text stringlengths 54 548k | label stringclasses 4
values | id_ stringlengths 32 32 |
|---|---|---|
Quantitative results.
Tab. REF shows that our proposed method outperforms other baselines by a great margin in LOL test-set. It achieves the highest PSNR on the MIT-Adobe-FiveK dataset, but slightly lower than MBLLEN {{cite:bee447fde11d9e7acf473c80ad9f2cdd707448fa}} in terms of SSIM and LPIPS.
In Tab. REF we report t... | r | 4fdb310e66ba3165459aeef09c952d7b |
Our results demonstrate that self-attention is competitive accuracy-wise when training on ImageNet from scratch.
Figure REF shows that pure self-attentionBy pure attention we mean models that use self-attention in all layers except the stem, which is convolutional. based HaloNets are currently slower to train than the... | d | 3e545937daecc141a82dce9a93cb6e15 |
where {{formula:01e79064-d586-4eeb-9353-2faaca228403}} are the observed data and {{formula:e5ba2716-6f11-4c5a-a4d9-c02148e5c469}} is the {{formula:214077da-f135-4a49-99d3-983416ef93de}} -dimensional deterministic signal with structural changes at certain points. The signals that we treat in the current manuscript are... | i | 13b5507ad55d455e4cf6a54bfa7b7588 |
We have shown how recent dispersive results for exclusive-mode
contributions to {{formula:231b6bfa-16d7-4bdd-87c8-fcccee5e0de9}} can be used to provide a
determination of the corresponding isospin-limit, light-quark-connected
contribution, {{formula:4e9390df-f547-467d-99a6-3db0dfb7376a}} , the precision of which turns... | d | d22f2b2ba315083d9ba4fc83c532ea57 |
The most common approach for associating neural network components with linguistic properties is to predict such properties from activations of the neural network.
Typically, in this approach a neural network model is trained on some task (say, MT) and its weights are frozen. Then, the trained model is used for generat... | m | 08844bde6e05359997233f5eebb0d0ca |
In this work we address two long-standing challenges that arise in the context of neural networks, and dynamical models of biological systems more broadly: i) how to implement a memory for continuously varying quantities without using special symmetries of fine-tuning parameters; and ii) how to generate long timescales... | d | 669d40a256175c460213614244de1546 |
In this section, we briefly describe the numerical methods we employed to solve the differential equations in this work following {{cite:877afa9ff8fb6fec7eb67a03977d4edfe31b0647}}, {{cite:f80920c138c6bb3248efcb8a948b1ccd4a69de7d}}, {{cite:5595c0dd0c3a94fbbf6fdd394e36b794d8a96777}} (for a detailed introduction see {{cit... | m | 58591df5ddd316d8ce081b28729c566f |
To solve the above limitations, we propose the HD-VILA-100M dataset (i.e., High-resolution and Diversified VIdeo and LAnguage) that consists of a wide range of video categories and will benefit a plenty of VL tasks, such as text-to-video retrieval {{cite:9bee4caa7230ed62a529694c9d592a50922a3e5f}} and video QA {{cite:cf... | i | e7f6571598f6ad0f07c14c159d11e110 |
An interesting application of the perturbation bounds given in Section is community detection in bipartite graphs. Community detection in networks has attracted much recent attention. The focus of the current community detection literature has been mainly on unipartite graph (i.e., there are only one type of nodes). H... | d | b1d2df892024eb80deda7bd2fb39f9f6 |
On datasets featuring a single foreground object, we use the 2-slot version of IODINE and Slot-attention. Since ReDO, IODINE, and Slot-Attention do not distinguish foreground and background in output regions, we choose the best-matching scores from the permutation of foreground and background masks as in {{cite:4b77917... | r | 11987e78dec2a02582bf22580553bea4 |
We compared our method with the existing unsupervised LDCT denoising networks {{cite:45364383697e6f397f6e97bf3c94d5e5658b2afe}}, {{cite:7c92c5df074ce819fd94dd601f7cff9553525597}}.
