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In the quantum field theory, redundant coupling parameters can be removed by applying a local field redefinition, while physical observables like particle masses and scattering amplitudes are independent of field redefinitions {{cite:826f5d525966a90c5790e669cc936f777618e5bd}}. In this letter, we investigated the possib... | d | 650f70f2c07ce78d0c0d36253e1da0f0 |
We note that our results are mostly concerned when {{formula:25239aba-9567-4dbb-aa52-941ccc8464f0}} is odd. Such condition comes from the necessity that the function (REF ) be non-negative. Essentially, this also explains why the assumption that {{formula:2fb0984d-ccb5-4759-962f-b6c6055e7450}} is odd appears in {{cit... | d | ab2b3156973a1ddf501dcc900f7967c6 |
BLEU: BLEU score is a common automatic evaluation method for majority NLP tasks {{cite:92422a605c7b1dbc663e8e380455b4fdeaa116fa}}, {{cite:c7cb2e5c4c035d609ae7112d872a646573f08e14}}, which score the n-gram overlaps between the generated sequences and reference sequences. For our experiment we report the BLEU-4 score.
... | r | cfac5e6331c9d432f010767793087c20 |
Intuitively, it is possible to learn general features by training a model to simultaneously do well on multiple tasks. Recent work in NLP started to show promising results on learning a generalist model with multi-task learning {{cite:35fd980d7f978afef369b528096d2564308e112d}}, {{cite:4ca7bae83e79208e61f247d1613f9bfc89... | i | ea0ae0214b3e33e0cdc8337e24278a24 |
Let {{formula:6a8286fb-1080-4f2e-8662-0732f55e19e1}} denote the set of the first {{formula:314b198f-3462-4362-b98e-7d5d7edfe5d3}} natural numbers. Let
{{formula:c377f8de-5377-4317-b120-cbfe495700af}} ({{formula:b66909b6-ebd4-4df8-a220-b50fe93a4a96}} ) denote
the largest (least) integer less (greater) than or equal t... | r | ca9ccee30d15b5ddf1dfbe8e6e044426 |
Datasets.
Our data was gathered using Pushshift {{cite:11294999c4eb07e2efe9b46c231ba83d10ab18ea}} and comprised of
all the comments and posts made on Reddit during the period from 01/2015 to
04/2020. In total, this included 5B comments and 684M posts from 39M unique
users. For the analysis presented in this section, w... | m | 2c28413d1f627756dcc7ae66b1e87a9f |
The proof technique is inspired by the arguments from {{cite:20e00768fcbb54c8a1db6bd41cab81dd1c5d7d9e}} developed for analyzing stochastic model-based algorithms, with several new elements along developed for handling the challenges introduced by the model averaging and partial participation mechanisms associated with ... | r | 6dc7d1e33040b527321194d2c789b305 |
As another baseline, we compare the performance of our method against SuperGlue {{cite:8d6342b1cfe0b322f4ef30c91ed8ae4fc8a7f873}}. While initially proposed for pose estimation and homography, SuperGlue has achieved state-of-the-art performance for feature matching {{cite:2d640a3ef0c43be0db9168c3c8dc6f169b12f412}}, {{ci... | m | af99d829c6e32faecdb508f62c7b1ac0 |
In addition to the score function, each neuron of a hidden layer also has an activation function that makes the network nonlinear. Several activation functions such as sigmoid, hyperbolic tangent (Tanh) and rectified linear unit (ReLU) functions have been used as the activation function. While some studies such as {{ci... | r | 7af2844d1e23b8e9b31d9477f61852a5 |
Data Augmentations: Following {{cite:ba4d613f7cca3db0306bf8990e3b85b62664aa97}}, {{cite:c3366af2273b17d5fb2520d461f13a0cd4f57d2f}}, {{cite:8824194439b3339438ea6342bb468578e231457b}}, we use horizontal flipping, random cropping, Gaussian blurring, and color jittering for augmentation. We also use a multi-temporal-resolu... | r | 85f9ef844bb78b39d0411fadf9b801e4 |
We see SQL variants being the top performing amongst off- and on-policy agents. Generally, Q-learning performs worse than policy gradients however, when comparing their combinatorial counterparts (SQL vs SPG), we see this trend reversed. Generally the greedy baseline helps SPG versus using a critic, consistent with {{c... | r | fd642fb74d330af9e30e9e1e3c2e405c |
Recently, the concept of being able to engineer materials for the fine control of water flow through nanochannels has gathered a lot of attention. Its application can be envisioned, for example, in membrane separation technology, where selectivity is achieved through both the size of the nanoscale pore or channel {{cit... | i | acb3cf73dda435223f21e73d4bf2643a |
We conclude the appendix with our robustness when classifying adversarial examples created from other models.
