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Starting from the seminal work Pillonetto and De Nicolao {{cite:fc34eb3072b93cb013cadc8f553e18293acfa6a1}}, a paradigm shift known as the kernel-based approach has emerged in system identification which allows integrating side-information {{cite:432ac3ae4c83c181e474eac700a1a1d86eda479f}}, {{cite:9d97f77c282a4b6d1bf18e6... | i | 8d74a0005932cd0dd2ef70d006fc8f3d |
Consider a system of linear equations of the form {{formula:386cb5c1-181c-48ed-862e-0eafb90498f6}} . The GMRES method {{cite:ee2e9e9b9811d56fbca3a306343c146934c7748a}} is an iterative method which at the {{formula:c815b10f-2431-40eb-a2de-179638cd44d6}} th iteration uses the Arnoldi iteration {{cite:61f6693e26981bb28d46... | m | c13f4c4feec8d7e030c6aaff22569c0d |
This analysis has led to a number of key findings: (1) There are certain classes of network architectures that are specifically well-suited to neural encoding problems since they can identify associations within multivariate time series. Initially, we expected RNN based networks such as LSTMs to be best suited to these... | d | e8b1679a6134cd42b9ab3c06bade3695 |
In this study, we employed the IPW method that estimates ATE, adjusting the distribution, and comparing by groups.
Estimating the effect by comparing groups, such as IPW, is a common and traditional idea in causal inference.
On the other hand, various causal inference methods have been proposed, which can estimate Indi... | d | aa225060b6c814fc83df5942b0d6e436 |
As already mentioned, the computation of the stochastic reduced-order model (REF ) is costly. The cost of the reduced-order model in the moment-mean has complexity identical to classical deterministic model reduction methods and various state-of-the-art algorithms could be used to decrease the cost further. In fact, no... | d | ea0e661a27d5d535d6797044a09a901d |
In this section, we present the performance of federated learning under AirComp with different user scheduling schemes. The channel parameters are given as follows. There are {{formula:bc1927b2-5d18-418b-95b3-775406384865}} users uniformly distributed in a disk region with a cell size of 500 m. The transmit signal to ... | r | fb3eb52f757afd79195eb037311377bb |
The theoretical models currently available attribute the observed behavior to different underlying processes. In the PYTHIA 8.2 event generator {{cite:d25b166820763aa6f267497684d5d2853a0e3a87}}, multiparton interactions (MPI) are an important factor in charm-quark production. Indeed, from MPIs alone a stronger than lin... | r | 0ae205c265f937f0d057d725368379e4 |
As is shown in Fig.REF , XMorpher is based on DL-based registration networks{{cite:47cd16b1fbd784ba0b7bb0524a27af8220b07df2}}, {{cite:646bb994f033cd3deafc3f0e83a8e7ff2dc196ab}}, and it is used to extract and match moving and fixed features in registration for effective representation of input image pairs. The final rep... | m | d61ce31040a288bc87e7936431b99377 |
The shape of the X-ray spectrum of BL Lacertae depends also on the Galactic absorption assumed. The amount of absorption due to molecular hydrogen is not directly measurable, making the estimation of the total absorption along our line of sight uncertain. The Galactic atomic hydrogen column density toward BL Lacertae i... | d | 485b059235e750459bb7c3f71f55aee7 |
However, researchers have noticed that deep and complex models achieving high performance on clean training data are often vulnerable to small adversarial perturbations over input space {{cite:b03b654a4b2d2ac6b2733a362e309cebbaff582e}}, {{cite:6664471c3691ad1d896d82725ede09e7303868ea}}. Specifically designed, unnoticea... | i | 13694632277042293a0438d247c51f94 |
A key ingredient in our proof of {{formula:a9242538-89a9-41b8-b90e-ac1571e9ceb5}} is the fact that {{formula:4dd08078-1e33-4690-8269-b7492cbb14a1}} -terms can be encoded as natural numbers, and therefore as Church numerals, in an effective way.
