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The validation of the method {{cite:b86a95b50f1f582c37ce0beb99cdc792194b585e}} proceeds by
establishing that the resulting Markov chain is ergodic
{{cite:4876adba68703fe6e2ae3788a756e9a8ff7f812b}}, meaning that it converges
to the distribution corresponding to {{formula:16e90cb3-903f-4a3c-bad1-6a4187121bd7}} , making t... | m | e62dad3792d05a086e9dc37a81a66a59 |
One important aspect
we did not touch on is the statistical properties of
{{formula:f8035a9f-7213-4d18-97f3-a9ad919a98aa}} -regularized solution path, which has been studied extensively
in the literature
{{cite:8fcda5b4a8cd5a95dba3eb92950ca8ecc74c455f}}.
Interestingly,
{{cite:281e38bdb2b67f0158ed5bfdf440807a456dcebf}},... | d | ef32fc38ab2aa1fafc4e5a513adc8823 |
In our approach we query the user about whether a feature is relevant, i.e., is positively correlated with the target variable. This is a compromise between detailed input about regression coefficients (exact value {{cite:4e91f28c39b73e382f7809f9cc85f94f3ca1c0d9}} or full prior {{cite:99b351bc112fd5b8b7299e6ce35911a633... | r | e0c14f60da48a5356c3d7751a5d2d059 |
Our findings on the connection between spatial behaviour and personality are consistent with the existing literature on personality. The correlation between exploration and extraversion could be explained by the fact that extraverted individuals are more likely to be risk-takers in various domains of life {{cite:24d3c4... | d | 484bb3b346d975684bb6cefb7c1f3eec |
The quantitative results obtained on the MIT300 {{cite:1ddae218f4dbe431bde8e77fec33ffcf74b3d74e}}, MIT1003 {{cite:8ec4f8d1376f9b10320a2f95de1e92ab528fc625}}, TORONTO {{cite:3559791af409f35c8be6cfe52b9c2abade6f27d2}}, PASCAL-S {{cite:ccadd3166755d790ef78e077f7a84e1e97ffc05f}} and DUT-OMRON {{cite:961860df91588c4cd50edb7... | r | 37f5a535db869da133b3e9b1b5b13a22 |
A very good approach for qualitative understanding of cosmological models is the dynamical system approach, first developed by Collins {{cite:7e3ede7b7c9286fbaa4a099471ebab36637e568a}} and extensively reviewed in the book edited by Ellis and Wainwright {{cite:55807384155bc84d690cc6621c423ddda515202f}} (see also {{cite:... | i | 8a3c936a5670b926386b40b05e548558 |
In the case of self-focusing (self-attractive) nonlinearity,
and for sufficiently high dimension (for fixed nonlinearity)
or for sufficiently strong nonlinearity (for fixed dimension),
a key feature of the NLS model is the presence of collapse type
phenomena, that have also been explored in numerous
books {{cite:3545a9... | i | b4dc7e397f64d7c98527e54e7874e0de |
In keeping with a widespread adoption of machine learning across nearly every industry, there has been a dramatic increase in publications applying these methods to carry out routine diagnostic tasks in medicine. Most of these have emphasized matching or even outperforming practicing physicians, whether it be for inter... | d | 367f7d1efc226dc700038d2571036aad |
The RF kernel (and RF) outperformed the Laplace kernel in our simulation study in most cases. There were scenarios still where Laplace kernel was competitive e.g. for the van der Laan data for regression and survival, demonstrating that the Laplace kernel is a valuable option to be considered in practice. There is no f... | d | a2ea085541bac0ce61599ea828419739 |
The rubik's cube is a challenging and representationally complex puzzle. It is quite difficult to capture the complex patterns involved in it. Predicting the optimal action using only the current state often leads to underfitting, and almost uniform action predictions, which do not correspond to actual solutions. Howev... | d | 8514ad1d99955c6ed70ef18c5f6bdd78 |
In particular, for the approach based on binary weights in the FC layer “binary, FC w.” we have considered the binarization of the sole weight matrix {{formula:2e0d9e0f-e97e-473f-9f87-1eb4648eff61}} (the bias vector of the FC layer is therefore represented as single precision floating point number). Moreover, as binar... | r | 0d95ac3fbc8c3786ca5cd5923f39af24 |
For {{formula:6277b800-28e5-4683-9e03-0a50fdf3db0e}} (and {{formula:c7bf8c4a-3026-476a-ae0e-15bdf7e35751}} )
equations (REF )–()
reduce to the classical three-dimensional irrotational steady water-wave problem,
which is usually handled by writing {{formula:83abfa3d-9a2c-4983-be1a-c67c764697bb}} , where {{formula:ef89f... | r | 0286e73e2d256776f08fb592eae8a71c |
In Fig. REF , we show the valence quark, sea quark, and gluon PDFs of the pion.
