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The main priority for developing further quantum PINN is to address their current low accuracy results. In all our tests, in practice, reducing the error below a certain value or increasing the convergence in a finite number of iterations has been challenging. This is likely related to the barren plateau problem {{cite... | d | ea6915cdf1b990ea9a3bceccd0fc49a2 |
There also exists an intent in the opposite way, especially after the work of Aharanov and Bohm {{cite:540fd4b71ca77c843fd4beacc605f15e47771f2d}}, to think of {{formula:b3f0bfa6-f62f-4744-aed9-e6656d145401}} as physical reality. Another support for this idea is that the Coulomb field always accompanies a charge, and c... | d | 8bbb261f41239686ee758ab976c8223e |
Our proof of concept aims at replacing the calculation of the probability of interaction via Compton scattering. This probability is a function of the weights of the macro-particles, their momentum and a normalization factor. We formulate this task as a regression problem {{cite:bcf6c49875c925db54faf551dfe689b0b16e71c4... | m | b10415a1cf9cf55f7d6aae42168f191a |
In order to investigate the variability time scales of optical continuum light curves, we use standard methods like Lomb-Scargle {{cite:7526acc854dc85001a22774cfd9177cfad475108}}, {{cite:fd8b390d9801ea84dd1e030cffe8549d106ee841}}, and sine function fitting, that we use here to determine variability time scales. Here we... | m | f6f2cff75ba074273f1d484740db4a5c |
The optimization program in Eq. (REF ) includes data-{{formula:d3a6fa75-5bbf-49a5-802c-8283d0bf10f3}} norm and DTVs. It would be interesting to investigate additional designs of optimization programs and their associated algorithms for potentially further lowering the minimal-angular ranges obtained with the optimizat... | d | e66c99ae622cb36b7035c30c75c5bddc |
table:fid shows comparisons of all the considered models using the FID score {{cite:116d99c6ea12033cb1553d3b3547ba7f5ab3a60a}} for the MNIST {{cite:ca4aa3067c0c95005006c2d0fb600a0d0a1715b3}}, FMNIST {{cite:d75ba693d55b46b1cc7b3544159da7b34c84b97d}}, SVHN {{cite:f70e1eb956842365871b8f36c3b11c5e727bb4b0}}, and CIFAR-10 {... | r | 26f1136c5c346a79414fcfadc2aeda49 |
We extend conventional factor analysis{{cite:7e4d2c7798be1771df99f8d85c8a344927fbe9af}}, {{cite:388cbecf1270359145e2acd1225a2af1e1163908}} by introducing a global-local shrinkage prior{{cite:41648a6615b006244854521d9115bac76435c5da}} on the scales of each weight {{formula:e3587eed-9821-4c43-9971-6d376a16842b}} .
{{form... | m | 02acee50ef4b8eb50aa55de9d8795642 |
As the jet velocity is typically close to the escape velocity, {{formula:f56f81bd-d071-4311-991a-953780a5dde1}} (where {{formula:754263b3-67e6-4691-8221-4c3d0bef6e89}} and {{formula:44ac43c7-44d5-4c95-a18c-35c51e110824}} are the mass and radius of the object which is launching the jet), then equation REF leads to {... | d | 98ac91612bacaf6f26b506179defb5e4 |
The double Wick rotated geometry may have problems with causality in the Lorentzian frame. We make use of a Killing vector to analyze the causal structure of the background. A Killing vector {{formula:09b76a41-5047-4985-9edd-971ec5c21b1e}} has the norm {{formula:e7af453a-7348-4122-b1f3-2e7e482d6911}} , which is spacel... | d | 4d6de560fd53d226e0c12bdd994bbe67 |
Given these similarities in results, even our very simple model may be able to lend insight into potential ways to deal with spread of conspiracy beliefs – though we in no way mean for these to be taken as policy recommendations. Our analysis revealed that the three most important factors in swaying any agent's belief ... | d | 7d1eb7b75202cf7c0b9e0fda26a0e228 |
Our experiments also highlight the well-known challenge of graph learning in heterophilous scenarios. This is currently an active area of research {{cite:1d85d2f2435e607827256a37fe1ee743d54a0213}} and existing work is less established. We hope our proposed edgewise metrics can help the related research, and in general ... | d | ee79f487e9b8b737b1398579b7ee6b92 |
Based on the assumption that the process leading to the ubiquitous emission of X-rays – the Comptonization of seed photons
produced by the accretion disk – is the same in all BH systems regardless of their mass, this method can in principle be extended to any BH including the supermassive BHs at the cores of AGN. In th... | m | 95ef71003c677ebdfd12460e13ab9e3d |