This is related to the theory of self-interpreters in {{formula:a8a4939b-a... | d | 75a18202b53353e9d7c56d31a20d3259 |
Recently, physical layer security in MIMO systems have been widely investigated, which exploit the uniqueness and time-varying characteristics of channel to obtain secure transmission against eavesdropper{{cite:47fbfe489a2029c38b480b57c5dcb04f5b55be81}}, {{cite:71216f6fa2396604f6a69f60660adbb0306a7794}}, {{cite:d8e9d7f... | i | a39239b87d5cdcec7e9b1615445363c5 |
One of the challenges of performing COVID-19 classification based on user-labeled and expert-annotated data is label ambiguity. Since the COUGHVID dataset is crowdsourced, it cannot be known with absolute certainty if the cough recordings labeled as COVID-19 or healthy truly originated from people with the condition or... | m | b36cd8eb35568b45b0b120b915b22b70 |
We used near-term quantum algorithms such as Variational Quantum Eigensolver (VQE) and Entanglement Forging (EF) to obtain energies within the carved active spaces. We showed that the results obtained with VQE using the Qubit Coupled Cluster (QCC) ansatz and EF with a few Schmidt coefficients provided satisfactory resu... | d | b83bf8bc48ae7a553964a6e804177f23 |
Recently, deep learning techniques have been applied to offensive language detection task. Such methods usually rely on word embeddings to obtain text representation and then train them by sequential or convolutional models. Badjatiya et al. {{cite:dffdb6b7b84e76ba7a1353ff656bfb625b27fd88}} proposed the methods based o... | m | a278d234337cefe86fc5a66d2baddd62 |
Proposition 2.4 {{cite:2488360d3bd7e6aa4bd3932925f32b13e03ba4a2}} Let {{formula:302415b5-18a4-451c-8b14-b6216d40c65c}} where
{{formula:db4768af-e190-4e87-9be8-cb5b5327bb38}} is a nilpotent subgroup of {{formula:3210b36c-ffb6-4962-8f79-47d26c0b3dd5}} for all {{formula:b90dd260-3a08-4fb2-b460-edad37a76616}} and {{fo... | r | dc3d581812d57f4148167d48e34ff236 |
In this section we present three reconstruction results with the MR-BCDI technique described above.
Two of these are with numerically synthesized digital nanocrystals of gold, with coherent diffraction signal simulated in the far field.
In these numerical studies, one crystal has a slowly varying continuous distortion ... | r | fdd6efc4c44af8d19fd799970035c121 |
A WKB expansion is {{cite:668142015d37bfabd9055e31477080e903e6bb3a}}
{{formula:c074891d-0d1c-413d-86dd-2a0b7269adb7}}
| m | c7632b590ff6949b016ea61145c6ebf2 |
Independent learning (IL) decomposes an {{formula:4b794b4e-5391-457f-8352-3d665e08c1af}} -agent MARL problem into {{formula:63e5ddd9-f2ce-4c33-95a4-465bf0cadbbd}} decentralised single-agent problems where all other agents are treated as part of the environment, and learning policies that condition only on an agent's l... | i | 4d6c2821816d9afc1fad0ef9a7d3735c |
Each component was trained using the following six models:
{{formula:fc51318f-a4af-4aa8-a1e9-4fe0ee8e3046}} -NN {{cite:0e96b00525a15263f0a1406d9295e6eefefcaffc}},
MARS {{cite:08010733485861daa18ba47aab213b50c0ca9a39}},
SVR with kernel Radial {{cite:9775c14f7880838ffe3b00fbd8232b2653662df3}},
GLMBoost {{cite:942e1e02321... | m | 998aec698c6bda6092c63f1b837471bd |
The construction is done in {{cite:da5326c06e249801767b4aeff89757189a5cd6a6}}. The second part ({{formula:f3ab86ff-bebc-4fb9-aa55-4e5a25292b36}} ) is established analogously as {{cite:5afb02cd178d5a6f9c3446973d30558ccd3bd43e}}.
| r | d91429c23174f26398392f2531e6d730 |
until it disappears completely. The loop size {{formula:a8866577-1d88-43ad-aeb2-d95f6af25bb0}} has a distribution and for the largest loop one typically has {{formula:d311ed27-5036-4fae-8348-1dcf9d673a8d}}{{cite:e03dddaf5dd78fe07751a03447885b2e00455708}}, {{cite:57e161ddcba46754125764260e5efd557a0a2706}}. We consider ... | d | 5d4c22a395f4f074206b13bdfbf44ca3 |
Learning codes for longer blocklengths. As blocklength increases, outperforming the TD and TIN via deep learning becomes more challenging because TD and TIN implement the Turbo codes, the reliability of which improves as the blocklength increases.
In order to outperform TD and TIN for longer blocklengths, neural codes ... | d | 095fb7f7b4ba4bb5a9318ab34831a209 |
Random walks have been generally used in recommender systems when there is a need to elicit associations out of a graph structure (e.g., social network). By observing their behavior, random walks can be classified into two main categories: non-personalized and personalized random walks. The widely used web search ranki... | d | 67e5828b3acf960a4c65e4b647bb7a73 |
This section reports a thorough evaluation of the proposed framework for extracting and recognizing the contraband items. The purpose of these experiments is two-fold: 1) comparing the performance of our instance segmentation model (CIE-Net) with other state-of-the-art models {{cite:90dd5e72cd96b89fb7051814f90bfe0d687f... | r | 75eb39315ff0bc7b72f6f0df4b994324 |
Quantitative results are shown in Table REF . Here, the “baseline” indicates results from original image before stylization.
