0e7e5db10dd935d3dff6aafb81da048 |
In 2018, a study into explainability methods was conducted, which determined that most methods were acting as simple edge detectors {{cite:ceff25b19caf4202e1db2f86b123fe09c85eb508}}. The experiments showed that produced saliency maps were mostly independent of the model parameters and labels of the training data, excep... | m | 3444f277e1901949f3535a907021b512 |
The update (REF ) takes the same form compared to the typical weighted gradient method {{cite:257ce3bce15e1a62640b9b9f9b2dae33ae02804d}} which solves the same problem, but has a different weight matrix {{formula:d778b77f-1e5f-42c0-8786-fa47c7c2ad69}} .
| m | 7578906886d900114fd573a0f0e218cc |
Wireless power transfer (WPT) is one of the EH technologies that overcome above limitations, which can provide enduring charging for communication devices. The prospect of integrating WPT with communication networks creates a need for technology that can transfer both information and energy simultaneously to devices. T... | i | 4290875ad054e78e345f54453c8791fd |
Included in this section are a number of qualitative examples for CenterNet {{cite:7c41440fe686239ad576e7bcdd728624661386a4}} and the proposed DeformCaps on the MS COCO test-dev dataset {{cite:867270e25a6a8bd36c23896078f4085144082b33}} using both flip and multi-scale augmentations. Results for CenterNet were obtained u... | r | 66b70eb965236011c5801146af4ad5ff |
As graph neural networks are more widely developed and used, their safety and trustworthiness also become of a bigger concern.
For instance, the adversary can steal the model through a model stealing attack.
Recent works have shown the high effectiveness of model stealing attacks on complex models even without knowledg... | i | dfaabebd858c9c85d0e04cda69f624be |
There are several advantages of using document NER models: (1) The models suggest a better way to bridge the gap between research and application fields.
Following previous studies, several researches have leveraged sentence NER models in biomedical domains {{cite:a6eef8c6efd4e85215a70e5b5a54ceafead312fe}}, {{cite:419e... | i | 5222ccee51b4ba2c080f6336f48bceb9 |
Research has been done to evaluate the idea of Lagrangian control of traffic with automated vehicles, demonstrating that vehicle automation can smooth traffic waves both in simulation {{cite:c94238d07a6e02d0fb4464754700a285bbf063d8}} and in full-scale experiments {{cite:5fd58f9460779a89adb9e7a58c6d7dbc0bd7da9d}}. These... | i | 02db3913832f6d583082dc0cb95881af |
In the low-energy gamma-ray band of 0.1–100 MeV,
we can observe various radiation processes in universe,
such as the line emission from the radioisotopes produced by the nucleosynthesis
in the supernovae or the neutron star mergers {{cite:3829981ff19d5aa326dc0daceeab83f8e403409b}}, {{cite:a2358f5cc83078989ca69bd2061b7c... | i | 3000b7fb5384ed08bf205d164e8ab5bc |
There are several options for the evaluating criteria on estimating layer importance, , weight magnitude {{cite:d70dbe58039386b4a185c7d4334d3077328d3e9d}}, reconstruction errors of layer activations {{cite:efcddfab4518025d7013b78d8697a47f75f8a866}}, BN statistics {{cite:7b7105f7ca9bb00879c4edd906077ca4b1068c34}}, and g... | m | d1401af7ce30ba7d1d46215b495f65dd |
We used 1D numerical simulations (described in Section ) to show that if a TNDW is able to propagate within the helium shell, then ignition within the CO core is guaranteed (Section ). We demonstrated this for {{formula:c8cc7fcb-efaf-4d23-9267-0a21d48b6985}} , where we were able to numerically resolve the ignition in a... | d | 8b49bfd330bccb0e6b2c708e3f61ada1 |
As shown by {{cite:1c4467e411939008385f002b41d24d1cb4185432}}, obtaining a motif clustering of the original graph can be done in two steps. First, we construct the motif graph, displayed in the bottom of Figure REF . This graph has the same vertex set as the original graph, but a different edge set: any instance of the... | i | 45432abda41c2c23990305368b5a2cbe |
First, I focus on the models with {{formula:34d12678-6757-4f2e-9bad-f63b3dd91155}} and 0.5 (the fifth and sixth lines in Fig. REF ; models AM3, AM5, AM10, AM20, BM3, BM5, BM10, BM20), which have a low mass accretion rate (Fig. REF ) and would form low mass stars.