The black lines are our results evolved from the initial scale {{formula:b4a1ed6e-4de5-415e-b68a-04bff496562a}} using the NNLO DGLAP equations to the experimental scale of {{formula:2326ca7c-cc8a-4db8-ab69-9f3e385e490a}} .
The red lines co... | r | 913b05f00d8d583def6b5721c0231fd0 |
In actual fact, the vacuum model III ({{formula:64be4f73-88f9-4f43-8994-ef15b72034e3}} ) tends to remain always fairly close to the {{formula:c8c25cb6-d297-4112-a4c6-dcea81375a37}} CDM. Its dynamics is weaker than that of the main DVMs (RVM and {{formula:7debf5e1-cc37-4874-bfb6-5bf4a1bf5fa4}} ). Being {{formula:1a0e165... | d | 988faf3bdbdc7d72c01d119985565b51 |
In a number of fields in physics, the formal equations derived from
the theory make use of the pfaffian of some skew-symmetric matrix
appearing in the theory. For example, the pfaffian arises in the
treatment of electronic structure with quantum Monte Carlo methods {{cite:875aa234d79eeeeb295bdf6e5e8ff03ad40148d4}},
the... | i | a5c8dfc17c3e67264fbdecabd19cbd91 |
Furthermore, the last three decades have brought another change in the computational methods to study quantum many-body-systems numerically, namely tensor networks (see {{cite:f92eb5c4550314ac8ca656beae2f7154595c57d2}} for a review). Tensor networks are sets of tensors which share indices, that upon contraction of shar... | i | e3f2fdd55bf51467804992b14e13ca0a |
One could also consider variations on the LBI definition. One might use loss with non-linear dependence on {{formula:75976774-ad02-4ea7-bc9c-7b33bede06dc}} and extend beyond linear queries (in doing so, one might lose the variance-dependent generalization guarantees). One could also consider a loss that is not worst-c... | d | 54a015b85c25e651ae359b59ac28f421 |
There are different strategies to incorporate auxiliary connections to a pre-trained model. One of the most common approaches in transfer learning is to add new hidden layers after the last hidden layer, thereby extending the network's depth and replacing the prediction layer, also known as the penultimate layer. Inste... | m | d4b65f382ab803f5254ab0fefeb9173c |
This paper proposed MOI-Mixer, which aims to leverage high-order interactions for MLP-based models in sequential recommendation systems.
We claim that Transformer and existing MLP-based models differ in performance due to the absence of an explicit high-order term.
Thus, we introduce a novel MOI layer which is capable ... | d | 5708c7bc372b50de81f57e728fbc9dd0 |
As we demonstrated in Section , our motion estimation method in Algorithm REF is both fast and accurate. More specifically, our method outperforms the state-of-the-art FGR method of {{cite:950fdb4d167f8cf57447127d016aca3a565e1fa6}} in terms of both speed and accuracy. Given their strong similarities, in Section REF w... | d | 2ff3d99704c6fdee0ff7cfab3eb46a66 |
(i) We provide new bounds for the instantaneous regret {{formula:97c62195-b9c2-425f-8726-ea8555b52054}} in expectation and in high probability for the inexact online gradient descent; the bounds include terms that quantify the temporal variability of the cost function, as well as the statistics of the gradient error. ... | i | 5f8f7b9bd7d39a06fa0734a2f1dab51a |
In Appendix C, we use the same example to illustrate how we can visualize the privacy protection of individuals. Further work includes comparing this approach with
{{cite:fd7429c00254eb2e712f61e1bffab39da1d9f330}}.
| d | 96a4c1bcbf45f65002a23e12f102039c |
Almost no additional hyperparameters are introduced.
Acknowledgments
This work was supported by the Israeli Ministry of Science and Technology, and by the Gatsby Charitable Foundations.