First, we tested the transferability of adversarial examples from model (A) in Table REF , a baseline model that was trained in standard training only on clean images, to our model. We attacked each of the five... | m | cd7b7629c7db997443fac950786dd004 |
In order to state our result, we first recall the local existence of
strong solutions to the ideal incompressible MHD equations
(REF )–() in the domain {{formula:2e34634e-9266-4346-a0ff-f0dc7a6f33b0}} . The proof can be
found in {{cite:d7a38e0a3cc4e0465b95241d1a56683a76ff4ac4}}, {{cite:814da7a17750f85be76d9b9f7694a6985... | r | c530eb8f3a67e891846c5e6322682bb6 |
In 2018, Li et al.{{cite:e019b3170e570395ac92d85a827da6ef879c55ba}} proposed a technique called PhotoWCT, which further improves the baseline established in {{cite:b29ce6ad89bf634108659d336d2ea17e18d01fb2}}. This method changes the upsampling step to the unpooling operation in the decoder, to smooth the final result. T... | m | fa6ae53933d8f3332bad8af3ca9deae8 |
where {{formula:a4bcb0e2-7e97-4a73-add3-5a88fb58f2d9}} denotes the scaled (by the energy constraint) adjoint veclocity projected onto the hypersurface tangential to the energy hypersphere at {{formula:70d8a65c-5641-4bae-97df-7d6c496f114b}} , as described in detail in {{cite:8f735ef15d944dbc32a33aafb95179e0a1415683}}. ... | m | db1b8fb1355e256be402d0adeb0d0d16 |
Figures REF and REF show the models that best fit the observed polarization for each source, alongside the maps from Figure REF . Comparison with our simple model shows that the polarization in DG Tau and Haro 6-13 is broadly consistent with that expected from grain alignment to the radiation anisotropy; the polariza... | d | 8ecdc89e121f4e8c8a66cca5542f4d30 |
Quantitative properties, a.k.a. quantitative languages, were defined in {{cite:345721eb4a44eedf5fa56021264e6b5ba063f21f}}.
Although such properties have been studied much
in the context of probabilistic model checking {{cite:9bd8db4f052ab5480abf7500b6a4217774441804}},
decision problems in verification {{cite:345721eb4a... | i | b6a3b343a08290d0ac01b7ec73fbe864 |
In the past few years, convolutional neural networks (CNNs) have been widely used for LF image SR and achieved promising performance {{cite:a02e7e760528be77345194df50773e15b472e86e}}, {{cite:92c8e910a5ca4353148f5896757348f9b4eb6521}}, {{cite:875f2e39f1298b5c73441d1f570537126ff20cc3}}, {{cite:3b36f99fce2c1dad67ce86def59... | i | 789277eb3f8e56d84565dbcc25308462 |
Relation to BN.
The proposed LogN is formally close to BN {{cite:ad2abb112fede11a3183e027a8a4213d10f32a00}}, i.e. they both use statistics for normalization.
This ensures the versatility of LogN,
since by post-hoc enforcing BN on logit, it can be conveniently adapted to any detectors and distributions in a plug-and-pla... | d | 5305f95c211b61c5cb210d75d94577f3 |
Most existing works use human-written verbalizers {{cite:d07507ac7423b711455cf19461ba13d9a3c77284}}, {{cite:6c8dd30bd140d84b8e81de3e84a732f22d222d14}}, in which the designers manually think up a single word to indicate each class.
However, the human-written verbalizers usually determine the predictions based on limited... | i | 65070ac287f9307d571cf00a44370887 |
As was stressed in {{cite:2b77e12daa1a12780750d727d7bfeac7eaf8e530}}, however, the time complexity of EDA is dominated by
the computation of {{formula:e616b40b-9b5d-413a-b167-15a2303aff9f}} and {{formula:d3f51095-1664-4c6b-96cc-cfa7bf4ed571}} , as well as the evaluation of the large matrix exponential eigenproblem. Mo... | m | 0855c456c230cd00c3808bcffe4e84ac |
(3) Although the theory of general relativity is a field theory of gravity, the definitions of gravitational fields are not based on continuum mechanics {{cite:dc0685bc1e53f49c526e7fd942170f90a4594c42}}, {{cite:c03a041e73bf3ee325952cb5a79cc4bf79cca231}}, {{cite:3d33c1d4a93c2480767bc1ad25f3656fdb167c24}}, {{cite:c649b45... | d | 34d75445aec8444a1a6952960bc79371 |
Theorem 1.3 (cf Lemma 1 in {{cite:8473d860c5f3b2133ca287769bda0a98a5a3fc90}} and around equation (17) in {{cite:1c76a6788ce157b618bae7b534b078d65d60cf5b}})
For any dataset {{formula:1500b23a-a5fb-4bc2-8bf9-f7d1557395b1}} we have
{{formula:8ab4f96e-aabb-4f80-b934-a5249791f0f4}}
| r | 54f17bdeca76848159d28a2cd5f3199f |
endows {{formula:d8293565-831a-478a-a71c-8c12513e616b}} with a Banach space structure. Additionally, (see {{cite:441cd5707ea9b7a787ecbc657ee59188c1f5336c}}) this norm generates a topology in {{formula:a7db3c6e-8f76-438d-9963-6ad16809f6c1}} that is independent of the choice of Riemannian metric {{formula:625fdba7-e3ff... | d | df60f01356039fb14cb96dd930562f1d |
One common prerequisite of these experiments is the cooling of the oscillator to minimize thermal fluctuations and its coupling to environmental degrees of freedom.