Figure REF shows that both outflow and jets appear onl... | d | fce202c095887c43d830703ff3215a87 |
We perform numerical simulations and apply radiative transfer post-processing to our data in order to analyze the influence of chemical inhomogeneities and optical depth effects on the {{formula:1c0f690b-b85b-4d92-8fdf-c97d25f48f90}} -variance analysis. Table REF gives an overview of our numerical models. Mass- and vo... | r | d2bd40fc9ee0830c0c755233e45d41f8 |
ID of classification layer does not predict the object detection performance in contradiction to (See 3.2 in {{cite:18c30ec1d65eeeb1262ef812d9c39e91bc6a9791}}) that corresponds to relationship between last hidden layer and accuracy of classification. In our case last hidden layers(fc layer) ID also have no relationship... | r | fc4f3618a1a7fff1836a458571cbac05 |
(U3) There is an even unitary representation {{formula:4e342f43-d549-4cc6-b9aa-10bb96fa8ee8}} of {{formula:42e8d433-fb17-4e42-a6c3-862a76815499}} ,
the simply connected group defined by {{formula:61f8f6d6-5cef-4708-ad8c-a05a6fbf6941}} , on the completion
{{formula:c2b9e088-7c7b-406b-b2bd-696a3da1ef6b}} of {{formula:5... | i | 3cc6cf4e695575287f448872ba683f60 |
Quantitative Results. table:sotamr compares the performance of the state-of-the-art methods when trained with our proposed training framework on the Visual Genome dataset {{cite:4dfa9c1971c0b53c82b300047dbfc33e83c9f126}}. Our method consistently improves performance on all the three evaluation settings (PredCls, SGCls,... | r | c0b9669da471693490ae1df0be626748 |
Upper limits on {{formula:e5df4f93-b147-419b-96e8-0f45270f81f4}} for the 2HDM+S for each Type-I to -IV as a function of
and {{formula:aa307221-2136-4e54-9087-89d06cb6c90c}} are shown in Figs. REF and REF .
The assumed model branching fractions for pseudoscalar decays to {{formula:0fca998c-3e8f-410b-9cea-422f7af61040... | r | 0801c76224421670c447e77802de5cf6 |
A good introduction to the concept of networks can be found in {{cite:870e27bcb9b5a75e98e08e5a0cf8d31d12a07065}} and {{cite:c9f8408b56f4175818afb1ee25d6ffeb5de40808}}, whereas {{cite:4ad1a783f0284f41d48467c6648014986bee140b}} contains the same ideas described in the languange of graphs. Methods of weighted networks can... | r | c9429c0087a8cc837e8f99fe49357a18 |
A first glance on the figure reveals already how challenging an accurate description of the electronic-structure is: there is no method that performs well over the whole range of bond lengths. Clearly, there are more sophisticated functionals for DFT and RDMFT that perform much better than our chosen examples, but {{fo... | m | 46bbebdbba52ee4a3ab1df2d0a8962bb |
Here we investigate the effect of using various saliency detection methods for our SaliencyMix data augmentation. We use four well-recognized saliency detection algorithm {{cite:2f26b5e9201bc4c7f1b48ae42f66d73380ffe444}}, {{cite:2adbb073a211aaad3d661f639c59085ff79ffd4b}}, {{cite:6ff52f63ee1c73148d026816504efd367e28a7f7... | m | 2b4323fcddbcece3fe766bb3e5a0e25d |
Human biases toward abstract knowledge might be linked to their ability to verbalize this knowledge through natural language {{cite:5deb5f00e1fe65070c663d6d18d503a24cedb165}}, {{cite:587baa068983411ed77f0be7e36ed6c0c1b04d6c}}. Human language descriptions can therefore act as a repository of this prior knowledge. Recent... | i | 54a5bc7d85b53e48f215c99c310c0a99 |
There are some approaches to quantize time in quantum mechanics such as the multiple-time states formalism of Aharonov et al. {{cite:2a9d1eb61e6fb39566a6962143bd60d1059f130f}}, {{cite:1d6fa33199286a0bd060056cc70627dd85dfe35c}}, the entangled history formalism of Cotler and Wilczek {{cite:6b6a544636973ddfc0b8a5b171107f4... | i | ca38119ba5bdaafc29ff211a45f0af7e |
We compare our methods to various competitive baselines, including BitFit {{cite:e1fda4068338a6ce682bd27f963e5e5f2c4c9990}}, VPT-Shallow {{cite:cb88f30cafe2c3a5268ed60ac22555cd3de8312a}}, VPT-Deep {{cite:cb88f30cafe2c3a5268ed60ac22555cd3de8312a}}, Adapter {{cite:105732d28330f4224cd9a134e867a9d054a65d09}}, {{cite:537fa0... | m | 6f722541435fe205714f5fcaf1b954dd |