We can see that the CUT-based method obtains higher CMC similarity than a CycleGAN-based method {{cite:b1ea7f145deef02e80bf0049a8bb5d82cbdbbd22}}.
In terms of material classification consistency,... | r | 4a1904380b88f6bd9c3750fa59a69a6b |
Acquiring 3D human models from images is a long standing research topic in computer vision. When images from several viewpoints are available, multi-view stereo approaches (, {{cite:2cc3665f36c8640a25f28a58f694d42246cba970}}, {{cite:e3970bdcaa3bd71515db78e29dccc43a6d12c1f5}}), and their learning based extensions (, {{c... | i | b0e2723384db6728fc06eebe3470c2b9 |
Speech sounds (=acoustics) are produced as the coordinated movement of the speaking organs (=articulation). There are several available methods to model the relation of articulatory movements and the resulting speech signal (acoustic-to-articulatory inversion {{cite:844480de67cb3102d1946beeee3b2fdd36f8c6be}}, {{cite:b5... | i | fd06f988ec4f51500ee000ce7b9ba439 |
Our proposed Deep Code Knowledge Tracing (Code-DKT) model integrates deep knowledge tracing (DKT) {{cite:34fccb6fbe1648b6fb8ff294c63ae2df79e54746}}) with the code2vec classification algorithm {{cite:2c9123a6af8097ea5b7b45fd61be466f61c350c7}}. In this section we introduce the DKT model and how we enhance it with code fe... | m | 209e9a6130244d9b37af170d827fe5c3 |
While our theory accurately describes experiments, it has limitations. First, using it to optimize for a training distribution requires the knowledge of exact test distribution which could not be available at the time of training. Second, the theory requires an eigendecomposition of the kernel which is computationally ... | d | 4b4a0757ced58d58a559c517ecc55251 |
For DiWA, this is realistic as it selects the weights with the highest training validation accuracy from each run. For SWAD {{cite:720de44babaa050daa02c6ef7695aa2a0693b642}}, this is also realistic thanks to their overfit-aware weight selection strategy. In contrast, this assumption may not hold for MA {{cite:fe0ab0052... | d | 6bce37e96bb0808da1f6107f02a1f061 |
Following {{cite:01ec197fc16ce96ed31e4a4a4886eb4810b798ad}}, we conduct experiments on pre-training visual representations and then evaluating the learned representations using the linear evaluation protocol. In other words, after the pre-training stage, we fix the pre-trained feature encoder and then categorize test ... | m | 6f501902624473d5bd3e9898b6232f23 |
In {{cite:1656397434457f5171230d2d417cccf5fb15713f}}, the authors leveraged reported symptoms and perform feature-level and decision-level fusion with the OPENSMILE feature. They reported an AUC (Area Under Curve) of 0.79 in a subset of data crowdsourced from a mobile app. Pinkas et. al {{cite:af2ccb5d4a1c3c526e719414d... | i | 558a7db0489e7e82bb104c522b0fc2d6 |
{{cite:bb9015dfc20a93f96b16534c2b366f42974c7f57}} explore two possible sources of this error, specifically decoder search failures and early terminations. However, they admit that neither seems to fully explain the performance chasm.
In light of our work, the likely reason for the performance drop-off comes down to the... | d | f7ab6c7a92e5f7526f3ecf67ab6992ff |
StructNeRF facilitates indoor novel view synthesis given only sparse input images, the framework of which is shown in Fig. REF . Firstly, we obtain sparse point clouds and camera parameters from Structure-from-Motion (SfM). We then incorporate self-supervised depth estimation methods into the optimization of NeRF by im... | m | 36f2482bfe74c89dc7ace0d3a8d2a9e0 |
In this paper, we perform our calculations by using full-potential
local-orbital minimum-basis code (FPLO) {{cite:cc933389303b58b217e64aa0ab2c50add212c5b5}}, {{cite:b847b9b6ee8449a1877fbcd9ef36dfa9516dabb8}}. The scalar relativistic
exchange-correlation energy is treated within the generalized gradient
approximation (G... | m | eacb134d17a6c9e221c3ffdc8717f1f8 |
We report the results of experiments using these criteria in Table . Except from when {{formula:e06d7a92-cc70-453e-9cd3-c45425992cde}} includes images with different study numbers from {{formula:77e76829-ba7b-4fa5-9b5e-fbaafbdf931d}} , where there is a drop in performance, we see consistent large improvement from the ... | r | f6819453ea2de3b81ff6486d924b2da6 |
Qualitative Comparison.