This can be achieved either passively (as e.g. in Refs. {{cite:347022317d15036f558bab14a51d7075b0638c1f}}, {{cite:c4a4409af3a4761d83f59f9265498d2f2f223469}... | i | 812d466d825120911481cf61d115d82e |
Figure REF compares the performance of five popular classification networks based on the methodology outlined by the current state of the art {{cite:72ac87195d20d6040c789b229f0b8fb4f16fa901}}. Results demonstrate that for a mixed occlusion dataset, ResNet101 and ResNet34 {{cite:430a91346e1603e039e3cdfdc9d4c6923cf01041... | r | 6c92de47e0e54001820f7903ec679bfe |
One of the major reasons why previous force-directed algorithms, such as in {{cite:f8f856a13fbeb5bc76b363c116cab4db1905f17d}}, {{cite:54ee8b22ef1cf2930ba3814679870405cd70534d}}, {{cite:1da99657f2e57c1f30b2f295b4bae023f8339a28}},
have become popular is how simple and intuitive the concept is. The idea of a physical syst... | d | 710be166b3810a8a86a3f3f8638c66b8 |
To infer potential informal roles, we need a latent representation of each node (i.e., each player) to capture its structural identity in the dynamic evolving network. We introduce three representative dynamic network embedding methods: 1) Diachronic Node Embedding (DNE) {{cite:90e9fe2015c2d72bc39a995921090ed03d20c624}... | m | a72b7f3e9a98d51a84d5cee419feea50 |
For scenarios with single-antenna users, Fig. REF shows the sum rates of ZF & MMSE in {{cite:701eaf13aae8619ef5ecfda7ac5c0dd5a86e8712}}, MF in {{cite:36c4947a5984ec96ec7fb0be71b58e1096697617}} and their widely-linear counterparts under IQI. The performance of both WL-ZF(WL-MMSE, WL-MF) and ZF(MMSE, MF) degrade when IQ... | r | d0fb99ede8710a583dcb6910a28c4025 |
Results of PASCAL VOC07+12. We also construct the experiments of object detection fine-tuned on PASCAL VOC07+12 using Faster RCNN with R50-C4 {{cite:dbd9c5bdafe8f15cd4c1c92a5e28e5203da19234}} in Table REF , fully following the setting of {{cite:92c1a9d8dfdb65516d43362456998ddb7b64ba36}}. It can be observed that UniVIP ... | r | fd0a2f6fbe22d3872da312cd2139a33f |
Clustering is another approach used in our study for data sampling. For this purpose, first, some samples are selected from the unlabeled dataset pool by the least confidence approach. If {{formula:0e8a6ad1-8bc0-4f80-9198-3f54f5de95a6}} instances are to be selected for labeling, we initially choose {{formula:06025fe6-... | m | ea79988f550dec58042d11807229830d |
However, most of the above works only focus on capturing the temporal inter-frame dependencies (motion information), the spatial intra-frame features (appearance information) are rarely discussed. Wang et al. {{cite:9e141712ff154bbdb64c3dfdacfac8dfe8c36b09}} proved that the spatial features and temporal dependencies ar... | m | 8c8f542f68b1dd14a9dafc1ce132e206 |
Most researchers agree that these events involve a massive circumstellar matter (CSM). However, there is no consensus on some basic ingredients of 2009ip-like events. One issue in disagreement is the powering of the main events. One view (e.g., {{cite:4e60d8f8e07406611a17f5090c5e34672ffb6b8b}} for SN 2009ip) is that, a... | i | f3e158e67729ea1e5d02eb82e556e564 |
In the case of filaments with isotropic or near-isotropic cross-sections i.e. rods, the force-extension relationship has been found to be {{cite:47f64c0414c17cbab79a1fcf4a641853c379476d}}
{{formula:5cc0eeb2-b3a8-4272-9259-6a68d87e1b62}}
| d | 09d0f78c9322330eecd893d13860c4f4 |
This truncated system is clearly not closed, since in the last equation the evolution of {{formula:c38b28ac-674f-4f31-a999-31f2c3a1ff72}} depends on {{formula:90874c6b-00c1-427d-8871-bbd83f764abc}} .