In Section REF , we present a surprising result: finetuning converged BASIC checkpoints on more ImageNet labeled data leads to worse robustness results. The metric for robustness in Section REF is the average top-1 accuracy of the finetuned models on 5 robustness benchmarks derived from ImageNet {{cite:cf510bc681074ca... | d | 444a36d80225da9ac4bd73f4a13824a2 |
The purpose of this study was to investigate the applicability and potential advantages of adversarially robust models in the field of medical imaging.
A limitation of deploying such models in clinics is a potential performance degradation as compared to conventionally trained models that has been found by other resear... | d | 3917dabaabe8da606895f07008b720cd |
We are primarily concerned with the problem of learning general nonlinear SSMs. The aim is to find
a model that can adaptively increase its complexity when more data is
available. To this effect, we employ a Bayesian nonparametric model for the
dynamics (REF ). This provides a flexible model that is not constrained by ... | i | ab1186fee53cb39497cdec90872880c8 |
Lossy kernelization stems from parameterized complexity, a branch in theoretical computer science that studies complexity of problems as functions of multiple parameters of the input or output {{cite:b38d7ae8d23ee8d618305a2d73eb3ac543c2a3e7}}.
A central notion in parameterized complexity is kernelization, which is a ge... | i | 677b14a9e6cf5e3d300f36adeb162d9f |
However, modeling conductor motion still has several challenges. First, the conductor motion is highly complicated because it conveys various types of information, including tempo, strength, and emotion. Meanwhile, the generated motion should be closely synchronized with music. Moreover, because of different conducting... | i | 425e8dbe9b1c60c2a84ab5f03fc57943 |
In this section we adapt Theorems REF and REF to two
different asymptotic regimes when the temperature becomes large.
In the first regime we fix the chemical potential {{formula:f7a0473d-d072-4e5f-9c3c-985dc94f9f5d}} ,
which is reasonable since we work in the grand-canonical formalism
{{cite:c105c395c892e4527280434ea... | r | e42083ce8138d12fba5e920a17c54bdd |
A key aim of next generation experiments is to reveal the nature of cosmological electroweak symmetry breaking. It is expected that future colliders could definitively rule out or confirm a strong first order electroweak phase transition {{cite:0b0bd56ffe779ab0686f642b101c38b29f1192c3}}, {{cite:24c0db622cb2774825e3d2fc... | i | 018cf26a64944cad12047a7d364d37ff |
We study three geometric operations for Legendrian surfaces in 5-dimensional contact manifolds. These are Legendrian isotopies {{cite:c7e7c17542ee37cf01a67e24e0958d30ab2c4025}}, {{cite:7b472395da313665f282148beee7cd16cb6a26a4}}, {{cite:a7eaf111509345d400e26b2b0a2cb12b33f3e767}}, exact Lagrangian cobordisms {{cite:0327c... | r | 0e70ddece41d6343d22b6b805eadf9b0 |
In order to obtain it, we need the quantitative version of the isoperimetric inequality proved in {{cite:cc3cb1a7cd2a30de64c953bcc5c3fb3a4e1607f4}}
{{formula:1e36e3f8-5605-4db1-a74c-9fe17163da52}}
| r | 7730a0fad7e8aab01ed706d4744fc8b4 |
Let us review {{cite:837e2d4d9d9131078d7e03719e8c81b2076b7563}}, which shows
{{formula:fdfd1ea5-0230-4954-b13d-8650605c9ace}}
| r | ffd3c6de1b53bb48e45d5ed4cd91e9cf |
Superpixels can be defined as groupings of perceptually similar pixels to create a meaningful image with fewer primitive elements for processing. The term coined by Ren and Malik in Learning a Classification Model for Segmentation set out to solve the problem that as pixels are not natural entities, they are meaningles... | i | f275f38347c95f2e066b8441c104e1d3 |
In addition, there is an evolving line of work on the early learning phenomenon and early stopping methods.