Appendix
Overfit and inter-model correlation
In this section we formally analyze the relation between two type of scores, which meas... | d | ab430c10109b686d9a6f0b800fdcd49f |
Similar AHEs are also observed in the related Eu-based compounds EuCd{{formula:4652ca6d-3144-4660-a8e8-59751e9dae22}} Sb{{formula:bac111e1-158f-46e9-8971-b31f65154e78}} {{cite:1efb41de390eb0cc81320a054004f7e7de5016ea}} and EuCd{{formula:12495e22-ab94-4dfd-9eb9-c17d70ba80fe}} As{{formula:cf300b04-4e81-4112-a6cd-2361981... | r | f54cb6378be576c980db346678698f3c |
GPDs contain the extensive information on the hadronic structure. In the forward
limit, at zero {{formula:a0bf88a1-4b25-4817-9d0a-b7c37dfe28a3}} and {{formula:3d2e49e7-fe3f-4be0-ba82-3e0b1c1f0549}} , GPDs are reduced to usual PDFs. The important property of
GPDs is that GPDs integrated over {{formula:040d1fcc-b20d-457... | i | 3e09f6c922711b14a15adc0da69d3d51 |
According to {{cite:fd6b933c8ead6f39de7e32739e97c69ac5225c3f}}, aPY {{cite:aff7015f8ad128ad9fc459b804a6d8b02156167c}} has a much smaller cosine similarity (0.58) between the attribute variances of the disjoint train and test images than the other datasets (0.98 for SUN, 0.95 for CUB, 0.74 for AwA2), which means it is h... | r | 737dc629dcfbbe54cfbe3c8237f3e9de |
The rare decays {{formula:f76e1909-909c-4d77-ace3-2ea15917abe9}} and {{formula:ff947f42-64c9-4bf3-b5d5-b2197700dd7d}} played already for three decades an important role in the tests of the Standard Model (SM) and of its various extensions
{{cite:fe162229c04f6193ba63efb0a583ba76d99208ff}}, {{cite:547a7e74fa94471e37570... | i | 4d9ee7acd27fd3b4261d7ae6cc115a91 |
The theoretical solar wind speed and density are consistent with those measured by SolO/Metis from {{formula:878a635e-1f48-4e76-bdea-50dc0fcc95d3}} R{{formula:f2df329f-0a10-4700-b1ff-0334238dfdc1}} and PSP at 23.2 R{{formula:51ed25e1-070e-45a5-a448-fdb5cfcaf3e7}} . The theoretical and observed solar wind speed incre... | d | 9417856658a4fcdc824968a29812bd12 |
The issue of the perturbing noise {{formula:6cf4497f-2a05-4df8-bbbd-183bff22fbc9}} is twofold. Most substantively, sparse
approximations to precision matrices—including the Vecchia approximation
introduced above—crucially depend on the screening effect, which is the
phenomenon by which predictions depend very little o... | i | 94d6b50aa33fbd27a2e88340deb7d7ff |
Experimental realization of twisted bilayer graphene (TBLG) {{cite:d4d0c605f55f8fac35402b3c56c10b0c5a15ca5f}}, {{cite:cf24a0526e7fac6dbdd6794c6385831f51f60e7a}} featuring the flat electronic bands at so-called "magic angle", as predicted in Refs. {{cite:a15266c3e8614222557cc51e9ac2bd0fd6f7b56b}}, {{cite:793a69d394aaa0a... | i | d69d3e5fe66fb5f2a60cbce157a96bb3 |
Systematic errors in our dynamical analysis leading to an erroneous calculated value of the mass of HD 206893 c are also most likely a remote possibility. Our calculation of the dynamical mass uses a combination of stellar astrometry that may have systematic uncertainties that are not well understood, as well as RV mea... | d | 689f0d6c80fdaf69eb80cb39dcf15b0f |
Comets show a diversity in composition, with a factor of 10-100 variability in the volatile abundance {{cite:2b7072cc04253534c54bee546d4e3bfeb9519531}}. However, in general this diversity does not appear correlated with the dynamical category. An exception is for CO, whose abundance relative to water appears depleted i... | r | cdee6223829c0e4c1efc57bd34bab3ff |
These facts motivate the exploration of both the process of scattering and absorption of massless scalar wave by a charged dilaton black hole, extending previous analysis {{cite:30309af6c14e81c7f19654599050ba15d567ddb7}}. In Sec. II we introduce the EMd gravity and the charged dilatonic black hole, including the Penros... | i | 63b5db37bf85cb2724144dd48b8efaa1 |
When tuning hyper-parameter {{formula:b028b29c-d377-4a49-a075-1c27b190614a}} towards 0, {{formula:573450c5-1523-4d65-97ef-25654eb70d5c}} can be altered into the format of one-hot vector with {{formula:14244537-6f8a-413a-866f-540372cc663e}} , which is then degraded to the case of contrastive distillation as in equatio... | d | eaf9169b1d7ad8ce386e3748f742c288 |
Recall that the reason for employing the Huber function in (REF ) is to provide a smooth approximation to JACoB,
avoiding the original nondifferentiable objective function which may result in BCD non-convergence problems.
But can the smooth approximation guarantee convergence to the optimum?