To investigate the effectiveness of HVT, we
compare our method with DeiT {{cite:11be2a1cceff55b98797a485e380eecb6a6deb08}} and a BERT-based pruning method PoWER-BERT {{cite:e0ccfdfb4cb2f09ba7db33636c08526a449c116e}}. DeiT is a representative Vision Transformer and PoWER progressively prunes unimportant tokens in pretra... | m | 08942b07f2208c246ec5cc1b379961ed |
The linear TECs of the considered phases of ZnF{{formula:0a5f3e2b-ddc0-40fd-921f-55917ebabbb2}} and BeF{{formula:096d0b75-d4f3-4430-bed5-b61ada79f00f}} are calculated within the Grüneisen formalism following the procedure described
in Refs. [Gruneisen26v10,Barron80v29,Schelling03v68,Ding15v5,Gan15v92,Liu17v121,Liu18v... | m | d800e28c49859b08cca0be25228ac196 |
Via numerical simulations, we investigate the scaling and robustness
properties of our strategy for 1D lattice models. As a benchmark goal,
we aim to variationally prepare the ground state, {{formula:da325a7c-d419-4b51-9437-9ac9201b0ddf}} ,
of the Su-Schrieffer-Heeger (SSH) Hamiltonian {{cite:af374cd68570701e149011a7f9... | i | 9be5133c3489a84f264fe8e09f4fecdd |
Deep neural networks achieve state-of-the-art performance in many tasks, , image classification, object detection, and instance segmentation, but they are vulnerable to adversarial attacks. A small perturbation that is imperceptible to humans can mislead a neural network's prediction {{cite:a943c97c01e0f4e82aa78d573f6... | i | dcb230c3c34cb916e45b0e456e533280 |
Assumption is standard in the analysis of SGD-based methods and has been used in many relevant works {{cite:e8b058ec9aac0263f04fd25ffdc57df7f1100953}}, {{cite:ff3634bb2a8c11c49946ff35c4ec303a6c8f8d06}}, {{cite:946b24c7ef364393797133fa0f8081bc495afeb2}}, {{cite:f8fffec6f84c616db32c156fa853d4faf9289940}}, {{cite:d1587... | r | edc7ca0b53542ac62a02ae2f4d794153 |
Among the existing data structures for static all-pairs approximate shortest
paths, the approximate distance oracle of Thorup and Zwick {{cite:5c00d77d629fe94d5bd949182abecddcc0b10c88}}
stands out due to its amazing features.
Thorup and Zwick {{cite:5c00d77d629fe94d5bd949182abecddcc0b10c88}} showed that an
undirected g... | r | 0b0a9c88e84ad8c30b120551440c3c42 |
[rgb]0,0,0Although VAD has been researched for years,
developing a model to detect anomalies in videos remains challenging, as it requires the model to understand the inherent differences between normal and abnormal events, especially anomalous events that are rare and vary substantially.
Previous works treat VAD as an... | i | 954cbadfdc473e64494c9cea24ccb54b |
With this lemma in hand, we now prove the theorem.
Let {{formula:884360ff-7d3b-4ba7-a116-ef0c74ff9960}} be a structure that is preserved by
{{formula:53db10b5-0eee-4bcf-8644-4da60f9ccb38}} or by {{formula:6557790d-15e5-4ad1-8213-9fdce2141a78}} .
We now present an algorithm for {{formula:f671c44c-5ec5-4cba-a9a0-55c022... | r | b5032c46e85a8eec4d1fa68e62edb1f5 |
The values of {{formula:32e029f1-c019-41b1-972d-85d41d22edd7}} are given by {{cite:e86a1e15635751f62034f303fe5edd68ff9ece2c}}
{{formula:b6e43157-3270-4423-afd0-c2ec919bac9b}}
| r | 7413ee6e584e12f7746ef93ea58a0916 |
The simulations in our scenario analyses show behaviour comparable to Cambodia with parameters consistent with literature and expert elicitation. Many of the model parameters are location-specific such as the bite rate, relative infectiousness of asymptomatic carriers, probability of death from radical cure and the ini... | d | 1501dd33cae1c6fa7e19ca982c1dc19e |
For the first-principles study of NbAs slabs, we construct films of (001) orientation to allow direct comparison of our computed Fermi surfaces with the available ARPES results, which have been experimentally measured for the cleaved (001) surfaces {{cite:5c88802344db6db32a54c7db1f0c84c4b1d720a3}}, {{cite:6c7e26ebb3eda... | m | 66cd09fbe84488af5ca5cbaab8d8d03f |
The results of the six transfer tasks of the ImageCLEF-DA dataset are shown in Table REF . Although the number of images in each subdomain in the ImageCLEF-DA dataset is similar, it is still challenging for the transfer task because of the images from various scenarios. Compared to ResNet-50, which only utilizes source... | r | c846b4c467fa801c26e5f30f71bd840e |
Fully supervised: train the deep learner only on the annotated data, as a fully supervised task.