{{cite:871d1289118100aa01b328e1f325b3119a1e036c}} observe the dimensionality compression and expansion stages of training a deep neural network, and propose an adaptive loss function according to the subspace dim... | i | 5114fbb21d215860c957dcec3caee7b7 |
LINE & GAE: LINE {{cite:e1ac09ce72089d431d620702c8c4fbb6985f5446}} and GAE {{cite:1369df7b479b2544aad35b1afac158f9674b6d32}} are two typical single-view clustering methods.
Baselines: RMSC {{cite:65cfa4adb61f9bb4c69cbe9fb88149195e2d7f32}}, PMNE {{cite:8c2f8bf606045a44dc26ead56c966470bf436db8}}, SwMC {{cite:60c679071b... | m | 87fc54ac3d58509bda8cfe84dcbc0680 |
At {{formula:f77b1885-a229-41b4-8dba-2722276fd1c8}} nonperturbative magnetic screening effects arise {{cite:7a735033a92af96a9b81a3710da85c858961b195}}, {{cite:3354339b30601a3425f453a84d2e54f6cbda57cb}}, {{cite:216e15bc3dc161a463e2e945ec40dc6a9035dc94}}.
Kajantie et al {{cite:bad174b234be1e10b532f8340be9dbe8ecee49fc}} ... | r | c795ba4a87c894be90e65f15c5420b46 |
He et al. 2019 {{cite:556ba596ee0c1fdfd42e50aac88744c35beaaef1}} CNN Epilepsy Five Classes: Healthy (A, B), Seizure Interictal (C, D), and Seizure Ictal (E) From Andrzejak et al. {{cite:679219f58a4afe9b829b1e90079bfe77e3a88236}}, 10 Participants (5 Healthy and 5 Epileptic Patients)
| d | 8d1086c4da25de36260a6b1c7f0a79af |
The study of shock waves is an important issue in the theory of both inviscid and viscous conservation laws, such as the compressible Euler equations and N-S equations in gas dynamics.
We first overview several remarkable works in one dimension.