There are various ways to close the system. For example, the classical {{formula:42e162ac-ca53-4afe-b5d7-e5710f13a585}}... | m | a74dc9d4a70805734e2902dbcc13ae30 |
Reinforcement learning (RL) combined with deep learning has achieved outstanding success in various areas such as video games {{cite:0097810c37306704be82988ccebcf4933e03735b}} and robotics {{cite:a57f7dc1131cf95de0edf0d0899dc2dec849d1e5}}. These advances have inspired the research community to examine the performance o... | i | 40ccdec18869fb3c6a839fb68d069f5e |
We start by showing, in Fig. REF , some examples of the political parties networks generated by the network-based approach, for each year. To help in the analysis, we have also applied the fastgreedy algorithm {{cite:490cc7d6b383adacc0c7e3c04f65eba047a08304}} to detect communities in the networks. The node representing... | r | a0e9298391a454821c4058fcf7ba4d26 |
Leibniz algebras first appeared in the paper of A. Blokh {{cite:82a70db327bcecb7096d1cb3d7601d2bb85bc16f}}, while the term “Leibniz algebra” appeared in the book of J.-L. Loday {{cite:3718d0629f327bbce79929bf41646e887a1bee86}}, and the article of J.-L. Loday {{cite:bf202f945b55a53bc4b7beb7ce64335c4b27b720}}. In {{cite:... | i | c90acf86e9272f099b93596886a2e9d8 |
One first type of viscous fingering controlling protocol has been proposed in Refs. {{cite:47e60f9d559b6ef24cbf6e8ebf1ee21820ff4e71}}, {{cite:b53f7b21b06f016c37a4d902b1362a5e4c753598}}, {{cite:6973f9e28c85421173239b8f7227d2bcc8ed000d}}, {{cite:a19f0e0457a93a9928d0b4c042508e3f1e94086d}}, {{cite:04cbe5df27883fe8758fceca0... | i | f437f0e691faac40741cefde4df13980 |
Following {{cite:aec855d237a5e4ff07ddec03c8eb7c117a8eccca}}, we use a batch size of 32 and 3-epochs of fine-tuning for each dataset in GLUE. For each task, we report the best accuracy on the development dataset with learning rates 2e-5, 3e-5 and 4e-5. Table REF shows the results of GLUE datasets for RoBERTa, RoBERT-AB... | m | f63cb3a1aff47e08dc47bca21903c783 |
The difference between old and young clusters herein is large: old
clusters (like the globular clusters in the Milky Way) formed before
there was a Galactic disk, and remain relatively free of its
influence. Young clusters however form in the galactic disk, and the
tides experienced by these clusters are dominated by e... | d | 5617f26f9f4cf447b595ee65f8dfe809 |
The breaking of large amplitude electron plasma waves/oscillations has been receiving a great deal of attention since 1959 due to it's basic nature and practical importance {{cite:1796e804368f9ef436e6055d955c0069b4dd8bd7}}, {{cite:fb0a43555a2b0f9382c9f26769aabfa858deaeda}}, {{cite:ae2e440b14c8745f7b88762093f6beeb9bb8e1... | i | 08ec75e710a72381dc08760e414d71ee |
As is known to all, the study of exact solutions for integrable equations which are used to describe complex physical phenomena in the real world have been paid more and more attention in plasma physics, optical fiber, fluid dynamics and others fields {{cite:2e00ee7a955920a7893e7e7b3577f0b18838583d}}, {{cite:742e288632... | i | f1af0129c1ed78fd5bb775558930bf99 |
Since the {{formula:8fa8d5f4-0ccf-46bf-abb9-95636ac80334}} -norm is (arguably) the most natural way to measure the sparsity of a vector,
the above idea suggests that
the {{formula:d255aaa9-0055-4ad7-ac85-6dddbfdbf36f}} -penalization method is a “fundamentally correct" (but computationally intractable) method for variab... | m | 887eb5931dde1b72b2e78b030a26ffd2 |
Other methods: Our aim is to show the predictive power of the semantic features constructed from the transcripts as well as to measure the accuracy of the proposed StockGNN method compared to baselines. To achieve that we propose several baselines which have two phases. First, we generate unsupervised low-dimensional e... | m | ef57bf22d37e3b789606d95dbd8c6615 |
First, we consider special cases that can be solved in polynomial time, motivated by
similar studies for problems on uncolored graphs {{cite:ea095e7f4b480fd1a45cc4f07ffb912ffd3b2d87}}. We are in particular
interested in whether or not we can exploit structural properties of input graphs that can
be expressed in terms o... | r | 70fd1f371f62af829e5ee33d2e4c458c |
With the advent of powerful hardware solutions (TrueNorth {{cite:f5e3fc10de7dd42ef70a419a69fb82faa35f3c65}}, Loihi {{cite:b6c3cd66c793f3c1e8cd3989ac571bd37c93b698}}) for spiking neurons, the question arises how deep learning can be implemented with spikes. The current approach is to implement real-valued activation val... | d | 8a37f92cac613a9de2668640f6d41aac |