By invoking an available BC... | m | 76537005d8c75fc73f8e11a22e9da685 |
The study of BTZ black holes in the
noncommutative spacetime provides the possibility of the existence
of gravitational Aharonov Bohm effect is a case in point
{{cite:89c1951cfd7b11aa0cde01c6f5cd561945c71684}}. Also, the possibility of mimicking the
BTZ black hole properties in higher dimensions has been studied in
{{c... | i | c55df030ffc1cdfdad40af9c79c5cc41 |
In Table REF , we see the performance of various models on the Abstract Scene dataset, for the metrics described in Sec. REF . We first note that even our baseline LSTM-RNN model (Image2LSTM+atten) shows a very large error reduction compared to the results presented in {{cite:6535c44c3709fdf9a0e0505440f74b066e31958b}} ... | r | 914f8cfdf67066101380ca8057ec25fd |
Characterising and classifying galaxies based on their optical morphologies is not straightforward. A number of different approaches for quantifying galaxy structure and morphology have been developed, documented, and tested in the last few decades, each designed with specific applications in mind. The general goal of ... | i | 8c94402636209c125d1f59e610d9f78d |
We visualize some examples of perturbed images and shift in attention (using CAM {{cite:2af2c63cc61eb34eb20b327947f5e7b32ce23f7d}}) for misclassified images from clean images in and in fig:qualresult for Res152 multi-object classifier. It can be observed that changes the focus of the victim classifier to irrelevant reg... | r | 760a0b692694bded612bc832037dea83 |
All of the mentioned models and others have one common characteristic: they perform better on higher resolution images. So, image classification tasks are easier on high resolution images that are vivid and have no noise. The problem is that, in some cases, we can't have high resolution images due to the age of the ima... | i | 2180544007c540e0282c0e0faaa3a160 |
One potential application of our perturbative construction of the excited state zero-modes would be to use it to reconstruct a bulk scalar field in the dual theory perturbatively. The starting point of such a construction would be the work of {{cite:19048076ca978d7341d46cf4272791e704b5aac4}}, where the zero modes are i... | d | a6b830b220c1a99d01acea0e503f76ba |
M-pSGLD promises accurate UQ by leveraging the complementary benefits from sampling multiple posterior modes and additional Bayesian exploration of each mode {{cite:017081db6b0b92ef63795f4fd331501ff9c11b95}}, {{cite:27177afe47acb316651283c3d6ca225d3d018450}}, {{cite:bd71827a48820eb5b5d28db8e3d24490f3724ba6}}.
However, ... | d | f462287272940c88e869bdad80e0bcd7 |
Figure REF illustrates the trade-off between model accuracy, model size and inference latency for three transfer learning datasets. For each model, we report results for top-1 accuracy, inference speed (in images per second), and model size (in number of parameters). One can see that eFUN models significantly outperfo... | r | 94cd290009916208cf60c3264211d518 |
where we have discretised and scaled wavenumber {{formula:377ce1f6-2074-4288-8eb3-252c077789ee}} . The transforms are implemented using the FFTW3 algorithm in C {{cite:ac4851bf4a6d9b39ee9921f5ac3b5128ddea81b6}}.
| m | 639b129739af51b23aebb725ca511491 |
Conventional motion-based frame interpolation methods only estimate one inter-frame motion vector for each pixel {{cite:44a8707654b24fb221e4d379db4bf860d8e157ad}}, {{cite:2496c1fc26213f02876c4d1b4946afe42e4a1d37}}, {{cite:fb597cf973af3d34e14972985527ae4808493290}}, {{cite:b788a3029cb7efd5da2fd7c639f0c5a17753e598}}, {{c... | i | 19f8e926fabd759511e93294b9e48529 |
Our initial motivation was to establish a link between the presence of (possibly non-invertible) global symmetries in the bulk, and the fact that the dual boundary system is an ensemble of theories (as signalled by the failure of factorization of the partition functions). At least in the class of models we have conside... | d | 098af72dac1149a0381dde81ab17536a |
From the computational point of view, we trained the different networks on a NVIDIA RTX 2080Ti. Table REF gives the training time for the different scenarios for 100 epochs. As UNet++ is a larger network than UNet, it takes slightly more time to train: while UNet requires about {{formula:70ba441c-402b-46ca-b5fd-0c9821... | d | 495b87f322293230cb1a7a87608e03fc |
Here it may be pointed out that the quantum noise models given by Eqs. (REF ) and (REF ) are considered non-Markovian despite being applied to individual rounds of the protocol. The reason is that the memory in this context is with respect to an external environment, rather than preceding rounds of the protocol. In par... | d | 894214fff8b32e2fb0b07df4e3006dec |
We find a remarkable agreement between the results obtained using the new formalism and QNS results up to similar orders in {{formula:3c264019-b454-4ab9-b0e7-7ba5c6d152fc}} , despite using only 500 RVS for all {{formula:48185798-c842-469a-92af-e438550ce536}} . The plots in the above figures vindicate the agreement, for... | r | abac7618d3e4532b8df821f1aafef16a |
The proposed method can be used for a broad class of models where the likelihood is not known or difficult to compute. This is a great advantage in the model development as models can potentially be calibrated before their derivation is finalized. If the model is deemed worthy of further study, effort may be devoted to... | d | 95c3a45940c0522c3b8992ff74ede784 |
In practice, however, it has been well-documented that machine learning classifiers such as deep neural networks tend to produce poorly-calibrated class probabilities {{cite:da51dc21133fcf9a0244c0adaf20d943b06a0fa6}}, {{cite:ef9f394e0e569b5d656835be7b54f91453ebe23c}}, {{cite:b46018df9049f3dd52c22176f3a8e9ab991bcd4d}}. ... | i | 93c5f8e5b5cd23e27e82548241f92f10 |
Post-hoc explanation is the most popular method for interpreting deep learning models. This branch of methods can be divided into two categories: 1) important feature visualization, which yields explanations by identifying and visualizing important features for input features. {{cite:09ea935658fffaecc1380b4b0138c099fe2... | d | ca1cd381ef9a0955a03e6eb516fa1732 |
Reinforcement learning (RL) {{cite:58e7cd8a0b3e6d0074e826144edf9ca3fe56a354}} is based on a framework that considers an agent that interacts with the surrounding environment, it observes the consequence of any action taken, and it is capable to measure the success of its actions via a reward (/punishment) system.