The shock stability for hyperbolic conservation laws has been done by many... | i | 7e9476f2fafda08183295cc14bef1795 |
We use the test set of MatterPort3D {{cite:9363f2e5fc7a0f331d23bfe2196b2d8bac7bbcaa}} to evaluate the effectiveness of our method in the high-resolution structure recovery. MatterPort3D images tested in this paper are consisted of 1,965 indoor images in 1280{{formula:ad64faf0-7615-4e7c-930a-934ce600ac81}} 1024. We resi... | r | f64e3210d64716cb308b8643714da8a1 |
This section defines our experimental setup, then proceeds to present the results. First, we test SKDCGN – as defined in the [sec:approach]Approach section – on both ImageNet-1k and MNIST (Section REF ), and based on the observed findings we make some changes to the proposed architecture to improve the quality of the r... | r | 5767311f821331cdc11185a900dc1ba1 |
Con-Reg: Con-Reg aims at regularizing the embedding space so that its layout becomes more similar to the feature space of the pre-trained feature {{formula:c6029fca-4bd5-427a-84c1-1d2533670142}} . To do so, we utilize the features extracted from the audio and add an extra loss term {{formula:8c9ac29b-4146-4a33-92f7-3c... | m | 6fd958a01daf4408dff25c94e82159e0 |
This view of metal-poor GCs being accreted and metal-rich GCs forming in-situ
presents a problem: the stars in the thin disc have higher metallicities than these GCs
{{cite:5c7d9eacf01f9b01223e6721b7f5edd33cc9edd7}}, {{cite:9383fc9fcecffe9499f8679583bfa605d1d88d9c}}, {{cite:482d21cf3c8859261004a1d2730a37faae07bced}}, {... | d | a8bd109263d613c374b19964f4c4a168 |
The main contribution of this paper is to introduce a novel model-based deep reinforcement learning (RL) based algorithm for DSS between LTE and NR. The main scope of the proposed scheme is planning in the time domain whereby the controller distributes the communication resources dynamically over time and frequency bet... | i | ff1ea5667324bbdf47c1f67c7ea0aef0 |
Without parametric assumptions, ITE estimation is not feasible {{cite:8f7227026496278e7bbdecce7440b09612726362}}. We focus on linear models in particular, since they are important in developing theory. E.g., in the literature on optimal designs in active learning, much of the foundational theory is built around linear ... | i | 1332a6307bf4d3b10c1eb0bf512ffe8c |
The SSIM, introduced in {{cite:8fdd56afcefacdd233ce0b9552482f79d7e9071b}}, enjoys widespread use across a number of disciplines. While some recent works cast doubt on the popular notion that the SSIM truly represents human visual perception (e.g., {{cite:52c6e4caaab065d214c29229ed8cd011bcb7bb80}}, {{cite:5dd447421c25ea... | m | 7b1ae9d4ef21e5baf6941d50f13cb7ca |
Previous work on the development of online SNN learning algorithms includes the work of e-prop {{cite:6f5a95aff7015d58a3004c42101c48cae24e6e87}}, which is a plasticity rule that was mathematically derived from BPTT, where a learning signal defined by a given loss function over a task is projected to all neurons in the ... | d | 0f3e0a0a53954bcc78ee6958798c7095 |
Let {{formula:9fbb9e20-1e6a-4371-9f96-f0e7254ceecd}} and {{formula:c262f9e6-b214-48f9-bda0-f435e5a26afa}} and let {{formula:9e97ca8d-b66f-411b-b023-f1097fad950c}} denote the fine moduli space of principally polarized complex abelian varieties of dimension {{formula:4b0e04f2-7bcc-42a7-8312-54de875efc69}} and level {... | r | ae78ffc93ec778231680bd42c82b6cc8 |
These computations are all done at the level of the classical, as opposed to quantum, sigma model. This is in contradistinction to the twistor-string or ambitwistor string which generate amplitudes via quantum correlators (and requires supersymmetry). Clearly the twistor-strings of {{cite:cbed76145e222cae7aa587f72910d4... | d | 8991bfd5d182372626eec41503807982 |
The proof of Lemma REF follows from Appendix A in {{cite:3bc6edc3e54ab354c39cd4708fa64f4b5b97a504}} with some modulations.
| r | d716f6a2920f04049cdb05eafc2916e6 |
where {{formula:2e2df46d-9c54-4cac-93d9-6201ef4c8e04}} , quantum number {{formula:14032a53-bc86-44d2-aa7e-69d327e6fa96}} is defined as {{formula:6ce31eac-196c-4c41-bdf1-63fed456c3e4}} , and {{formula:dfc5cc78-72ea-4b21-8e60-2a329788a2b9}} is the spherical Bessel function of the first kind {{cite:74028ecd19dab662d8587... | m | 13c90208935d57dc7a8d0808f761705b |
where {{formula:2d47fd70-7891-4a7f-9500-0e8d04448c87}} is the distance of the binary system from the Sun.