In the present paper, we focus on a specific class of Markov processes dubbed local density-dependent Markov population processes, which preserves the density-dependent assumption of Kurtz, but allows an underlying network structure that dictates the environments observed by each individual. We incorporate interactions... | i | 052bd974b3e488ad16461ff7f28ab05f |
Finally, the results in Figure REF again verify the usefulness of unsupervised post-processing also in cross-lingual settings. We observe improved performance with both m-bert and xlm-100 when mean centering (+mc) is applied, and further gains can be achieved by using abtt on the mean-centered vector spaces. A similar... | r | 1977ea413c424d8cbaa24604d4a7c54e |
with {{formula:beb31f48-3fb5-4321-a4c0-25be21c9c59e}} the electron gyromagnetic ratio, {{formula:a0b3e828-9cee-4cc5-8921-10dcff5dc3d5}} the vacuum permeability, {{formula:e73eaec0-eec1-4b07-9125-b4235cf0d0f6}} and {{formula:dae84a89-b0a0-450b-8e0d-bebce66668a8}} the YIG saturation magnetization and thickness, respe... | r | 2117db3470b2b54797ed3baaed702f99 |
Evaluation of the state-of-the-art neural network pruning models {{cite:a74f8031d10c5138dda3730de9e77b68fabc619f}} namely; Global Magnitude Pruning, Layerwise Magnitude Pruning, Global Gradient Magnitude Pruning, Layerwise Gradient Magnitude Pruning along with Random Pruning using ResNet-50 as base architecture fine-t... | i | 5907d66d103258a0bc9613439344bb2d |
The input SAR images are resized to {{formula:3740c898-9e7e-4bcb-8125-7201237d1a2a}} in both the training and the inference stage, and the output feature {{formula:b109f6f4-90be-4cca-b4a8-065002d1c440}} is with the resolution of {{formula:69251a9f-30d9-4390-bd06-b1cf2c0c9045}} . In the training stage, we use the Imag... | r | bd2e3f0e146a6f1437e16227e0f8dbc8 |
In Fig. REF , we compare the median-likelihood estimates of dust physical parameters derived using the optically-thin assumption with those derived using the general opacity model. The parameters are well-correlated, however, we find strong systematic offsets in the derived dust temperatures: the general opacity scenar... | r | 6077a5c36ba38853caef140ebc3e6d3c |
Let us take some cases where the dimension {{formula:0059c66d-0356-42ff-b8c9-5b61a77df375}} is not prime.
Consider {{formula:e028e26a-b490-43c0-818d-112a7db59d64}} -GBS sets with {{formula:835361bb-ae69-4dec-b3ed-5f855af2019e}} . In the case of {{formula:df7ee166-65c4-4efb-adca-6d5fe41a84ee}} ,
there are two types of ... | d | 265e238b7f91b5b2d663f9bc71e1cbe5 |
However, both FD and MMD are not suitable metrics for KITTI.
As shown in Figure REF , real-world LiDAR scans usually contain clutters which should be removed in the recovered point cloud.
MSN {{cite:1c78ca6c14c73c0ba31c5452a176dd1d537ab9f0}} incorporates the minimum density sampling (MDS) to preserve the structure of t... | r | 4de69a7cc1eab63ead21c699cd3e34cd |
In this section, we provide the results obtained using various approaches described in Section and REF in the form of latency-quality curves. We use Average Lagging (AL) {{cite:adb81f40a0ed00ce06ee54ae6bdf9835ac2c01e6}} as our latency metric and case-sensitive detokenized BLEU {{cite:96636af3e0df4f26b3428b5cf11dc9379... | r | e54c039eda11fd22c98a8707d6ca33bf |
Results on a larger architecture. We compare the test accuracies of RCAD and ME-ADA (most competitive baseline from ResNet-18 experiments) when trained with the larger backbone Wide ResNet 28-10 {{cite:c85f2a47d3fe9504c5b9e2c748b9190215cfd408}} (WRN) on CIFAR-100 and its derivatives. We plot these test accuracies relat... | r | 6817423a776bd04e0f62bf06c1c5f280 |
Remark 1.2 There are some studies about the isolated collision for the general {{formula:9203319f-53e2-4a82-8504-b450aaffc2cd}} -body problem(see {{cite:5a5c7a7e2031389d5a6a879bec8fa2238844fcda}}, {{cite:8b5d3f3245918312f83a50a48c542998afb70567}}, {{cite:c33c58e20a88b5f7a3180160f119dd42d9ab52cb}}). However, all the res... | i | b108dc1afdd06b5ee9fc1961039fc2a3 |
Pre-trained language models have become a cornerstone of natural language processing, thanks to the fact that they can dramatically improve data efficiency on tasks of interest – i.e., using a pre-trained language model for initialization often produces better results with less labeled data.
A historically common appro... | i | 8e48b17cc129c3697fcce3e4ac175016 |
In this appendix we explain an effective general method to obtain high-precision numerical results for the coefficients of high-order growth terms involving {{formula:639d13a1-fefb-445e-a8df-663714ddaa15}} and powers of {{formula:8c85140b-d0f1-47f2-ad49-1129cfb79968}} . For applications to {{formula:e73380ea-5596-4af1... | m | e534326109cce0be504f2137a9497b0f |
In theorem:curlupperbound,theorem:curllowerbound, the size of the representation {{formula:fa75eb1d-8a7e-42cc-9b1e-6c1428dedd04}} is assumed to be bounded.