Exper... | d | 66057f6e7c286db9b832872ec76c96bb |
In the present survey we deal with invertible dynamical systems on the circle. In the case of smooth diffeomorphisms of the circle, deep results have been obtained from the mid to late seventies onwards, starting with M. Herman's thesis {{cite:5b33aa946bc9f716c168c80e5a369d72ca91e72c}} and culminating with the work of ... | i | 0df77b49cda46fb07e7f336672619213 |
We compare our AMPL with two DICE methods — AlgaeDICE {{cite:930f4fdb9050677af3701ca30793e6aa7a2e19d8}} and OptiDICE {{cite:e28b6fdb3ac885d291e971bdeada2801fd20bed7}} —
that directly utilize MIW to improve policy learning.
We further consider three state-of-the-art (SOTA) offline model-free RL algorithms: CQL {{cite:2c... | r | bc6baeea11167f0982db584fb81f6542 |
We also compare our methods with three state-of-the-art techniques for imbalanced data. Decoupling {{cite:4ca1abdaaf566d86048fe6281a361ed88dbf4ed1}} is based on re-sampling, while Class-balanced loss {{cite:e13cee5d4424400dcb0bcef98aa1fcbba284651d}} and LDAM-DRW {{cite:064a1da393d8122bf1d1dc58f7d87bfe3e074f1b}} on re-w... | r | 7d29d92ed5fe9e6472febc0bd124b6b9 |
Second, we evaluate SpMV execution using multiple DPUs of the UPMEM PIM system (Section ). We evaluate SpMV execution using both 1D (Section REF ) and 2D (Section REF ) partitioning techniques, and compare them (Section REF ) using a wide variety of sparse matrices with diverse sparsity patterns. We select 22 represent... | m | 44ad4514b0895cffadfd2d9c6faa433c |
In an interactive environment, an agent follows a policy {{formula:b963f4d1-e1c7-4422-b854-570773b06e92}} that produces actions {{formula:7174522d-38ef-497d-bd52-6a454ddcd875}} , and receives rewards {{formula:b6c141e3-953b-4961-a19a-422317f5a2b3}} . The goal of reinforcement learning is to learn the policy {{formula:... | m | 834434cb621c70d3846ad14fd5007b45 |
Another related approach is transfer learning or meta-reinforcement learning, which aims to accelerate learning in new tasks from a previously-experienced family. One meta-RL approach {{cite:39d23876688d38a99f444e852e504c0ce8388eac}} uses a particular recurrent (memory-equipped) network architecture that learns general... | d | 7c399b1169fdd823921657f339d38fca |
The sample complexities that we obtained for tensor train completion with (REF ) and without side information (REF ) depend on the core coherence as {{formula:451d5aa2-a45a-4226-a341-5f00cdff4b8d}} . It is, thus, important to have a qualitative estimate of how large core coherence can be. Candès and Recht {{cite:a024d5... | d | 91e0e61fd1279d447b79a72ddfbc5e95 |
Theorem REF bounds the residual in the low rank
approximation {{formula:cf30b5c0-f4f4-437d-a788-e5e7e4818f8b}} according to criterion (REF ).