Since the average extinction
of the Galactic disk is {{formula:ae0c2ce4-48cb-47c0-8365-0f263e3b9c14}} {{cite:4b4219da54d56e29dda13fcf9eb74658597f1682}}, we adopt {{formula:bf60b127-508f-4afb-82f9-9f071ab72144}} .... | m | efae95c8db3c14134ce8d7f20b4eec12 |
Our formulation of semi-supervised learning sheds light on the working of current SSL methods. For example, the reference prior can automatically enforce consistency regularization of predictions across augmentations {{cite:eecb3b0c55932e7ad5f5f4ef173c82eb0c0fc0cf}}, {{cite:098f001710c66d3028e5cb3a1bbe7ae73953d55f}}, a... | d | bbbc07d3e46430bf4bdc77503673795e |
With the four galaxies at the lowest redshifts one obtains the effective redshift {{formula:d0acf9ec-f9aa-4cc9-acc3-4dde3e30723e}} corresponding to the Hubble parameter value at the lowest redshift. Determining {{formula:1b20d23b-0783-4c5e-ac99-ff8e079c3995}} requires the determination of the derivative {{formula:57a... | r | ffe1ca262d5447f4da3b5efc8a1a249f |
We evaluated the emotion recognition model using weighted accuracy (WA) and F1 score on each emotion.
Weighted accuracy, as used in {{cite:b7189cebe26f621f47e0af7f545d5dd7af60b306}}, is equivalent to the macro-average recall value.
We also averaged the metrics across all emotions to obtain an average WA and F1 score.
| m | e506bef953f6e1f3f68f2c557aab046a |
Consider the output follows Gaussian distribution. The noise scalar {{formula:fa94fb78-13dd-4d44-bac6-f3de7f961f2d}} is often fixed as part of the weight decay of neural networks. To capture aleatoric uncertainty with dependent data, we would tune the observation noise parameter based on maximum likelihood inference. ... | m | 54458920fba920173957316d612833f2 |
Sentiment analysis has been approached across many domains, including products,
movie reviews and newspaper articles as well as social media (see e.g
{{cite:057ce2eea69e91ac195e3f200eb23750555b873f}} for a comprehensive overview). Typically, the methods employed
depend either on existing language resources (e.g. sentim... | m | 683472e6db9546abc915e35eb0e57120 |
The introduction of pre-trained language models, such as BERT {{cite:bfd32f9e68dad1fffadc89c0b5102687d4d9d07a}} and Open-GPT {{cite:7ed7754fde3ab16231708cd6208370d68577824f}}, among many others, has brought tremendous progress to the NLP research and industrial communities. The contribution of these models can be categ... | i | 00495b8d5f4ea5167d26685e3dbeb0ae |
Since the development of deep learning, several excellent backbones (such as SegNet and U-Net) have been widely applied for image segmentation and can obtain satisfactory results {{cite:52af3b6549e1c0468264f1bfdd920331c5182e2d}}, {{cite:862e93bb4d894fa25a5aa88578bb5eb4c352375b}}.