This assumption is reasonable from the experimental perspective since it is common to normalize representation to employ the cosine similarity as the similarity me... | r | c3829e51078cae37732d4a19f14e3ea5 |
However, convolutions are not the only approach to increasing the receptive field of image processing networks, and models like MLP Mixer models {{cite:166307f0490e6f3cc82ae94e89aa4268617e2da1}} as well as vision transformers {{cite:f6237da983559647e4b00ce9ece12b3915088cf9}} are exciting topics for future study in this... | d | e4ec989f60416fcc62ddd90887ba053f |
To further validate the efficacy of image deduplication on neural nets, we aim to evaluate a wider range of image deduplication algorithms, including SIFT {{cite:3828848b55e0cf4bbaf198ad4ff3f9fdd538a58c}}, SURF {{cite:46980f09ac2e9dee1261a902da14f176f86904d1}}, ORB {{cite:e6b6fd17429ac86775bc68f7e66451b64b50ac4e}}, fea... | d | 0d9430b413d1f436b43595c9b506b220 |
Therefore, to tackle the above challenges, we propose a generic framework for boosting current SOTA traffic speed prediction methodsCurrent SOTA methods in traffic speed prediction are graph-based models that use GNNs to directly learn over road network topology, such as TGCN {{cite:8742cb306e309cf3095453a8fbf13beac770... | i | 0e0bf68ad60cda5621b89c64b64a8d52 |
One of the questions of interest in the theory of persistent homology is the following: given a random function on some topological space {{formula:3b9c0da6-6378-4138-ae4a-8932d439c89e}} , what can we say about the barcode {{formula:83df7c8e-403f-4cc3-adcc-7435a63472ca}} of this process? The study of the topology of (... | i | 225c3fcaeb1acca5fad58e6b88103ee3 |
As in Section REF , we investigate how the weights scale with depth and whether Scaling regime REF or Scaling regime REF holds true for convolutional layers. To that end, we follow the steps of {{cite:b54abb98592a3b2d3fe7f5fd90b4686c2c285308}} to get the singular values, and therefore the spectral norms, of the linea... | r | 809a75079cf687f41ddd45466b020bdd |
Remark 11 (Computable Parameters)
We note that both {{formula:dee6cbc3-fff1-4828-9ee0-78745d083fa6}} and {{formula:3efa6b86-6374-4458-8866-93c5c6a77e06}} are quite easy to compute. This is valuable because it means that, given a sampling pattern {{formula:2b8c5cdd-1ffd-4007-b053-37867ad79997}} and a desired weight ... | r | 94003799aaa94139a02e541bb1c60e4e |
These shortcomings have shifted the interest to benchmarks of advantage that are thought to be more robust against noise and that are known to be verifiable with a feasible number of samples. Prominent examples are the heavy output generation problem (XHOG) {{cite:93a02b7d60d15d82e06d6c0a3c9c91304c38a91c}}, {{cite:b2cd... | i | 917930db46ade123f9581c2fefb2d6d6 |
There are various approaches to derive QNMsThe detailed discussion on this aspect is referred to {{cite:e243bf018694a7abc4c28709301ea71a5e4bc559}} and the references therein.. Different approaches produce the different precision {{cite:57c692c65cfcac80c83e95cbf0e110a302227c79}}, {{cite:6de47eceb2c6167b8a9d2246ccf7c9902... | i | c17db43afa935d583c4d81db0a6529fa |
Both CNNs and bi-directional LSTMs with fine-tuning achieve accuracy higher than 90%. If we disable fine-tuning, classification accuracy is still high, although overall fine-tuning appears to consistently outperform non fine-tuning configurations, which is also consistent with the results presented in {{cite:edb482a248... | d | 629720ed04efd0c8c3e0a5ce0e7c5f5a |
Baseline: Max-softmax {{cite:bb1957e954842dd8e0de574391827ee66d1b6a06}} showed that the maximum of the softmax outputs, or confidence, can be used to detect OOD inputs. We use it as the score of an input being in-distribution (ID). We will refer to this method as Baseline. It is well known that the confidence can be ... | m | 98306576cf5b04705bf1a1113b2052b5 |
where {{formula:b2c1cdb8-ac39-473f-9835-f31529ab7143}} is the coherent neutron scattering length for atom {{formula:1701b7cd-0021-4b67-b6ba-dac478b86cac}} , {{formula:766d9fde-aa90-472b-9fd7-054574d4486a}} is the wave vector transfer, {{formula:51efc493-f0e7-49d1-a6d4-4a8786673bdd}} the equilibrium position of atom ... | m | a19bb4ff3d00d0c3718101fb9a2839c7 |
Bolukbasi's method {{cite:bdd7e0663b0495c14f14e1efff09539d7d1ad24a}} requires sets of pairs that define the gender direction. For this we use their predefined pairs, since we target grammatical gender bias, which we have demonstrated to be similar to social gender bias. In addition, a predefined set of inherently-neutr... | m | 3678193b2555e09c48742b55e596a435 |