Fig. REF shows some visual results for qualitative comparisons. We compare our method with current top-performing methods, ESRGAN {{cite:945a7c491319b217a758e4b6307ad4a43486e01a}}, RankSRGAN {{cite:52d7d829c95cb4d60839baa0f3da4b841c47703c}}, TTSR {{cite:1b543c0668aaa0f01e258878ba9ad1b8d33515d2}... | m | 9b2b6cfdbfb5b0b90dd41a6d8a999079 |
In this work, we propose D-REPTILE, a meta-learning algorithm specific to DST task. Following what {{cite:ce17ffb64f4556baea1feae6735aefebfb1b371e}} did for dialogue generation problem, we treat different domains as tasks for the meta-learning algorithm. Let {{formula:0ebbe072-1868-4e09-beb3-8e812067098a}} (eg. {{form... | m | fd10ac823e8bc4314bef6958ba315e77 |
We evaluate the computation implications of our method around inference latency and space-time trade off. We estimate the latency based on benchmarks and publicly available packages and performance data.
In the Natural Question experiment, we use the 1.56B XL model in Table REF , whose encoder output have dimension of ... | d | 009e7ce02e6adf3c91e58a082a5686d3 |
We summarize our results for the class imbalance case exemplified with the
CIFAR-10 dataset in tbl:resultsJ6. In fig:expstrat3, we see
an example of how the performance varies when we increase the non-IID-ness
factor p for the case when {{formula:d8557c0a-11ac-4c16-b968-7e5b9ac912c7}} .
In fig:baselineexpstrat3 we see ... | r | 75f0151024bf0ca4e5c89ff304be2d96 |
The recommender system (RS) {{cite:9448ddb2ae80ea3c7441687793c1fc0cc6b6bcbb}} is now recognized as one of the most indispensable and powerful intelligent daily-life assistants, that can offer accurate and personalized recommendation services for large-scale users. Owing to the wide deployment of RSs, users are alleviat... | i | ebadbfa1bbea3010e43f29522d1041da |
Personalization and Federated Transfer Learning:
The aim of Transfer Learning is to transfer learned knowledge from a specific domain or task to related domains or tasks.
Transfer learning methods are of particular interest in FL settings where the client's local data generating distributions are statistically heteroge... | d | dfed7f456a7fbedd200ee195eab23019 |
The control tasks with safety constraints on the state have been extensively studied in model predictive control (MPC) literature {{cite:10ea427cc2a6b82463baab20070ae9cfc78c95b4}}, {{cite:8e6ce9375d333970547a18ed6b949036b91310eb}} and the results are applied in various industrial processes {{cite:d72de73eca3b7e2de4a705... | i | be65700a0dc433e186b94dfcf53b9dab |
In this paper we study the effect of a small, non-Hamiltonian, time-dependent perturbation that is added to the system. Provided that the perturbation is small enough, the NHIM's will persist {{cite:d97cc5b146f95224a2c0eeba8d887dd33419ad1e}}, although periodic orbits inside the NHIM's may disappear.
Also, the transvers... | m | 4c9e90029d4113bc6510f654f7da4efd |
Aggregating customer engagement and collapsing several searches with the same query into one data point leads to a larger percentage of distinct queries and products in the data. For example, in one of our data sets 86% of the queries in the ACE data were distinct while only 49% in the ICE data were unique. Similarly, ... | r | d4b29ce69c50ac14a17900f5492d3c64 |
{{cite:d605dd31b2a3144acf34dfe20b5d6df418642063}} proposed a multi-experts model named RIDE for long-tailed classification. Our xERM framework can be easily applied to RIDE to validate its effectiveness on ensemble models. Note that the implementation of re-balanced method is open in RIDE, thus we applied LDAM loss {{c... | r | 32fe8c7b90ec1ef4e1e83a5846c8de15 |
Here, {{formula:d69ba9b3-a119-4a86-a7b4-764b4619ef28}} and {{formula:d7350fc4-7986-45a2-b7c9-df94bc7b09c5}} denote respectively the digamma function and trigamma function {{cite:67493b5864ee173076bf62e12c1c0c00946e7810}}.
| r | ec70788014995a40fc6723f6f5a1a726 |
Considering our results and findings from the literature, we believe that while being critical overparametrization is only one side of the coin. The other side of the coin involves more intelligent architectures for robust perception that we have not yet discovered (e.g. similar the ones implemented by the brain). For ... | d | aa400d149baea34892e07cb20786d382 |
On the other side, in the non-{{formula:e23bb700-4c8c-4eb9-b675-8eb59e18d85b}} -smooth scenario, the uniform stability theory can be applied to analyze Stochastic Langevin Gradient Descent (SGLD) method {{cite:6c6d9477347ea35c65c6b73d17a7f444e83422c9}}, {{cite:b1085b0e20ef1e6cb260b9d26fe518458771ea64}}, {{cite:01b9f636... | r | 36422485cd78b43cbe0d33768e3e4a7a |
In this section, we present novel data poisoning attacks to evaluate the ease with which current UDA methods can be fooled into producing a representation that leads to a large error on the target domainThe code is available at https://github.com/akshaymehra24/LimitsOfUDA.