Semi-supervised by transfer learning: create a representation of the data by training with a self-supervised task on the unlabeled data, then train a simple model on the ensuing representation in a supervised manner. This... | m | 1e5e25af8b0b510202f233c0e146e945 |
1. The one-dimensional GLL equation with pseudoscalar {{formula:1bdbf46d-2f3a-400a-bce9-97042d00fdbf}} -function potential is reduced to the Schrödinger
equation with an effective {{formula:d00c3a36-e83f-452e-a1ba-613ba1566b73}} -function potential the coupling constant of which depends on both the energy of the Fermi ... | d | 55af15153c9fbae4540c5e292b549168 |
where {{formula:a5267655-d964-4cdc-a927-d6663b675f6b}} .
Importantly, the advantage function {{formula:5202a4f0-104f-44aa-bf54-fabef484948f}} appearing in (REF ) is the average-reward advantage function, defined as the bias minus the action-bias, and not the discounted advantage function. The constraint set {{formula:... | m | ffbd4d9feece651958bf2f224193de77 |
There are three different approaches described in {{cite:0bb0be00d94e56be64669f18b3f159bdc5897676}} to deal with the hierarchical classification problem:
| m | 315246935233f0ba84d1e5c6f12a3959 |
In {{cite:f44cc1fa0fd4071007ec65a19b67a23d0850732a}}, the authors present a new method, ESPIRiT, for sensitivity map estimation using the SVD of the calibration matrix. The approach is combined with regularized least-squares reconstruction ideas and is shown to offer similar performance to GRAPPA {{cite:b75072180d542cb... | i | d24d311d3aaadfb0d7e31451fd00b29c |
Remark 1.5 The uniqueness of the discrete uniformization factor was proved by Thurston {{cite:316b4c29d051710434b214fb8afd86415ba72cef}} in terms of the rigidity of circle packing metrics with respect to the discrete curvature.
The existence of arbitrarily dense, {{formula:eaad225a-ca99-476b-862f-e1751afab02b}} -regula... | r | 86255520965fad10d23825e9f57f789a |
In Kalman's celebrated paper with Bucy, it is shown that the problem
of minimum variance estimation is dual to a deterministic optimal
control problem {{cite:d684ad28078f0be3315db6bf05dbb07e24adb7d3}}. Duality offers a constructive proof technique to
derive the Kalman filter equation from the optimal control solution {... | i | 8763df14bc88bd163b35f4f87bd682fb |
One promising testing ground are Weyl- or Dirac-semimetals {{cite:155f5c2f6dbf7ef8ab2a6bfe7816a3c5ecd9151d}}, {{cite:ca4ebca1888bce282602d1237142929a6175adc3}}, {{cite:a8a05a63564dabeb9a62f39a43c65a96bb1b29b4}}, {{cite:45130bf0375aaaf78f06df6d1488208e9af265e1}}, {{cite:c68bcf42b1618c8f3ed77646a841c61f3d0a34b1}}, {{cite... | d | 9ba453e71c605a407b8c42ff5de72fe4 |
Our MAE pre-training, using only IN1K, can generalize better: the gain over training from scratch is bigger for higher-capacity models. It follows a trend similar to the JFT-300M supervised pre-training in {{cite:aa9a21fdd6868296cede97d8e26669e20b47502b}}. This comparison shows that our MAE can help scale up model size... | r | 954d1dc3b980ea1ef3d9d89e37de2292 |
In Fig. REF and Fig. REF , qualitative results of ReSTR on the Gref dataset are presented.
Pixel-level predictions and the results post-processed with DenseCRF {{cite:1ddb314b4d2a8030cbf95a4202738a4861e86597}} are provided together.
The results show that ReSTR successfully segments masks of the target entities descr... | r | be75d7a1cbcb36c152fe79a201f985db |
Within the DEM formalism, there are various kinds of contact laws to determine the contact force {{formula:74663b6a-e45a-44e9-ad9e-35eee80d5dde}} . One of the most widely used models is the linear spring dashpot model introduced by Cundall and Strack {{cite:ab5f0f1e2f378e0647a800a51af65ecccc39716a}}. The normal compone... | m | 9e8f4d7cc90afce6b7262449cb5add3e |
Self-supervised methods have emerged as a promising approach to achieving appealing results in various applications without requiring labeled examples. This is typically done via pretext tasks closely related to the downstream tasks of interest and typically differs from domain to domain. For example, in the image doma... | i | 9dff56ba4cefb30374b84fa83ee3a5e2 |
Another important question is how to calculate {{formula:739feb08-f89b-4fd2-83ce-fc421aa30e79}} for general assemblages: a big result here would be to classify for which assemblages the steering weight gives the optimal value, since this is easily calculable. This question is a direct analogy to asking when the “fully... | d | b75607ceea2f00094bc5a911fc2fddac |
Much of the current interest in Majorana particles is focused towards quasiparticle excitations in condensed matter physics (e.g. {{cite:694363cf62290352d6b612128f295541ad1917f0}}, {{cite:e60c10f0a674ee48399f3f7a25007d328ad42f4d}}, {{cite:1e23364867e838bdd9382b08744747de3d5cdf8d}}, {{cite:4c54a3dbd8fb145796c814a6bfb24c... | d | 13931722084984aaaeef0ffbe140bacb |
All experiments were carried out using the Python programming language with standard Python libraries Pytorch {{cite:a44ace4973b41afa5b6ebad3f100b368e7d5bd22}} and scikit-learn. Before describing the experiments in detail, we first describe the evaluation measures and comparative approaches used.