For AAPM dataset, we compared our network performance with the conventional CycleGAN{{cite:45364383697e6f397f6e97bf3c94d5e5658b2afe}} whose ... | m | 2684581e8fa768429bfb9bf5fd7b4e85 |
One of the possibilities to modify GR by considering a massive graviton also attracts the attention of various people. For instance, a ghost-free theory with massive gravitons is developed in Ref. {{cite:fe1bb3d1e6af2b51cd55470422eedcbe0bc6386e}}. In curved spacetime, this causes to the presence of ghost instabilities ... | i | 387f81dea1591f19056bccac95b55426 |
With simulation in hand, we aim to train a SBI method to estimate the posteriors of the SN parameters given the observations (and redshift) and compare this SBI method to traditional Bayesian inference. In our baseline comparison, we will use the affine invariant MCMC ensemble sampler implementation in the Python modul... | m | c68b9e36b44f7b27ac8ab2876f9bfa49 |
In our experiments, all networks are trained on the Extended Cohn-Kanade dataset (CK+, Figure REF a) {{cite:e06414a3d12f349f10f17d3de566ab0726bb49f5}}. CK+ is composed of sequences of images, starting with a neutral face, and ending with one of seven universal facial expressions {{cite:231ff43bda8c6ea1bfdefd04a8102673f... | r | db39637cedbdbee7c5f645c05c11f3a7 |
The experiment in Section REF clearly shows the benefit of initializing the network parameters by (REF ) and (), respectively. As seen from Fig. REF , the optimization process is able to offer around 8-9 dB improvement in performance over the SToRM initialization. We note that the proposed framework of recovering the ... | d | 3fbd1433d88bfd2be70b687247f6fa9a |
Our proposed booster method can be extended to prepare excited states.
Using existing near-term methods for estimating low-lying excited states {{cite:675f332c14f96655211b69965be61f6676308694}}, {{cite:7047fe5d931d628921d50128e313fc8db6030be6}}, {{cite:38b22b06ee5c7f3984a95ebe421bcf290bd778ff}}, {{cite:3c16c50e1557da57... | d | b8f1cd18f5322a8d909a4b06530c4d10 |
It should be noted that in more complex, an-isotropic, in-homogeneous systems (e.g., channel flows or ocean circulations), spectral analysis using other basis functions, such as Chebyshev or wavelets {{cite:fa45dc0752dfa6a3beb62f99deca8110c132f30e}}, {{cite:a8d12956fc0d84e4cd50ef30cdbd72f635289f66}}, might be needed.
| d | 8a4f27bf7a1682a0f4f2562782ac7c10 |
To compare the efficiency our proposed IGSAM with different sampling methods, we have experimented their GPU memory usage and processing time on a single GTX 1080 GPU with 8 GB memory. The sampling methods include Random Sampling (RS), Reinforcement Learning based Sampling (RLS) {{cite:8c3697c7585c195d89abc76bfe7198547... | m | 7069b275322f6dec642c4d4e14bd01d3 |
Using the experimental settings of sec:more-setup, we extend tab:cls and tab:adv of sec:class by comparing MultiMix and its variants with additional mixup methods in tab:more-cls and tab:more-adv. The additional methods are Input mixup {{cite:5157c8c6bebcc6e872c98acd012868b92aead743}}, Cutmix {{cite:416b08b88cfbfcf4151... | r | fb3db8ac87fc189b98447d167b34a117 |
A recent trend in the theoretical understanding of deep learning has focused on the
linearized regime, where the Neural Tangent Kernel (NTK) controls the learning
dynamics {{cite:c2a71195f5b7509be9dbae59780356f347436153}}, {{cite:151c2f630601dfe681108fb35c26647677d92e9a}}.