The first 13-layer convolutional networ... | m | 9b92fbc2399582993db8698b5c0fb6c1 |
Remark 4 Observe that the feasible region {{formula:43e265c2-82a8-4feb-b33f-05c6cf340dc2}} of (MICP) is a rational polyhedron. From Corollary 17.1d of {{cite:453cf8e0a4e35db7fd28f630ace71584839295e2}}, there exists a feasible {{formula:bc9f2826-06ae-44d2-a497-d7dc03c95687}} whose size is polynomially bounded by the s... | r | d4e749ef170a00b73fef2cf7bea2285c |
In addition, the non-differentiable quantization function leads to zero-gradient problem during training. Although STE {{cite:e97f7b3aa602675f9df3319af87be13963985f3f}} can be employed, the approximation error is large when the bitwidth is low. DSQ {{cite:a3502f4467855262aca59b0c7d38cab452cc8a0b}} uses a series of hype... | m | 937b9a358f3d13d81955bf9f5434d887 |
The work on StyleGAN {{cite:d4b4dbc0db905b18267e099f49db6258fc98d3b8}} revealed certain input conditions that contributed to the generation of super realistic images, improving upon the blurry or distorted images generated by previous works. These conditions are a) images in the dataset should be of similar zoom ratio;... | m | f131fc448dfe74ea598bbe6603eddfd6 |
In Table REF , we investigate whether the proposed layer-wise attention pooling of language models performs better in contrastive learning. The experiment compares performance by training on language models with three training objectives. All results evaluate sentence embeddings on all STS tasks. Equation REF is basic... | r | a223f76323095a496266a4a2c3a6b9b9 |
Sections REF and REF are structured as follows. We begin by assessing the performance of our models trained on different sets of features inspired by prior work. These experiments not only give us an insight into features integral for the LDP and BEP tasks but also allows us to view our work and feature set, in the c... | r | 416e00e702aa8c4bc5846f89fdf2b2e6 |
(ii) CLIP pre-train models dominate over ImageNet-21K pre-train ones. These results well match the shift of paradigm in current AI research {{cite:bc8a6ae7527891328c9c1a1c6e9dce49a4713094}}, where pre-training no longer needs limiting to curated data and annotations to deliver good performance on downstream tasks, but ... | r | 72b579f620e964c69a09d48fa1b793ab |
It has been over a century since Einstein, Sutherland, Smoluchowski and Langevin formulated the theory of Brownian motion
to describe the thermal motion of particles {{cite:af4e772ebec6a82c6c0cbb725ea8e3c70baa1005}}, {{cite:8153baaeb62606ee4c33591d134aa8adf9802499}}, {{cite:4d2dbbb9907ac195b2cf8aeb58939e5bb1c2d217}}, {... | i | f41cde431f760aacc4bdafd71bf0d929 |
Yet another interesting property, which distinguishes (REF ) from certain other well-known integrable equations such as the Korteweg–de Vries
equation, is that even smooth initial data can lead to singularity formation, also known as wave breaking, in finite time, cf. {{cite:ca5139b86a0cb002fd57d47d836ae7835cd19591}}.
... | i | 800bdce3b96525539b0c630ba40491cc |
To evaluate the quality of audio and video generated by MM-Diffusion, we compare it with SOTA unconditional video generation methods DIGAN {{cite:6cb09aac220a6a8e4408466db971a080835ad874}}, TATS {{cite:14feb810350608ee0e6ebf8067b3906e7a582636}}, and audio generation method Diffwave {{cite:06ea0cf1061aba24dc099cc5705a3c... | m | 9e60ad5710d64b0304eb252668bde551 |
In practice, the average fitness of the population changes over the lifetime of individuals in the overlapping model. An empirical study by Kenneth De Jong showed how the use of overlapping generations introduced genetic drift (i.e., faster takeover times) which increased in severity with the decrease in size of the of... | d | f263043ecd5b53a8c722e35913e0731f |
Heterogeneous degree distributions suppress diversity.
In typical networks with a heterogeneous degree distribution, most vertices have small degrees, while there is still a significant probability of finding vertices with large degrees. In other words, heterogeneous degree distributions tend to be characterized by lar... | r | 1f1f0f54c8c44d27ac0d302b7e820137 |
All methods under this category follows the philosophy of using one generator and one discriminator in their models, and the structures of the generator and the discriminator are straight-forward without branches. Many of the earliest GAN models fall into this category, like GAN {{cite:106dd2a97adac818fa4730f4afa874dbb... | m | ed0c3ef35cd22d4bbb2d3d5c6720e9e2 |
Theorem 2.3 (c.f. {{cite:fd9a9ed428d2b887ec24092ed403b985dd919585}}, {{cite:b9051d1c0b597735d947208152620bb49106c12b}}, {{cite:2ced6c44c1bc99d68cd7d74bab421e983789db1d}})
Suppose that {{formula:3b22d691-af89-4ebe-910a-812c4ff7706d}} is a square differentiable system with a given interval extension {{formula:a5891cc2-... | m | d0804929852544299ab4f2ffb244ee66 |
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