| r | 0a6ac045d23285a4282274d85b80d1ec |
Given the recent discussion in the field about label quality in datasets {{cite:6ab303cc2c824d5248b1d123dace4b688ca13ea2}} and the "success" that crowd-based re-annotation had for the FER+ dataset, here we presented results of re-annotation experiments on the popular AffectNet dataset.
| d | 9f7a6387462cbdaff1d6f2e28ad5612e |
{{cite:96d8838b6e4a3de91756c1fc5d0c7cd2fc20224c}} and {{cite:704e63742fddf1c01e6d9160181df95087b1f395}}
challenged the usefulness of statistical confounder selection (without
structural knowledge) and suggested that other modern techniques such
as regularization and model averaging are more suitable than variable
selec... | d | be2508e2b2ae39045ae33af145240218 |
Distance Metric We consider three distance metrics for the nearest-neighbor matching is Sec. REF , which are cosine distance {{cite:ba746deef6fcc0a3dd0995616c994ea589af3d5d}}, Euclidean distance {{cite:1f8a630f273b158d3da2da0d072a5f8b0996ba8d}}, and earth mover's distance {{cite:24358def0d322cec6c5173bb333738edfb775b7d... | r | 5c3598e966e492ef127a1be1225e35e7 |
The Alternating Direction Method of Multipliers (ADMM) algorithm {{cite:fa6286b0d5d2c62b2bd28d1a453ca84b87b00704}} is designed to solve problems of the form
{{formula:46a4bcd6-745e-408e-b338-d5e06346f337}}
| m | ef704e9e7ae5d4513afbef95687725cb |
In contrast, all the indices of the 3-fold state change continuously but largely in Fig.REF , where {{formula:34e0c838-1db5-4e39-b6b4-405ea96737d0}} decreases from 490 to 0 in segment B.
The index for the degree of nonaxisymmetry, {{formula:0898d925-63f6-4175-a9b1-314b4edc3580}} , monotonically increases after being... | d | 313614e1e99c1728b2bae726a5745ee8 |
One of the ultimate goals of Video Question Answering (VideoQA) systems is to assist people in solving problems in everyday life {{cite:36dea1c47f516c72645abbf3516124abfabfc03e}}, {{cite:dc663357b9787043490cfc014cf086e36da10af2}}, {{cite:f123215a4b883f4f8b326a95082db86ded8548d9}}, e.g., helping users find something, re... | i | a81c2b072cfff6169c2ad5f39bdc92b8 |
Unfortunately, to the best of our knowledge, there exists no holistic solution to reliably estimate the confidence score for the task of document information extraction. Current confidence score approaches are either generic methods verified only for simple image classification tasks {{cite:aad1b007dc5a7f2022ebfcb3c652... | i | 9a02b2ccf370b6bb220bf7faf53767c4 |
NĂśrlund's logarithmic means with respect to the trigonometric system was
studied by Tkebuchava {{cite:5121adabc4da655bdddd201a52f27d7d66e2dae7}}, {{cite:56d92ad0545d0b4ba8f206d64d365cf0ddee3771}}. The convergence
and divergence of this means with respect to the Walsh systems was discussed
in {{cite:3fbba0d92312fe89fe8... | m | 2e176331a4439256a23b0cf422dac572 |
For comparison with other representation types and encoders, we use MeshCNN {{cite:1c06d411f5a793c2d065a28f901eaf1cf78367f1}} for meshes, and Pointnet++ {{cite:714ef83309ef50778717af0ae9a58354266e165a}} for point clouds. We use Pointnet++ over DGCNN {{cite:9a68b86795621a8280ca38374bb7210ebaaadcf7}} or Pointnet {{cite:4... | r | 6399c0e12c2d6eb4c9e91ae976b9e61a |
The offline coreset construction of {{cite:d1feb532c217d949d7c53d727c1dc39fb4745123}} has the following structure: first, a bicriterion approximation is computed. Second, points are sampled according a distribution that depends only on the distances between points of the input and their assigned bicriterion centers.
Th... | r | 87388aac919a4efa7ef9a434dec84b3b |
In Table REF ,
we show the domain generalisation results on the three benchmark datasets.