Like many subset selection bounds, the upper bound can
be achieved by artificially contrived matrices
{{cite:be05d0f1099379fac1155696da7d2823003cd7f4}}, but tends to be quantit... | m | 6f8065b4e8d86c94a9e7788a4523ef54 |
We have investigated the interaction of a relativistic fluid-jet with the radiation
field of the underlying accretion disc. We noticed that proper relativistic transformations of the radiative intensities from the local disc frame
to the observer frame are very important and these transformations
modify the magnitude a... | d | 794e7cc5b682cf43d0ed7fead54ce292 |
The initial wave of deep learning methods employed transfer learning {{cite:179a0f83a4b2259cdb2545dec5c832049e2e1f24}} {{cite:27569926e501ee8cd70358b4f692269246ac5497}} for recognizing the baggage threats followed by object detection {{cite:32a54326376f9277341d110e2b327e02ce9ec054}} {{cite:b76228dc8f17dd33b2f5be0a2b6a8... | m | 54f5df48b88d2eb277f882769bb2d80f |
To our best knowledge, the rate of convergence results in Theorems REF and REF are entirely new for SMM-type algorithms even under the i.i.d. data setting. Only almost sure convergence to stationary points were known before {{cite:c8479f2bfaac47f6d5d54bcbcfd0cb7410bf46c7}}, {{cite:cd1ff1938325b1c1e71032aec41adab44a39... | r | a4ce702b2433bce29ee88778ea375cfb |
According to the degree of geometric form symmetry, the crystals can be divided into different crystal systems and space groups{{cite:4fb5d9c30d07db66d3d392379c751388e67c945e}}, {{cite:7b836ae95e28b9946f557605293a9dc8d74e485d}}. Determining the symmetry information, particularly the space group, provides wide applicati... | i | 5f398e1bd3474959fb344a1efa491256 |
Applications to Interpretability: Interpretability of deep learning models is a concern in many domains. While gradient-based attribution methods are a common way to gain high-level insight into model predictions, the resulting saliency maps often lack the granularity needed for sufficient model understanding. Such sal... | d | 0f1f0dd96410b2ba4928681a292a027b |
Given an {{formula:a70e09f7-3b93-48a3-a5df-84e35be016e9}} -partite entangled pure state {{formula:1f6b773a-e0ec-44d5-a270-76963a2d764a}} with {{formula:db8f0d19-e872-41bf-b80d-ad0a4456d491}} , Werner states {{cite:8f27d139007d00ccfb21f1da97c3b21ee9c1865a}} of {{formula:3c93aa85-9cc5-4a83-bccd-b2e7b3c68122}} are defin... | r | 2155adc3c2ec9373d2c8c6cf0de326a5 |
We now proceed with results obtained by means of dynamical simulations of
the system's evolution. The latter are
performed by numerically integrating Eqs. (REF )-() using a high accuracy spectral method {{cite:1251c8833e3aa62a5b056392b6d07a0975eaef6b}}. The initial condition is borrowed from the soliton solution of Eq.... | r | 49e9837f87a0b137ec58ceec3af5c2d0 |
Expected decrease condition. There exists {{formula:1f849a29-5624-4623-820a-c515dbcbc74c}} such that, for each {{formula:dd95c71f-97e7-4b77-9e9d-a527603ee9fc}} at which {{formula:ac79bca1-b81b-48cb-8517-6f4a43f75ce7}} , we have {{formula:6936afd7-781b-4ad8-ac39-017af8752505}} .
Comparison to Lyapunov functions The de... | r | 0027db1ff883809569576e9a7543c189 |
Due to the breakthroughs in deep learning, numerous deep subspace clustering methods {{cite:017337f24e5114046bd138302be1065c9ed15e40}}, {{cite:48331e7e67ed0d77471924a9a691cff121765fd6}}, {{cite:6db33e54520f6556c9fcf64056e24d6eab6947f3}} were proposed to learn a nonlinear mapping of the data that is well-adapted to clus... | m | 2aef2c932e2b247b9a2db8f2956a53b8 |
In order to determine the polynomial {{formula:8fda4996-d7ac-4fe1-a730-ebf878ff7741}} , the parameter {{formula:d7966be2-2eee-4af7-af8a-a2d15bdc1ab1}} under the square root sign must be known and the expresion under the square root sign must be the square of a polynomial such that the requirement on {{formula:638cbe1a... | m | 1151fb50213872720d8c60bc34be0967 |
We take {{formula:d9eb5a11-f4f2-4eb3-8fb9-d0a25eb14769}} as the incident field on the the spherical particle to be trapped.
The Mie-Debye theory of optical tweezers {{cite:795bc5f36f570102532b0919b97214be0598be80}}, {{cite:606f4484741b8cca11cf65532faec1cb797d6033}}, {{cite:a375e6b40cad5fffbfe43b1c5b8596c63107ded2}} co... | m | beed47f15b3509f339b3345a98d9e952 |
There are several research directions we intend to pursue for future work. First, we acknowledge that although our method is scalable to optimize relative to other methods, optimization remains the computational bottleneck. We expect that porting our optimization scheme to the GPU, using fully-fused CUDA kernels for bo... | d | f67c226d672cb5579c242b8da1c3356d |
This section presents numerical results on two power systems. The first one is a small-size system especially designed for illustrative purposes. The second one is based on a realistic power system from Texas. All experiments have been carried out on a cluster with 21 Tb RAM, running Suse Leap 42 Linux distribution. Th... | r | cab6f2047db766a4af479875aa2b3ebd |
The BIC is defined as {{formula:15e673e9-f94b-4b87-addc-b0e2be9ccbeb}} , that is, the maximum likelihood plus a complexity correction term that contains the number of free parameters {{formula:dd6f5d19-bab9-4017-9589-ec44eead33f4}} and the sample size {{formula:b0389aca-b9d2-4807-994e-069acb88fb78}} , both {{formula:4... | i | d0af2a37297796324dade53376c263a8 |
The ML techniques have much potential applicability.
Power-grid stability prediction can be regarded as a classification problem and the problem is handled as such in this paper.