We propose three methods to generate poisoned... | m | cac08eed99ccf8b917d6f459a251c58a |
Eq. (REF ) needs few lines of comment. We rearrange the operator product {{formula:a1354d79-2ae9-4eb5-8aac-1b88894789e2}} asWe remind that {{formula:f7140d14-0c49-4532-a2af-30ad8b021e44}} (see {{cite:6be292e62a440fe344e6f13bbea134c79de58d21}})
{{formula:fd884343-a108-4f0c-a5c6-6215c210eae3}}
| i | eb4e773fdef4f09042b61183a4ac970f |
Theorem A.2 ({{cite:7f216b0ba91b4cd0369d48a78ad6ed145aafa3be}})
Let {{formula:25deb606-2a25-42d0-be73-7ae7d94bdbad}} be slowly varying
and {{formula:d8273e1a-af40-4458-9ca3-57c2b13bb8c3}} . Then for any {{formula:42519dba-00cc-4ddc-8e75-6d248acdc1d0}}
{{formula:dc3265c4-15c0-4928-8c99-08d7391ef8b8}}
| r | f63c073e14b9392c5c45f542894e7edc |
When investigating different training objectives, we find that knowledge-intensive tasks like ranking better mitigate forgetting compared to span prediction. Although the fine-tuned models generally contained less factual knowledge, with significant (and expected) forgetting in the last layers, Rank-MSMarco remembers r... | d | b4296e91e4b11f2c4d2d1dbeba2e38dc |
The Weyl tensor is the independent component of the Riemann tensor that is not captured by the Ricci tensor, which is the traceless part of the Riemann curvature tensor, and in {{formula:0da1ff62-6112-44e6-ad2b-017ab648f5db}} dimensions it is expressed as {{cite:47eddb814425b62180e915aefade5f08bbe91aef}}
{{formula:e29... | i | 18b420b254c2d48de75a79bd48414414 |
Our goal is to minimize the reconstruction error on unperturbed test data, from perturbed training data. Specifically, we assume that the data we observe comes from a perturbed distribution {{formula:26202470-dcac-4d24-a399-a34e0895a006}} that is the mixture of an unperturbed distribution {{formula:a4191d4b-6065-4708-... | m | acb1765d5e7b19c9664af6463eea25d8 |
Our research presents timely studies on how some recently-proposed parameter-efficient tuning methods, or prompt modules, fare in computer vision problems. Crucially, our studies expose a critical issue that, for any specific downstream dataset, hand-designing an optimal prompt module is extremely challenging. More imp... | d | cc32d9c9f49f7dfeb85c6575bc8fec27 |
There are several effects related to the vacuum in quantum field theory: the zero point energy, Casimir effect, and dynamical Casimir effect (which is like a moving mirror). There are three more quantum vacuum effects that are due to a curved spacetime: metric quantum fluctuations, vacuum polarization, and Hawking part... | d | 7ec41f3d5dc5e27c966b09494d4d95c5 |
Other feature attribution methods can generally be categorized into two streams: perturbation-based and gradient-based approaches.
The former perturb some parts of the input and use the change in the output as a measure of feature relevance {{cite:0750b6d95f28c8c1f56366d202c46df0feabb21f}}.
Such methods are sensitive t... | m | e27c2282a06691e6423d2a46e4412335 |
We propose using visual semantic information from image scenes to contextualised and aid predictions.
Providing the model with access to scene information can instil probability priors which can be utilised in cases of high uncertainty, such as differentiating between the character `S' and digit `5'.
We build upon the ... | i | d10d44c928bff974ba0666576c3166bc |
We follow the experimental settings of AlignMixup {{cite:e2ac9088f5916e2343ee751be152035d86075174}} and use {{formula:be5a4255-0123-48bb-ae5f-2f5469269025}} {{formula:2fc8169f-b5b8-4e31-9364-47e7689eb495}} {{formula:4102182a-4d9a-4674-81f8-7f8e8325518e}} -ball for FGSM {{cite:becf8b0217dc21649ad009f5353a83666a139b55}... | r | 54cb5703d0c29eb845eb47b34edd43ac |
Fourier Measurement Process:
All previous experiments used a measurement matrix {{formula:d35e4560-4267-4758-a75b-63c0d5abef3f}} containing Gaussian i.i.d. entries. We now consider the case where the measurement matrix is a subsampled Fourier matrix. That is, for a 2D image {{formula:c8028183-5daa-47b7-b5e5-651abe693... | r | 1f82127c1fb9709a48b591ca1fc351fb |
Now, to characterize the ordered state, i.e., the flocking state when all individuals move along the same direction in unison, we consider the order parameter, {{formula:52941d5a-2b0a-4158-a371-a6e4df4995fa}} , to describe the degree of the collective order as the absolute value of the averaged normalized velocity of t... | r | 772ee0a0d91e3c605089fe4a0738f9ec |
which is broadly consistent both with the Planck CMB {{cite:98ccd06718189ce0f18f21bac47d58387555bcfb}}
and the weak lensing measurements {{cite:e93198ab66f02b98bae60fe6b06f47bc7100b536}}.