The baselines are divided into two groups: non-causality-based methods
(from DeepAll {{cite:af238ec5fa9fc085c7f2e44685d073dc1e128e76}} to FACT {{cite:9e346b2bf4aa54db150ce83cb3b204e8b5acaf94}}),
and causality-based methods (from M... | r | 68b48e1e8af471e301654a23cc34bcf2 |
Robustness to input corruptions. In this experiment, we investigate the robustness of DDPM-based representations. First, we learn pixel classifiers on the clean images using the DDPM and SwAV representations on the Bedroom-28 and Horse-21 datasets. Then, 18 diverse corruption types, adopted from {{cite:b66b9a65f37f7f2... | d | 8b5a8155afe4fb7af06e607adf40d7b6 |
Latent structure. We also envision extending the sing algorithm to learn the
structure of graphical models with latent variables and partial observations. For Gaussian
distributions, {{cite:e31db3355c5a5b5e0438fd775e5bbadcf1aed744}} proposes a penalized maximum likelihood approach to
identify a precision matrix with sp... | d | 2b7d2cb21319e3fd1ccf592544a7a795 |
We have chosen three data sets. The first is a small molecular water system in the liquid state {{cite:731461a8b7df6a039204bd84c2bcecb24b14e169}} containing 32 H{{formula:24d73319-6500-464e-bf53-fdcb654b410e}} O
and a single Hydronium ion. The system is a single time frame of a CPMD {{cite:2d14d955f8b271b7e55a755ba44dd... | m | 59468ecd5fc18ee31abac1fb3339bacb |
be the affine space whose coordinates are indexed by rays in {{formula:eeca5d7c-553d-4c94-8e9c-1331fd51781b}} . In {{cite:9535e92bc2dc796124b7436eb98dcbcfa7d4ce15}} and {{cite:badcd2d85f5612cf597b0355d112f1ba48178df6}}, Cox showed that every projective toric variety {{formula:828ab198-d6ac-4347-82a2-5cdce14de0f4}} is ... | i | 81bfb0be2ea941b5a1c765ecb1d7a11a |
In this paper, we showed that an EF1+fPO allocation can be computed in pseudo-polynomial time, thus improving upon the result of Barman et al. {{cite:537939dc5f8f39d2fa03c27b7e8254522e093854}}. Our work also implies polynomial time algorithms for two special cases: (i) computing EF1+fPO allocation for {{formula:40837cb... | d | ec7896208d5ecf0c75f16a22ea77004c |
Pre-trained language models are few-shot learners, i.e., GPT-3 {{cite:fd576c51b2137c774448cb84e739c56182777cb0}} that surprisingly perform generation tasks from a few examples without any further gradient updates. Although it lacks a rigorously theoretical proof, prompt learning inherits the few-shot
property {{cite:d2... | i | 1c7dbe236c4b836445927d4a1e81c4f9 |
By other hand, black holes are probably one of the most suitable objects in nature to test – at least theoretically – some features of quantum gravity. For example, logarithmic corrections to the area-entropy law seems to be a common characteristic shared by different proposals of quantum gravity {{cite:da4229ea9806116... | i | 36bf27d4dcbb6158ee6f5be8fd310d61 |
A key component of multi-object tracking techniques involves constructing newborn object tracks (i.e., track initialization).
In GNN, MHT and JPDA techniques, newborn objects are constructed directly from unassociated measurements using application specific procedures {{cite:e783f9da1d099c1b79bbaa72f0a1e22a2677414f}}, ... | i | c38bf9f0ed3e64af8fad6698f60ae4e9 |
Atrous/dilated convolutions {{cite:abd7c28ec60bf728672b3187d0315f413be405a8}} have successfully demonstrated capturing the spatial and contextual information, and simultaneously also reducing the computational complexity of deep CNNs with wider receptive fields. The popularity of atrous convolutions has led to its adop... | i | 21b68cd076b1d6f8ed7b4620dc9ccf04 |
To set our approach into broader perspective, we also shortly discuss limits with moving objects, like satellite-{{cite:27f90c5619402654a6da5ee1e0fd8457e0647147}}, {{cite:f17fe7ccb96d094143440ce40d76d4818cdc8457}} or emerging drone-based {{cite:2f6c997db7460d0c1841d9a1c3348838ea476018}}, {{cite:5e3d05a757c2b12a7d0e3c0a... | d | 897d30d574af71ca3ba1b8223e174835 |
To address the CDFSC problem, numerous Domain Adaptation (DA) methods {{cite:cc82205a084e5b33292844499360804dc61b2240}}, {{cite:7c5e4d508b603ca9bfff302e45a37815c53b6afb}}, {{cite:e5d357b4384e77dabda54c0b60013253ab5b307a}} aiming at minimizing the impact of domain shift between the source and target domains are certainl... | i | d41664ac0df85607d75bb11867a1da2f |
Vertical shift in KIITI (Fig REF ) has high ID {{formula:aa8b9b20-4876-44a8-b1b8-ab63e28bf27a}} 187 whereas the normal data has ID {{formula:87273370-722b-48d3-b37e-5cf92f0854d5}} 84 at pool1 layer. It may be because of irrelevant features like filling of resized image with interpolation that attribute to increase in... | r | 4a1472e44d8180599846b4f8ed434986 |