The NTK describes learning dynamics of all net... | i | 0b33af5d5c588e4aa3109d4a6511da33 |
We summarize the overall algorithm in Algorithm REF . Our method is a special case of the constrained {{formula:f8084e76-cf27-4fc1-9b2f-92374b43bbfb}} -means {{cite:0f1204b98fede75635a8ee5fafdc52844c42fc21}} where channels recovered at each slot formulate couples of cannot-link constraints with each other. Same as the ... | d | f4758bf06037ccd420c9dcf7bf7f049c |
Notably, conditional information such as labels can improve the performance of the generator {{cite:b335718bcc50d2776c944f79c2760d9a1fa36016}}, {{cite:002f91aceb7c0a8d7b79e29f16f6dbf91f857274}}, and the completed data vector can enhance its task prediction result. However, state-of-art imputation methods, e.g. GAIN, do... | m | 1fd4fda101ef0a6b22962ac95d2a371b |
Throughout this paper, particularly in Section REF , REF , and REF , we use a ConvNet named `compact-convnet'. As mentioned earlier, the proposed analysis is structure-agnostic, and we chose this network since it achieves a reasonable performance while being easy to understand and analyse due to its simple structure. T... | d | 817261f4a843de2fff90ab4eda6b3f27 |
Based on this assessment, we propose in this work to keep the best of both worlds: use deep learning to process the image and discard unnecessary details, then use handcrafted methods to detect the line segments. We thus retain the benefits of deep learning, namely, to abstract the image and gain more robustness to ill... | i | a5431e6b76ee6f423fc6176541713411 |
In addition, we compare UGCL with supervised methods (i.e., MLP and Supervised GCN) and self-supervised methods, including Node2vec, DGI, GRACE, and BGRL on ogbn-arxiv. The other GCL methods are not selected as they encounter out-of-memory issue during training. The experiment results are presented in Table REF , where... | r | 9b25a7c03c0ca3e9758d9b020d009bbe |
In this work, we have obtained constraints on {{formula:f59be0c2-86bb-4f0b-85c9-02cd82f284ce}} and {{formula:2dcee4b1-6b88-48b8-aa8c-7eb2dfb20ebd}} from the tomographic analysis of the two-dimensional slices of observed large-scale galaxy distribution. The amplitudes of the two-dimensional genus curves are measured i... | d | 62f8d06fd20b152f82a602813ef08498 |
In what follows, we show that criticality, studied previously in literature, occurs when the norms of these Jacobians either remain finite, or vanish algebraically as {{formula:e29b6855-6325-4713-8211-849ce5061ad9}} becomes large. To prove this we derive the recurrence relation for {{formula:8784aa88-5d9c-4c74-a287-2e... | r | 623252e41a682efd05e7ce82ab2beb1c |
The following formula {{cite:2f2e07e01abdd9a1a960b16448ea40cfe239dbf6}} can be used to compute the
adjacency matrix of the line graph {{formula:75145d8f-6bcc-4bb2-b430-276c253e74f2}} of a graph {{formula:9180c592-4140-4e5c-bd1d-cb3e298eedcf}} ,
{{formula:c171a0f4-0468-4a79-828e-bc3bb4427303}}
| i | 3f8a95a34599b40596f287175df3dc6d |
SLIM for Image Recognition. Fig. REF illustrates the Same-Layer Inception Module (dubbed as SLIM too). Similarly, we first split the input feature map into a number of groups (e.g., 4). For input group, the transformation consists of a standard convolution (with a {{formula:8ab40f62-c996-49f0-902c-7b1fdef76d91}} kern... | m | 51d26ebdd866f6aa3d96b32e008852cf |
We recall the Arrow-Hurwicz (AH) method from {{cite:ac4cebfccbc7b7f1b022891a1017d6a5773afd6b}} for steady Navier-Stokes equations. The method is given in {{cite:d0179a36f6c5c7e13b996d18c6bbb929b8664c74}} with a slight change of parameter variables.
| m | 35328ae08bcc1629ca2dac5a5d821010 |
In this section, we present numerical results to illustrate the performance of the two proposed resource allocation strategies. For clarity, we denote the proposed resource allocation with ESB by PRA1 and the proposed resource allocation with ASB in a PACSR by PRA2. The numerical results are obtained by considering a r... | r | 649510d2fba486c534157950e09044d1 |
Table REF compares the performances of our NRR model to the state-of-the-art results reported by Paetzold and Specia paetzold2017a. We use precision of the simplest candidate (P@1) and Pearson correlation to measure performance. P@1 is equivalent to TRank {{cite:f6542c27ebf95720db45b41281ebb19a4f92202d}}, the official... | r | 8e6cf96a45ebb776d35287b628c483f3 |
Among these methods, GAN-based vocoders {{cite:b8cb4f191d46fd535c153807509ca27557d97417}} can generate high-fidelity raw audio conditioned on mel spectrogram, while synthesizing hundreds of times faster than real-time on a single GPU.
However, existing GAN vocoders are confined to the settings with a moderate number of... | i | 5b6e76fe96fb37263fdda048a558d461 |
Setup. We first invert the videos frame by frame using the Restyle encoder {{cite:dc624ae05e13fe778b6c360eeb1a5a26c9cdea5e}} (psp-based {{cite:dbeaefc883b0b1da730fd41c82c00b931812e0e0}}).