| r | a91e746788ddef222202a8eed10e5f76 |
For the deep neural network model architecture we follow the recent advancements in the field of image recognition and segmentation, using an encoder-decoder style convolutional neural network (CNN) with elements from the SegNet {{cite:8fb6cff3808d37fd53725b4610be11d8a8a76623}}, ResNet {{cite:74ba118450dd0805e0846eaf1d... | i | f0a3db238c1560f516ceb394e3460dba |
where {{formula:a0dfe800-e7d0-4776-adb7-4a71f83f7bd3}} and {{formula:433e9373-ab86-468d-9229-178ae04108ec}} are regularization parameters.
The authors solve this problem by an algorithm called LARS-EN {{cite:2e20db6405ca9e6b798a9bf2353d168be1ae3b9e}}, which we will abuse the notation and refer it also as ENet below.
| m | 9d487b48404df2f0f7eaf0f4be97eed2 |
Recent work has shown that asking language models to use step-by-step reasoning improves performance on reasoning tasks {{cite:d1bc07d645fb6c61641ae60a1c5a03ebe5d38a6d}}, {{cite:caf6a906756c75ec8c204e33463b770aa4a0bdc8}}, {{cite:90b69446169f19c096cdc391d1308cacc8a50ae9}}, {{cite:133affe16b8982bfd46d4d4eea48244360168b84... | i | bff11b1b24d5bdc3a5f0a975a204fb1e |
Unfortunately for our ambitions at interstellar construction, this proves difficult to implement in detail. Firstly, the answers depend sensitively on the UV completion to the effective theory of FFE. We compute corrections to the relevant dispersion relation to the Alfven waves subject to some conservative assumptions... | i | cb9a236b1a1a0419742cc6c253a3d01b |
Encoder: U-Net {{cite:864ab8de4f3830ff13480955ebd010fa5f730eb3}} is essentially an auto-encoder with skip connections from the encoder layers to the corresponding decoder layers to allow the flow of global information and gradients across the network. In our encoder, we use {{formula:099104ee-dd41-4ca9-9164-a73ec745abc... | m | b7ed6ee35d9d588e71cb09079c19a50f |
The work of K. Cumanan and A. G. Burr was supported by H2020- MSCA-RISE-2015 under grant number 690750. This work was also supported in part by the U.K. Engineering and Physical Sciences Research Council under Grant EP/N020391/1. The work of H. Q. Ngo was supported by the UK Research and Innovation Future Leaders Fello... | i | 22088009b175ede23942881aa05059de |
While our work addresses important questions on sample efficiency and compositionality, we note a few possible limitations of our proposed benchmark. CVR is quite extensive in terms of the visual relations it contains but it can always be further improved in its use of elementary visual relations. For example, the shap... | d | 324b3e53320904f3f6224a398a352841 |
with the Hamilton-Jacobi-Bellman (HJB) equation associated with the value function. The transformation makes the HJB a PDE that is linear in {{formula:741ca5f5-b02c-4fe8-b6f1-0834328f7908}} . The HJB is usually solved backward in time. However, the linearity allows us to reverse the direction of computation, replacing ... | m | ad3a5a8d0b56d6f1fc82dce0435b7835 |
provided the initial profile {{formula:d3ab4419-21b8-4c2b-96f1-388b57d81b08}} has compact support {{cite:deccea42f6c886491293f54203fc716df194b2a1}}, {{cite:b70f74085d09ab860b61415d401cb0746ee80996}}, {{cite:577e6391a12a7d66634c52c25966c34762c96bd3}}.
{{figure:85dd9e88-95f3-47e2-8660-cce5d1ca01e2}} | r | 33ce8a9838d7c7600df3862742622b9d |
A {{formula:58d68d1f-de74-43a1-96d8-76bade71d8f4}} -automatic set is a subset {{formula:65c30089-f9cd-4e3a-b982-33e85d68e6a6}} of {{formula:ff1a11c7-b5d7-4533-b239-48f864778b22}} whose elements, {{formula:ef99f648-bcfb-4e30-93a9-c18b5d42a6af}} , are recognisable by a deterministic finite automaton taking the base {{f... | i | c9112828c754361640200ea9ddc5ff13 |
Although RISs can be used to improve the end-to-end channel quality, their main limitation is that the equivalent value of the reflected channel (base-station (BS) to RIS to user) is weak compared to the direct channel between the transmitter and the receiver, since this channel is the product of the BS-RIS channel and... | i | c5c4883591bb62b35152d600e385349b |
GCL setting.