For the sake of simplicity, in Eq. (REF ) and hereafter, we fix the lattice spacing {{formula:6ceea550-ed21-4816-a729-41f0b62caa95}} and the time step {{formula:13689bb3-225e-4e12-863f-c184cd1a52eb}} to one. The left-hand side of Eq. (REF ) rules the streaming of {{formula:3dacfc59-b3ba-4a14-afb2-c63b60277065}} on t... | m | 253fb9e5a72ec282f64f3c2d5cb80cc7 |
Here I will follow other studies (e.g., {{cite:720e5d303c16cf4e6be7bda05e19ff166d876f69}}, {{cite:0f1e7e6fb0c28862b9adfb7606096a9c99a068d7}}) that explore the relation between the direction of the jets' axis, i.e., the axis along the two opposite jets, and the kick velocity direction. These studies assume that two late... | i | 616bb3143e38caf8021baf8d2e8a7288 |
We have proposed an extractor-paraphraser framework, which is inspired from the extractor-abstractor (EXT-ABS) framework introduced by {{cite:2343f909ace1effe15c4aae657e257645e2458cd}}. The summarization function {{formula:a60c5904-82d4-4fe6-8276-fc5bfa74ed3a}} is approximated as the composition, {{formula:e77c48e4-23... | m | cc4d5e518936a0ac16d3e30917ab7891 |
A CNN with 16 layers was designed and trained to analyse the algorithm accordingly {{cite:b0d7196d1375138d9916b105c7ee02daecad9c5e}}. The network consisted of residual connections after every 4th block and used batch normalization to improve the understanding of the data. The network was deep enough to understand intri... | r | a7e2474c4c300c807df7d9a1f7e990dd |
Currently the neuromorphic hardware community grows fast and it is probably only a matter of time until new or updated hardware systems will emerge.
The field of applications is very broad for such hardware, including the estimation of linear models, like LASSO {{cite:44580d8bf1c099aecd462574517964868dcdd9c5}}, {{cite:... | d | ee4ef482a8dac2079e814ba1e0d8587c |
Section describes the Moffatt and Kimura (MK) model. This is followed by Sec. where the Hamiltonian structure of the MK model is given, which is essential for our analysis. Here we discover two constants of motion for the MK model. One invariant serves as the Hamiltonian for its noncanonical Hamiltonian formulation (... | i | 403fe427ccfd45d581254d6e3d6467e5 |
Our basic setup relies on Qgraf {{cite:6ac230308fa7faadb3c3b8fffb30b200294f909f}} and
Form {{cite:edeacebc140035805e33a9b9ae54454c709e7744}}, {{cite:ddbc12c90e58b797d28c16f63e0939a2bf338df8}}, {{cite:0d54a8747a591d482b8a8e2ad170dfd11903751b}},
employing the program Minos {{cite:96618c64e0c2b99cda2461497e34edccd27b2857}... | m | 47cdeb205426653d613edf6c7caa0d7e |
Our results clearly show that the parametric statistical methods used for group fMRI analysis with the packages SPM, FSL and AFNI can produce FWE-corrected cluster p-values that are erroneous, being spuriously low and inflating statistical significance. This calls into question the validity of countless published fMRI ... | d | 8ec39ca600a1d86124e062a4371ffa82 |
Generally, our main contributions are highlighted as follows: 1) We propose a weakly-supervised approach based on subset scanning over the activations of the inner layers of a pre-trained skin disease classifier to detect OOD samples across two use cases: detection of OOD samples from different collection protocol and ... | i | c64220d1cccabd9a3a4b86a045bf83bc |
Another potential issue with refs. {{cite:6c8b5360e7ce201795ddfeca1a5f0606690a3c21}}, {{cite:6fe0b53c0c3314e1e455a17e9a9ee4c0c34ea1fb}}
is that the C̆ech approach is not so widely used in contemporary
works on twistor approaches to field theory. Instead, it is more
common to use the language of differential forms, wher... | i | a577bba3ee3fcbe5aace9034102f24a6 |
No doubt to say, the effectiveness of such an edge/cloud collaborative architecture depends on the accuracy of the predictor that is responsible for differentiating `easy' and `difficult' inputs for the edge DNN model (see Fig. REF ). Mis-predicted `easy' inputs result in accuracy loss while mis-predicted `difficult' i... | i | 1638b12e638ba30ababa17074022fcca |
The raw event camera output is composed of a sequence of events, {{formula:fb7326ba-3455-4f83-96d0-57746eebd727}} , where {{formula:83eb0fad-b3dc-4524-99dd-fbcacc2b2c91}} indicates brightness change with polarity {{formula:fe19193d-40f2-4287-9728-b173dfd16b33}} at pixel location {{formula:7a43ca6a-7c05-4d6b-b073-f9fe... | m | 2fcb482db3825a01e4d4d122ba8f1bed |