for some {{formula:3778ee0e-305b-4005-a5c2-037e5d1e9819}} independent of {{formula:30485662-10a2-4972-ab58-5818de9755b6}} . The asymptotic behavior of {{formula:390513e6-a4bb-4024-9162-9bc045985026}} in the energy space {{formula:2b351ea5-7d1c-4421-9fce-f248c474d65d}} has been known since Struwe's seminal work {{cit... | r | acaf3aed89d1a6c2c34cbbb17b52e0a7 |
The presented approach clusters the data into segments and models each of them
with an individual, locally-stationary model. Such a decomposition of a complex
dynamical behavior may not be optimal since it may not always be clear how to
differentiate between a nonlinear and non-stationary time series without
knowing th... | d | 26d34d07af7ad4e0524073a7193995b5 |
{{cite:6ff2a61fbcfecbcce34f4f44215e38f85f27bd9c}} makes the case that researchers building language models should be purposeful in curating training datasets, as curation choices are effectively world design choices. Corpora built on top of news scrapes snapshotted at a specific point in time will capture all of the in... | d | 16388f991765b63321100ff949fefce3 |
Active regions exhibit sufficiently slower rotation as compared to ephemeral regions. Moreover, there exist a weak tendency for larger active regions to rotate slower. This finding is in agreement with previous conclusions made in other studies {{cite:f2fe13c654c3e21b356ac6a5660cd0fa6148b137}}, {{cite:e3c7065cfddc4ebd... | d | e81cb7fb605fd6bca0444fd5718ac5a9 |
In our experiments, we extend the GPT2 architecture to formulate our model, named GPT2E and train it on the CoNLL-2012 dataset {{cite:22de77c99480646ea452405845aa28f8139cb7b4}} using the annotated coreference information. We evaluate the model's performance in terms of Perplexity on the ConLL 2012 and the LAMBADA {{cit... | i | 7afcd9d6548dd22a745479184e81423a |
Besides click feedback, a few methods also consider other kinds of feedback to construct training tasks.
For example, CPRS {{cite:f4c15d34a0dc0bb55d34614b825926e586b331e1}} trains the recommendation model collaboratively in the click prediction task and an additional reading satisfaction prediction task, which aims to ... | m | fe5aad59bc6c3755a614ec4aa1cf84f1 |
Remark 2 The weighting matrix in WLS provides the relative importance of the components of an error vector to be minimized {{cite:7824013d598e9a1b711a89cec837ff6a4a3d3db7}}.
In the proposed method, the derived weighting matrices ignore the second- and higher-order error terms, which are non-negligible when the noise is... | d | bae4267222e45a58582ea6775fb0fe54 |
Observe that, for Formulas {{formula:b777327a-0fe8-44df-8d4a-3a388d21216c}} and {{formula:79da97aa-b636-43ac-a6f2-e521063056ef}} to be satisfied, a trace has
to be finite and so that in its last instant the value of the
{{formula:8a8f7e14-cf6d-4e90-b707-430f58d85d35}} -counter is {{formula:992a5abf-313b-4f4b-b7e2-33... | r | e188b92d7715b6fa7d0dad5374b0f70e |
An obvious challenge in the use of loss functions which include {{formula:4112ea05-3db1-4cf2-8b9f-c74dec4ab9bc}} is that the number of samples required for density approximation grows geometrically in the dimension of {{formula:8b377e4d-3a3d-4a68-95e5-0f837a738b8c}} .
In this work we have focused on the case where {{f... | d | 4b83a438cce7e08c3f40c052724063a2 |
On the other hand, we found that the benefit of the axial summarizer mechanism is dependent on the complexity of the prediction tasks and the size of the data available for training. When randomly initialized, we observed that it is important for the complex sequence-to-sequence prediction in the MIMIC tasks but it was... | d | c39d377b3530307960cae3c87b008231 |
For many meta-heuristic methods, there exists little to no theoretical justification or convergence proofs {{cite:4d37a384031a43ea7ae200da0a2ee38ff7ddea2e}}. Others may converge with probability arbitrarily close to 1, but might only do so after infinitely many function evaluations. In practice, many meta-heuristic alg... | m | 3a34274aeb315e3a7fd97694e604aae6 |
The crucial step is the determination of the weights {{formula:da4a0c56-9c3b-46f1-a14c-d3ca2db405d5}} and {{formula:3febec3d-aa11-45bf-8047-4d2f5fa6f5e6}} of the atomic neural networks, for which we need a reference data set containing the atomic spin values of a representative set of atomic structures. This data set... | m | 9ba94cde91b9d3e04e71e7f917a3a8f0 |
One way would be to employ time-consuming techniques requiring to both manually
collect and check (digital) evidence; for instance, one could look up sources like
encyclopedias, newspapers and even gain further evidence by asking
friends. Another way is to devise automatic fact-checking systemshttps://fullfact.org/blog... | i | 54a511e334ce8e4289d5f4a047e95d7d |