In {{cite:cf227de85fe4b4d70f1d0233518457b72cdb5a23}}, MNIST-360 dataset is proposed to validate general continual learning setting, where the task boundaries are not available.
We compare our method with ER, MER, GSS, and A-GEM-R.
A-GEM-R is a variant of A-GEM with a reservoir replay buffer.
The results ar... | r | 03c4b589ca8fa5889e33a51df05d9714 |
Propositions REF and
REF show that clipped long-term
constraints can be bounded by combining the algorithms of
{{cite:4f20d2fa5df1c6303cd4efc2344780468c959220}}, {{cite:5001c128837ca1fb5eb91ee52c9a0da47dc42933}} with our augmented
Lagrangian.
Although these results are similar in part to our Propositions REF and REF... | r | 8acaede63f424ae5ca5daf6cc1dcd698 |
Detection of gravitational wave (GW) from the distant compact binary coalescence has now become quite common since the first operation runs of LIGO started in 2015 {{cite:e85f5edefa2d494656e9b69f963d70251353435f}}, and up to now about hundred events have been found {{cite:c417302396197560f60ca6fdcd8f594cf4aa4dd4}}, {{c... | i | 4998b98b43013aae08eded302b53f5cb |
For the 4D temporal segmentation, the proposed method was compared to four other state of the art methods, two variants of the demons algorithm from Insight ToolKit (ITK) {{cite:2edf1884da586ff8d520d48a7e48cfa44f04700b}}, optical flow from the Scikit-Image {{cite:2cdacc4c9b2058cfedbe030d3fe29f60dd4fec66}}, {{cite:52725... | r | acdd579d3b75a966b803dc7cd510fe71 |
All {{formula:d85e99df-68ff-4ec2-927d-f10e0ae69c62}} predictions of the {{formula:caf82247-2630-4391-9dc2-3bfa0d52d22f}} CDM, CPL, and {{formula:a1034696-5c23-45b8-9fd6-46dee44b1d52}} models with all {{formula:c13e5577-bd1f-4163-aaa3-be0c3acc6657}} data fit
the Planck 2018 estimation of the Hubble constant {{cite:83... | d | b188ffe2c96aacbd13ca187cb85137ca |
The level transition of an axion cloud due to the tidal interaction is formulated in Ref. {{cite:e919d2ea106aac21e5baa937e25591d7e416c1a0}}.
We develop this work further in the following respects.
In the previous works, the main focus was only on the effect of the leading quadrupole moment of the tidal potential.
Howev... | i | c2995af960e24a1459504e1d81e9d13c |
Easier access to information via co-ethnic migrants is not only positively correlated with export from the recipient to the home countries, but also facilitates “chain migration”: it is typical for communities with a significant presence of expatriates to attract more migrants from the same communities. This feature is... | i | a2cd8ecc6b6ef0b7ba3a7a034ee404a1 |
This paper focuses on the affordance grounding task, i.e., given an action label and an object image, our goal is to generate the heatmap where the action happens. However, in Gibson's definition of affordance, “it implies the complementarity of the animal and the environment” {{cite:6205ae4a359cceb21f4ec54bd6f15d01410... | i | 743976fc32cdabfb635ef6c0b9131f87 |
Table REF shows the classification accuracy on the VOC2007 dataset, and Fig REF shows the results from our model. We observe that again our accuracy is comparable to RINCE ({{cite:e7202f8122c1521865946b634b7999a1643970a1}}) and SupCL ({{cite:9439249f138148943b9497d3f7382454847bfc4d}}), while the discriminative classi... | r | cb4131ce6aa96215575f2a7bb579a81b |
In fig. REF , I display both, the TOV equation solution {{cite:3d714854c3c936d8ee02c56f10bd1bbd588c170d}}, as well the calculated value for the dimensionless tidal parameter {{formula:74f8de7f-454c-4a82-be95-66ecd38ea2c7}} . The BPS EoS is used to modeling the neutron star crust {{cite:2f26ce924c380ab43f97b6a8b039fb68f... | r | 45b73e94a57ae32cb10abee5ae388bdd |
The physics that is appropriate to analyze the motions of fluid masses in the atmosphere, ocean (and mantle) is that of stationary turbulent flow (e.g. {{cite:a6020af3e271b0de0353cbb857c93d8b6b428923}}, {{cite:78993faf2e4846be1f13db8edb7ec6691d812d4a}}). The large Hadley, Ferrel and polar cells are generally interprete... | d | 6d0b2fc5d5a950e2cb51ea3402ac5547 |
We point any reader interested in background information on LCP, in particular the definition of sufficient matrices, to the work of {{cite:05f6ad7f6fb73d8cb8f38b4370e2dd9dbef49ad4}}. Additionally, we note that Assumption is not extremely restrictive. For example, the claim of Assumption is satisfied when upLCP resul... | i | 7d0b202364ce153054cf5ca86b9ac1ab |
While other large-width scaling regimes can preserve some non-linearity and allow for certain types
of feature learning {{cite:2e3fb2ce278a3994b484e4d0ef5c4652a659814c}}, {{cite:13398720b69b1d2c172b15989e8cd0ae4e2d94bb}}, such approaches tend to