To ensure that the robot tracks these commands, one possibility is to simultaneously train a low-level controller using RL that converts the high-level velocity and gait commands into joint torques. Such a scheme has two drawbacks: (i) sim-to-real transfer issues and (ii) large data requirement for training. Another po... | m | 306e51e040bc88fd16979dc829f93b57 |
The network embedding problem that we have addressed in this study is distinct from the more popular node embedding, which aims at mapping individual nodes into a low-dimensional space such that their pairwise similarity is preserved as much as possible. Examples of node embedding include DeepWalk {{cite:c3421ed127b80d... | d | 9d1047dffaa52d3cc2aecd64040f7e56 |
It is worth mentioning that in {{cite:26f4460cfe2d63c6b078c5e05687e6af85fd9fb9}}, the authors have employed the PINNs framework to approximate the Euler equations that model high-speed aerodynamic flows; both the forward and inverse problems in one-dimensional and two-dimensional domains were studied carefully. Here we... | r | 2015e2e9ba1306fbfef1720cf51372e4 |
The main motivation for using data storage and computing at the edge stems from the desire to make high quality computing resources available closer to the users and to reduce the need for end devices to exchange private data with centralized servers . There is an ongoing trend to deploy machine learning (ML) algorithm... | i | f007db123a8bb2f1cccb58b3f6754070 |
All eight of the stars are relatively low mass, {{formula:b0a8d134-fded-4e03-bbc0-97e11f35b007}} , so
none could be a significant (relative to the initial mass of a
neutron star progenitor) mass gainer. They also all have
relatively low proper motions (see Fig. REF ), so none are candidates for
a disrupted, short perio... | d | 8cb91ef135a48090ba3dc15ae69f23d8 |
NH{{formula:a1e8064e-24d5-40d0-ae6d-649919901f28}} has been detected in disequilibrium in some cooler T dwarfs
{{cite:4cc0a30ab1c8b804397621ed7d966ddd09ebc70d}} and Y dwarfs {{cite:6e5d6e105d8ef0df920b5c15a1607b560768dc43}}.
Overall our coolest disequilibrium model at 500 K, for
{{formula:85a31586-0cf9-4ca4-bab0-fd7e6... | d | ee0c35ca78ef1004b2445d3264df14ef |
(2) In the theory of general relativity, the Einstein's equations are assumptions {{cite:dc0685bc1e53f49c526e7fd942170f90a4594c42}}, {{cite:c03a041e73bf3ee325952cb5a79cc4bf79cca231}}, {{cite:3d33c1d4a93c2480767bc1ad25f3656fdb167c24}}. Although A. Einstein introduced his new concept of gravitational aether ({{cite:88d6d... | d | ad5f5a9607b9a29447ac2a0b314095c4 |
Equal task split scenarios: Exemplar-free comparison.
In Table REF we compare our method with other exemplar-free approaches from the state-of-the-art on the equal task split scenario. We observe that our method outperforms PASS {{cite:3978eb1cc93fc30ff256f38b674e62ed77e25801}} (that uses self-supervision for guiding... | r | dea70b136dcb1799ef96d219d3d44255 |
During the past 25 years, observations from IRAS, ISO,
and Spitzer have revealed substantial mid-infrared (mid-IR)
excesses associated with hundreds of normal main sequence stars
{{cite:cf43306a729232b383e7c9e12239cd3f9d45740f}}, {{cite:b79b5f21f14f75df28c22ae86e1d04c10cf91498}}, {{cite:7616a256c978424eae5a721ae7d84f39... | i | b5b61a23b0b826a66b538c976e64894a |
With regards to the choice of ML model, we admit that there is no unique model that is capable of forecasting all types of data more accurately than other models under all conditions {{cite:b5ff6f389d7d4cd3bbed38000ba96b26cdd677d0}}. However, various empirical studies suggest some effective models for certain types of... | m | 13441eec8e86f41e8c0dc0ac69ff2aa7 |
We first investigate the kinds of networks where the bimodal distribution of cascade sizes can emerge. In particular, we consider a vital property of networks, the average degree {{formula:d3007dd4-0ac4-4b6e-a3f0-e9156737dc2a}} , which tremendously affects the ability of networks to withstand failures. As for informati... | r | 2d0d224c22dae900dd852d7bd02807ee |
Recently, the combination of Monte Carlo optimization and neural networks has gained increasing popularity. These approaches include both using MCMC processes to find optimal weights in ANNs {{cite:1684e8a8e420d88f6a5f2e7bc281f7027a43a39b}}, {{cite:8ed9c2d818bd2c6bbe2fa2f4e0d41708733a74ab}} and using ANNs as parametriz... | d | d886bed4088d89bd2b4d914ed35863ab |
For details, see Da Prato & Zabczyk {{cite:ac1b79bcefb92d4ba67f0ce10ec0ba545e9039a6}}.