Automotive security research has been traditionally focused on in-vehicle vulnerabilities or adversaries exploiting the lack of secure communication {{cite:bceedf9c6f4334027f55d969117cd2feea560a5c}}, {{cite:daa5652b1ce9a3bec169f1479b19360fe249f212}}, {{cite:8887df1a614ac602af5fd1400bf0ce8aa1ca2248}}. Machine learning h... | d | 84e1c26ed151227ce85359313bcb8899 |
In this section, we evaluate the performance of the proposed widely-linear precoding schemes through simulations. We compare the proposed widely-linear precoding schemes with their linear counterparts, e.g., MF {{cite:36c4947a5984ec96ec7fb0be71b58e1096697617}} & MMSE {{cite:701eaf13aae8619ef5ecfda7ac5c0dd5a86e8712}}, R... | r | 8be197071a59265b00b0411bb71fbd84 |
EF in PLAX view is estimated based on the distance between inferolateral and anteroseptal landmarks, i.e. LVID. We use the length error (LE) of LVID as well as the location deviation error (LDE) of inferolateral/anteroseptal landmarks (abbreviated as IL/AL) as key errors. LDE is also the most widely used criterion for ... | r | 0c8b4d08e4462caea2accc1d3004edca |
We compare the results against traditional variational autoencoders and generative adversarial networks. The results show reconstruction of the inputs with feature and generator. We also show random samples from the generator network. We evaluate the Fréchet Inception Distance (FID) {{cite:03eff438dd65dcf1a36a2cf33e769... | r | 1f98317aa37259c42b60c12f7cb6373b |
Compute {{formula:86b7a6c0-8a5e-46eb-97d7-d30e52dab2c0}} , the optimal shape fitting {{formula:23616381-0396-4ea4-b91e-b582f81f4396}} . (It suffices to use
an approximately optimal shape.) Compute {{formula:d0b99551-49d5-4b5e-9e46-1ecf86750911}} , the projection of {{formula:f1453faa-554d-4f0b-8591-5403e40b4d99}} ont... | r | ababf1cf171f4c0a6c4b27b943d64072 |
Particle yields in 0-10% central Pb–Pb collisions at {{formula:3799def5-09a1-4ba9-bdbf-09ec31789aaa}} = 5.02 {{formula:0af6e64d-6e6d-44d2-872a-184ce1386c06}} are
compared to predictions from three Statistical Hadronization Models (SHMs), each based on a
grand-canonical
ensemble, and are shown in the left panel of Fig... | r | a622f73e3aede6ecdcb35558e259aaf0 |
We then add the Planck tSZ angular power spectrum {{cite:40d0e9fe8777832f4b415f7c31ac43a2d1a7251c}} in the likelihood to add contraints coming from large scale ({{formula:8e6b87a3-bb24-4fdd-90dc-1a69b147d0ca}} ). We only consider the case using the RF modelling of the tSZ spectrum. Adding Planck tSZ data does not impro... | r | df206a86ad3b6113d6455b3e801df868 |
It is not clear how much bigger dimension one needs in order to achieve a coarse embedding of a graph of growth {{formula:66b4b5af-d90e-479a-870a-06a39f068167}} in {{formula:746ac979-bf06-41c3-ba12-2c13a98c3357}} instead of an
embedding considered by Linial, London, Rabinovich {{cite:681ddd5d774d4c463e40b71ae55302540... | d | 4470daf312c37ab9fda1d92965f319ca |
We perform both frequentist and Bayesian analyses of our data. The measurement of {{formula:b1aaf22d-4bc8-4803-8b67-78c89f121d78}} from each of the analyses is in agreement, with the presented confidence intervals coming from a frequentist analysis and the Bayes factors and credible intervals coming from a Bayesian an... | m | f332bbcb7a76d1158dc0b9a763cfe25d |
In this paper, we present a new mechanism, differing from the self-tuning or well-tempered classes, to solve the Cosmological Constant Problem through a simple, minimal scalar field model. Our model belongs to the Kinetic Gravity Braiding (KGB) {{cite:a81de749a5a0f3762f931d12d4fe9f8cd1673b90}}, {{cite:ae89520bed04b405d... | i | 127fc326aa831486ffa04a1c8a79ab9d |
Although there are at present no direct constraints on the escape fraction from faint galaxies during the epoch of reionization, deep searches for escaping Lyman continuum radiation at lower redshifts do show some evidence for redshift evolution {{cite:8d451e2f1b1a898184b919b277524e4e80ab6d18}}, {{cite:1bffb188b8269874... | d | b8dc99f383144ba5425021e706b50e3d |
It is a classical result that if each ball selects one bin independently and uniformly at random, then the maximum load is {{formula:c5ad55ab-60b3-4497-93f1-8fac5c1e8881}} In general, with high probability refers to probability of at least {{formula:f4ffda3c-39dc-4fda-a754-2c6766db8b6f}} for some constant {{formula:e... | i | 12f3356743e1b5368725ee4ab0cb5bdf |
Unlike two schemes in Section , we develop an accelerated variant of the Douglas-Rachford splitting method by utilizing the Halpern-type idea in {{cite:ac6b1fec51fa1dfac7da1704df777a65eeb66575}} and the Lyapunov analysis in {{cite:77f4bbf25c451b42e7da23fd36b9a83caa960240}}, {{cite:28c64930f17e6f82b88b90e31b836674be0e20... | m | 3bd73d8cb10d0a1b2e0319bbda81dc43 |
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