However, the cascading failure of a power system, which is also an important problem in the system stability analysis {{cite:6614be9779fd3f50... | d | 447c0ee7c5f9955ccd9615e95be91534 |
The system of PDEs derived in this paper also are interesting in their own right. The form is reminiscent of Patlak-Keller-Segel model {{cite:cae59b9aabf6a824cedb2ca6749e04b46a417a13}}, {{cite:550d53e5036f2d82f29e8491db65d03e5af604a4}}, with chemo-repellent rather than chemo-attractant and no diffusion of the chemical.... | d | d6c64b38246f777b3314525f6ffc0ce7 |
Given {{formula:9a8ba7d2-080b-46c0-848e-6e60f54ba1e0}} , the CE boils down to first estimating the tensor factor matrices.
Several techniques have been proposed to achieve this end, e.g., in {{cite:cf50676431faa68b6f662d78478a909d72fd1acb}}, {{cite:d51613a2ab5ab9eb69850a51bbea018c921345f1}}, {{cite:22d13cd9392fe8dec9f6... | m | 157ab0b624c68c20330d4f46fa2a6a23 |
We denote by {{formula:db86ec0f-ff74-4337-85d6-eafa3db3d67c}} a complete
filtered probability space, {{formula:2f821926-910e-4491-b14d-aaf97038fe4c}} is a sequence of independent random variables with the zero mean.
The filtration {{formula:b36b0c31-b7e7-46dc-9dc9-fda126408fa0}} is naturally generated by the sequenc... | r | 9f4132d45a07ded98f445442334320fa |
The structure of CVAR is shown in Figure REF .
The basic design of CVAR is generating a better embedding for item ID and replace the original unsufficiently trained embedding.
The Item ID in Figure REF denotes the original item ID embedding {{formula:f8b68443-22ad-4e39-a247-d5e03eeffd08}} trained by backbone model, w... | m | c17467afbbbef88fd58ecccd8bc64314 |
In the classical nucleation theory, it is assumed that the shape of the new phase nuclei is spherical and the nuclei do not contact with each other {{cite:e7b403585bbeb4a57ab0ce4285e9957dc464bc54}}, {{cite:03ebf26e107e1c417b706a5e4dfe6a6e643329f9}}, {{cite:b8ba7307c899cc800ab9335eee746eb494d7c111}}, {{cite:a953d22ea316... | i | b7756ba361b6edaf25544c9292b12a20 |
For WideResNet-50 {{cite:0ce2f8379caf8adae887d569f75ec29f2e213410}}, we plug the proposed WaveBlocks or AWBs after the stage 2 and 3. For DenseNet-121 {{cite:0e5afadba41fd1d23b38c9749f298b9fb16a27df}}, they are arranged after the Dense Block 2 and 3. The experiment results are shown in Table REF . In Duke-to-Market tas... | d | 98882e212b062037b05ee7e30d1dc9fe |
Domain Randomization: Building upon the work of {{cite:075af8099481270c2fcd2e24eed7552a02e56dba}}, this method proposes a new technique to bridge the domain gap, inspired by the domain randomization {{cite:f4f78557657f56af3fecb94a65abd855fd4325e3}} works in robotics. During training, the poses in the training set are g... | m | aeb57ec5e36fa33c6c9657baa3a9c71f |
Most of these methods can be roughly divided into two categories. The first category contains the neural architecture search (NAS) algorithms {{cite:d3bf122c1a6cb6512b0077b68c500f5f7a4b2dba}}, {{cite:9291798e181f817d000d94a15c4786b06b78d6c5}}, {{cite:220792ecd1649697429cbdb6dcc15cd293f3e965}}, which allow constructing ... | i | 2e65f192214de3b1224c86ce3df62584 |
We will use the formal definition of a decision tree used in {{cite:1223db04a5004dc662e4294943ffd49901b9527d}}: A decision tree is a pair
{{formula:bd4f62f4-75a7-45d7-bd4e-f74136223bd1}} , with {{formula:622491a2-5450-4941-b317-32fa46e129a9}} and {{formula:540afcca-8296-4601-a4c5-bd331aed1e74}} of the form {{formula:... | r | eec4e764f9edeaa08305e271651711aa |
The weighted Tchebycheff problem formulation has the form {{cite:a0128fa379042a49890fc572095cdfcb9b0e5123}}:
{{formula:76d548f0-a153-434c-923b-29a4c967b549}}
| m | c66048bf7635a5dc6795b7a01001f2c5 |
The remain of this paper is organized as follows. First, we
establish the free boundary value problem to the physical problem in
Section 2. The solvability of the free boundary value problem
follows from the standard variational approach, which has been
developed by Alt, Caffarelli and Friedman in the celebrated works
... | m | d350dd6f200bc7c25c763bf9e56e2073 |
Anomaly detection, which attempts to automatically predict abnormal/normal events in a given video sequence, has been actively studied in the field of computer vision. As a high-level computer vision task, anomaly detection aims to effectively distinguish abnormal and normal activities as well as anomaly categories in ... | i | 4a7a84a06b250487a12a738edb32efd6 |