| r | 53f214099159daa9dcb19431a621d9e5 |
Large-scale crises, such as pandemics, natural disasters, and social crises, drastically shift and reshape the physical and mental well-being of millions. Understanding emotions that people increasingly take to social media to express during large-scale crises can have wide implications for a deeper understanding of th... | i | 53b0130e697e6d84965b9e6d51697eb4 |
The data set employed for this analysis is the atomic database of the National Institute of Standards and Technology (NIST) {{cite:69862a7ed57fd5c7a495a9793cac2026ec98aca3}}. Complete data for all transitions were downloaded and a computer program was written to apply various criteria to identify transitions of interes... | r | 1d597015bb60bc5587bcfd278b6a877e |
To address these issues, several authors have proposed methods for adapting zero-shot models to a task of interest using labeled data {{cite:21f2ec40c8f2336d53c98e3454c0283e0aa8ad8b}}, {{cite:658a56be9d408a5d3676753af4844d88f6694f66}}, {{cite:50146ed1c87ad8e5f3b89ea2dde6579df05abfb2}}, {{cite:0771db6868b511ac0b0b4acce9... | i | c61995f8701fa5ed50b7ec8cd6193362 |
Notice in the beginning of discussion of small gaps between primes, we consider the indicator function and pick {{formula:ebb74de2-d31b-4d00-89a3-e05de731b091}} to satisfy
{{formula:58586cd8-9beb-4233-ae58-e2b518b56c64}}
which basically is a weighted sum. The things we really want to show is {{formula:615d7086-f365-... | d | b3bdf183ca8068fe3316c5d0aa21a1db |
Other features that were added in the current feature set characterize user behavior in a community earlier, before the current state. Furthermore, we added modularity {{cite:7b4b51bb244ad34e3b388626e5f4b53ac9efaa10}} of a snapshot where a user appears to the set as well. The list of features can be found in the Table.... | r | 596ee6a75c6e7b92d42f25099d1a9c66 |
Interestingly, The Depth Contrast pretrained model, which is fine-tuned in a semi-supervised setting using only 20% of labeled data in experiment 6, achieves a comparable F1 score of 0.65. The proposed method, Depth Contrast, clearly outperforms by 11% on ImageNet {{cite:5f91a5a4aa4c3bc31a69fe3096441ecce90c4921}} pretr... | d | 8c571d055635191e85ac6f973d8a3a5b |
where {{formula:bfa0e5a4-6b89-4d0d-bc85-af25279e80d8}} is one time series sampled from {{formula:b89e9cab-1638-4409-83ba-bdc9e63ee306}} with probability {{formula:69faebe3-32e5-4e96-8eec-26627c16bd4e}} .
We also sample {{formula:c7353cb4-0ebd-4553-b319-e521059ddad2}} from the nearest cluster {{formula:1ef6b806-ee56... | m | cff81aeae8abc4d378c72613eb6d05ba |
is positive and grows in {{formula:b8f5aec0-1436-4fb3-a286-7cfcd1f5c942}} . Since {{formula:d23c1749-104e-44d6-b52c-63357a3a65ee}} and {{formula:17aed44f-2afb-46a9-b263-4602642e5e8c}} become negligible compared to {{formula:79614bc0-dcb3-4797-9e18-19a18282e5af}} as {{formula:e96db563-4772-41ad-8b0b-8ffa8ae5ff5f}} ... | r | 2dc5098348090143ecc92652e44f2127 |
In this paper, complex networks are expressed in the form of simplex complexes. The purpose of this paper is to derive the adjacency matrix and obtain the reconstruction model of the complex network by estimating the probability of edge connection between different nodes using the maximum likelihood estimation (MLE) an... | m | d09bd368a7dd26eaa4ea04561c9056f6 |
In this part, we illustrate more results generated by different methods. The listed images are sampled from nuScenes {{cite:cc343004bd0181da83dfc2f56153218d689d78aa}}, Argoverse {{cite:4f1cf0e09b61ef50539493119a57ed641549154e}}, KITTI Raw, KITTI Odometry, and KITTI 3D Object {{cite:1ae0cdfcea8cbca2a5bb97dc3a067f8ebf... | r | 7b70aeea1d896a3e408bdbb5f85b6816 |
In our work we have followed the augmentations used by the authors in {{cite:255cb49b1fd1b5f16ab397460e8ae873113012c8}}. They are summarized in the table REF .
{{table:58b43686-c42c-449c-97e6-3bfecce7ca89}} | m | b30e5675d49645e445ef53b1378bbb5a |
On the algorithm side, our bounds applies to the practically used version of SGD, going beyond the SGLD studied in most uniform stability-based works. Compared with the anisotropic noise of SGD, SGLD takes an isotropic noise and has different behaviors. Our analysis also works on full-batch GD. On the model side, our b... | r | 52414ce7cf85ed077c6b147bcdef854e |
The results of HD-VILA on video QA are shown in Table REF . We can find that our model outperforms existing methods on five tasks in all the three datasets, with 1.2, 1.2 and 0.2 absolute improvements on Action, Trans and Frame tasks with TGIF-QA dataset. The limited gain of Frame is because Frame focuses on one frame ... | r | 4dad221ce7a7b6f176c829970b5a0730 |
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