focus on the small learning-rate or continuous-time dynamics. In contrast,... | i | 7995f628bf4567f0d5fedca73b5459d1 |
An initial-value problem for a parabolic partial differential equation is said to be well-posed if there is short-time existence, uniqueness, and continuous dependence of the solution on the data in a given topology. Often well-posedness is proved by setting up a contraction mapping argument in appropriate function spa... | i | 7937cd1d4c5b70acc3b5cb26c8ec61e5 |
Next, we combine the projected 2D pose features and scene depth in a common voxel space and regress the 3D body pose heatmaps with a V2V network {{cite:b1cfb5131f5b6157599bc240edd93153f6949b2a}}. The 3D voxel representation projects the 2D poses and depth information from the distorted fisheye camera space to the canon... | i | 3b073b46eaedbd97f0a71e666292ecac |
The vector representation (SVG) could be further compacted via Ramer-Peucker-Douglas algorithm {{cite:d5eeebe2471de837b45d18f55addde1ad78a5492}}, {{cite:ebd18a686243cf7e34a2dc9add025b7e360bbda7}} or constrained to compose of elements from a strict set of geometric primitives (e.g. only straight strokes).
Since the ve... | d | dd27cbd9de379e93732950d9ada49dc7 |
For completeness, we also trained the speaker-embedding system using the VicReg objective proposed in {{cite:2d38fab08e3692e72fdd258434b0df7b2450ef65}}. This objective similar to Barlow Twins, exchanges the variance term with a hinge loss function and adds a term minimizing the MSE between embedding vectors. We denote ... | r | 51f8ac87420bc96983207f44a69b40a1 |
The established variational principle is imposed by batch normalization. {{cite:13da82af0d62190ff3b34e7dc09305f6b1beaf89}} proposes batch normalization to keep the variance and mean of representations constant across layers, which significantly enhances training. Despite its conceptual simplicity, the inner workings of... | i | d5dccc20b068a5fc2303a1ef888fb7a3 |
The origins of response-adaptive procedures can be traced back to {{cite:11897f9466b67dbc53767b059411baecf3e6540b}}, who first suggested to allocate patients to the more effective treatment arm via a posterior probability computed using interim data. This work motivated procedures (commonly known as Thompson sampling) ... | m | ff0fd3dec9ef8b8a0ece8a2f14f5a906 |
In summary, our contributions are as follows:
(1) We present a novel method of incorporating supervision into an RL framework (A3C) by introducing an information-theoretic regularization for improving the sample efficiency in target-driven visual navigation policy learning.
(2) We propose a visual navigation model that... | i | cfb32844f8e5ac4a523211b50bfa6617 |
We train RoBERTa-base models with our implementation of different position embedding methods including absolute, Shaw, Raffel, M2, M4, DeBERTa, TUPE and M4M. Table REF shows the accuracy of different embedding methods on MNLI, SST-2 and SQuAD 1.1 dataset. Absolute is the default absolute position embedding which resul... | m | 1c1f18c4913c8b6479474bfa11e16ef1 |
When there are no binaries in the stellar system, the long-range forces are the most expensive computing part in simulations.
The direct pair-force summation for all particles in the PP method requires a computing cost of {{formula:0cc06331-206e-4d84-b51d-6f143ff2c80b}} , while the cost for the PT method scales as {{fo... | m | 81c0257dbf9fae98a6f75e3a384e5490 |
Now we can plot {{formula:c1bdd903-d1c5-4592-9912-4e1de2c5e102}} as a function of {{formula:533bc994-2d10-43f5-8fc9-b5c71aecd273}} . From the
dotted curves in Fig.(REF ), we see that the plot which is
fitted to a straight line has slop {{formula:2437943f-142b-472f-834f-10bfa1a75947}} , that is the critical
exponent. T... | m | 1dded271e6d24d8883b76f374eeead81 |
The overall identification accuracies of each neural networks are summarized in Table REF .
When the image features extracted by the pre-trained VGG16 were fed to Vearch,
Vearch performed much better to match images than what it did when the pre-trained AlexNet was used.
Its accuracy value is 88% which is higher than t... | m | b4e624abf88fa94524d37061daf58fd0 |
In text GAN’s, generators are often pre-trained using maximum likelihood estimation (MLE) and teacher forcing. Teacher forcing feeds the model with the ground truth sequence at every time step instead of using previous predictions. While effective during training, this creates a discrepancy between training and inferen... | m | 19dcc5ebc5ffbefd71bed0b173c50f85 |
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