We then directly apply five different out-of-domain editing effects produced by StyleGAN-NADA {{cite:26acbeed3073a8ab1a2ed1fd539c90e... | r | 7c999f13ba47364d411ed03e79f61c53 |
The spin period of rotationally-powered pulsars range between {{formula:708d6c77-e504-4290-9167-b013203f0b46}} ms to {{formula:bf3fed8a-e17e-4356-a008-fb3dc2b90205}} s {{cite:d95bd654468d8e18ba02969787b18411e63eaacf}}See https://www.atnf.csiro.au/research/pulsar/psrcat/..
The spin period of galactic magnetars {{cite:... | i | 0fc9ee1a5a333c1125b0f0cc09452294 |
To improve the accuracy of the macroscopic approach, we could consider even more moments, since these would theoretically approximate the DSMC model to higher accuracy {{cite:4bda8bd039a097c95c72aad6513c34f73a604a44}}, {{cite:387f19685707c20a3adf3aabfe20dbb7c53ee6e0}}, {{cite:82c71e73d43f1e45e2bc90752e5b7033d0448563}}.... | d | 1822d7f16ccc33b516af09d9adbac039 |
OJ 287 is the first blazar for which periodicity in the optical light curve was detected, revealing prominent flares that occur approximately every 12 years {{cite:235949c11bbe9e914220ce8b24f04456922dd374}}.
At that time, the unified scheme of active galactic nuclei {{cite:be767dea801718320b2c32526bb0810a1dd259b6}} had... | d | 1930a66ab1d916ef6aacef1a8d54134f |
KITTI is a real-world dataset with street scenes from a driving car. This dataset provides sparse but accurate dense disparity maps as ground truth. Image size is H = 376 and W = 1240. For KITTI2012{{cite:6990a1653cbaf9f5cc5d710b44eadeba9cc07f09}}, it consists of 200 stereo images with ground-truth disparities for trai... | r | 8e28876c76558fa0e3bdfadeb0352dfa |
In order to assess how different evolutionary algorithms perform on the generation of adversarial examples for deep neural networks in black-box settings, in this work we compare three different evolution strategies (ES), namely: (1+1)-ES {{cite:3fb4b0f7e7c130f7474933161cc0fc09a91a00f7}}, Natural Evolution Strategies {... | i | 48887d2f8efe66d07a105e66374dc020 |
Qualitative results of initially synthetically generated brain tumor images by different models are shown in Figure REF .
Using the best FID and KID of the pre-trained models, the brain MRI images generated by transfer learning are shown in Figure REF . By analyzing our results, we find that FFHQ gives the lowest
FID o... | r | 31f7fdae0775055b61c364b55ba164d7 |
In e-commerce, recommender systems can provide personalized recommendation of products and services by discovering hidden user preference from data. Traditionally, the hidden user preference is discovered by techniques such as collaborative filtering and matrix factorization, which are based on users' past interaction ... | i | 76b40f9bd648ab8bcc96ba7eea70cab8 |
Finally the concept of global normalization has also been visited with deep neural networks
{{cite:5e068cc35f671b8b2c492d82448d70f1d59860cd}}, {{cite:181a1116572ad9720c2e7844e759914d0162b5a3}}, {{cite:11fb0ba9ab78a3bedb76d888167ef1c476e5257b}}, {{cite:bd63ffd06d57c524a58fcded10a5deff891d1fb9}}.
These models can be seen... | d | 7885b8c51feef85df3a0563baaa6439b |
Multitask Learning
Traditionally, the problem of disease normalization is tackled by first identifying the disease names (NER) and then normalizing them (EL). We attempt to learn from both types of supervision by having a NER and an EL model share parts of their architectures. This is known as multitask learning {{cit... | m | 6ea8c9d4b872ab20c48b9ee494e0522b |
The policy is trained using a large-scale dataset collected via a VR-based teleoperation rig (see Figure REF , left) through a combination of direct demonstration
and human-in-the-loop shared autonomy. In the latter,
trained policies are deployed on the robot, and the human operator intervenes to provide corrections wh... | m | 8adfe847d3ad00884a91100a6b93eeb5 |
The main contributions of this work can be summarized as the following. The application of acceleration techniques for fixed point iterations in this particular problem is new and have not been reported in literature. We have adopted a version of the Anderson acceleration {{cite:7d3fd224aa0042d11714dfd0bc2bb3fb037b447c... | i | 43bdc15f9d29741f1ea1812f1ebb85fc |
The whole network was trained on a Nvidia P100 Graphical Processing Unit (GPU) for around 250 epochs for each experimental run. 318 CT scan slices were split into a training set containing 268 images and a test set containg 50 images. The model was built and trained using pytorch-lightning {{cite:73b5df0d5c70c2d29932dd... | r | 1e278f23de28bfd4ca4ccfdf77b16360 |
The 17.7 d period proposed for the binary CSPN of M 3-38 is relatively long compared to the majority of binary CSPNe {{cite:23b65d5be647918c919fc503b68fcc8d9aec836a}}. In contrast, simulations of CE evolution result in relatively large separations of the order of 10–20 R{{formula:6063cd2d-49cd-46c2-84e0-618c40cc0bd2}} ... | d | b81daa799501f5116d32aa320acad2f2 |
Accuracy of overlapping region detection. Overlapping region detection is critical for our method to select the corresponding pixels and points, and the accurate overlapping region detection would increase the registration accuracy.