Throughout this paper we consider {{formula:da843ef6-2351-4741-8dad-6f71c4ca270b}} as a finite group. {{formula:734039cd-7edf-4b47-a839-b1945dcc183e}} denotes the cardinality of the set {{formula:0304af84-13d9-4bcc-b32d-ba4d18fd0136}} For a prime {{formula:982009ac-a754-4cb1-b82c-993e449bba37}} a group {{formula:4c... | r | fca7bc52d301ed71d356831562576e2c |
Alternatively, if we are seeing the jet of Tol1326-379 at a small angle, its SED would be intrinsically
different from those of the FR Is. This would be the first indication of a
discrepant property between these two classes (other than the paucity of
extended radio emission in FR 0) and might be an important clue to u... | d | a30dfa5b2ed033d50fd2acf4fc2ab866 |
Quantum error correcting (QEC) codes {{cite:8f9d76bd5b82b5d1360897e08e667bc739a8cb33}}, {{cite:82f71ab346b405dde58e435355422c7579bb5e0d}}, {{cite:34f9d3f6a5d4879f3f528e6209b31597f64ff47e}} are vital for implementing fault-tolerant quantum computation and overcoming the noise present in quantum hardware {{cite:ef80a780a... | i | 6662e731d413f6b1090e332a6e9a581f |
Furthermore, we consider the use of the Pearson correlation coefficient as a limitation of our approach. When modeling the outbreak of a disease, it is especially important to properly reflect the points in time that correspond to high numbers of infections. Other correlation measures, like weighted variants of the Pea... | d | 776ba8ce0b85338c66ebac708ff12937 |
A U-Net architecture {{cite:8735f6dec0e309f6199662dc2cc2667e7e6b82f6}} with zero padded convolutions was trained using the Adam optimiser {{cite:138f952c5bc54268b0b7a5d1dc3398fbaadaa5a2}} with a learning rate of {{formula:4861729e-a0b8-4e8b-82de-4b5a23d109fe}} and standard {{formula:2ed47f0e-76e3-4919-8c98-1deac2cde0e... | m | 67a2e6e62fa9f1d584a81bfed8b0935c |
The results above shows that pure annihilation decays {{formula:999c453b-d4cd-4503-9145-54ef31ae59a1}} and {{formula:81bf5a5a-6813-4f17-8441-63314bd62ad0}} , which all cover two kinds of topological penguin diagrams contributions and the predictions are in the order of {{formula:fba0d818-58f8-47c9-aa84-78261ea8966a}} ... | r | 64e0801fbd68796b14caa77337c858ea |
Recently, various regression-based object counting methods {{cite:9d53237db29ebdaad35bc43ba9be564d6e6463e3}}, {{cite:b16df63e3c3841237f41f3de3a6c044a5ab30b71}}, {{cite:6dc4627d32bf0f1fd44806e3975b4d9df6395bdc}}, {{cite:2a10f4d50ab1d281880dc354d059ade7f6b8ba45}} regress a density map through different techniques such as... | m | 074ce5fd137e8837edec0a4f0c012520 |
where {{formula:7e7e9c35-d7a7-4c62-9648-f3c215a4cc77}} has {{formula:177fe119-209a-4930-a166-ea1f58d30442}} Lipschitz continuous gradient in Euclidean space and {{formula:56e69764-9baf-4480-80ba-34b171d13d16}} is the Stiefel manifold. Unlike the Euclidean distributed setting, problem (REF ) is defined on the Stiefel ... | i | 68e5e81ad449f1326bc1f5797f05bd90 |
The sensitivity of each feature based on input variation is recorded. This continuous observation makes the XAI practitioner justify different predictions at the end of the neural network. Rank assignment to various features is similar to deep explain. one drawback of deeplift approach is computationally expensive. Aft... | m | 46f473193b35b200d388703618cbba19 |
The inclusion of Pantheon to the CMB, shifts the mean values of the parameters to the {{formula:d7737a3c-7d24-4532-a3db-68bb9afbb61a}} CDM values but the indication for a non-cold DM ({{formula:fe152c05-9e98-4931-9fb4-f98abf5bc601}} at {{formula:4d1fca79-5df5-4fdc-b7de-5d0dd55d057b}} ) is maintained: {{formula:a9f6ce6... | r | 0f54eb7b97f070d82a5bde55b5ea2c10 |
In this section, we evaluate the proposed schemes through simulations.
There are 40 MTCDs uniformly distributed with a BS in the center.
We adopt the “data-centric” clustering technique in {{cite:1c3b478e8fa049dda6f79285bf1684f4d9f0fdf9}} for cluster formation of MTCDs,
the number of MTCGs is set as 12,
and the maximal... | r | 6763d7b96479f04d65afd708cdabd473 |
Autoregressive neural network (ARNN) is a modification of the artificial neural network (ANN), specifically designed for modelling nonlinear time series dataset {{cite:bb9bdf33441a762294cdf167d4ff63faed3453f7}}. ARNN model comprises of a single hidden layer embedded within its input layer and output layer. The ARNN{{fo... | m | 0d7f1f5004657f765d69f6fb14317c27 |
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