As visualized in Fig. REF , the overlapping region predicted by our method is the most ... | r | c1dba1a94f5acddd250df7e5cd496fdf |
Since our data is generated in an unsupervised way, we compare against other unsupervised methods in Table REF .
RAFT {{cite:3b0903b76e6c710498e32cda835fba6dae14bd33}} was originally published as a supervised method trained in stages on FlyingChairs {{cite:cd1d1beac3fbc1e5d81f8f38cadf2d612b7a3d60}}, FlyingThings {{cite... | m | 3e49228fe9d4f1c3c629ef8fb6d62b8d |
Random access memory (RAM) is a fundamental computing unit that allows on-demand storing and retrieving data. While a classical RAM addresses one memory cell in the database per operation, a quantum RAM permits querying a superposition of multiple memories {{cite:e54f4a0596dcfddd1ddab85a53ba9e11fd9dc126}}. Given a sup... | i | 68932915b4d5a42c614db06b251a59e0 |
The interpolation mapping in {{cite:b1c523ecd294eecb2eae08cd1291c0005242c447}} first assigns
a point {{formula:6757e87e-3eac-4395-8e6a-172139207b14}} to a Voronoi cell {{formula:89dc0b45-534b-4b03-bb10-4737fa4220a8}} ,
assuming that {{formula:6b81db5b-2b33-43d5-ae38-1a49b30a4b8c}} forms an {{formula:e20c3d69-f208-495... | r | 005d4ed6dcab0c800f040a2e164bd4d1 |
In this work we perform an analysis of the backreaction on this region brought about solely by semiclassical sources, considering that all classical ones are part of the background. Our goal is to check the validity of the standard assumption that evaporation of the trapped region primarily occurs from the outside, and... | i | c18089108157173c5fca00d73850202b |
In light of these negative results, people have tried estimating {{formula:1646752f-8d30-42bb-96ba-ce3e358bde3c}} from the coordinates of {{formula:57602a99-06c9-46a6-8674-daab30543ad4}} .
When the entries of {{formula:b393b71a-11f0-42e7-ac06-979bfb9b9ac9}} are {{formula:0b4c195b-30e4-46ed-b792-874233b9a35f}} standa... | i | ce608676f882225acfc6011aab7d3b62 |
The joint NICER and NuSTAR spectrum is well described by a broken power-law model with photon indices {{formula:d1b7942b-14cf-41bd-ae43-cb2a584c7da1}} = 2.10 {{formula:d616ce35-6426-486b-9501-68685b19c0c2}} 0.02 and {{formula:212eaa35-be68-4bce-91a3-7b3969edbb26}} = 1.60 {{formula:c32c571a-361b-4a9f-bf06-3f194620cf2... | d | f61ed66582db4084ccdfb39fda9ec74f |
{{formula:956290a1-46e0-4c63-a14b-5b4d929fd1ee}} Quantum fluctuations and rate prefactor.
When the semiclassical approximation is valid, one can still worry about the impact of quantum fluctuations over the bounce {{cite:6c976803498699a7ad33b96ff9dc25b97caf3405}}
which give a subleading correction to the rate and amou... | d | 4a08f0d7621fe1d6b3c943d2d118fa3c |
Let us show (REF ). By the Law of Large Numbers, {{formula:7d7e4413-9e91-488d-a6ba-27a4e448fd07}} as {{formula:ad9fb470-5543-4ccb-abd3-8e4709df9cef}} in probability. Apply Slutsky's theorem: {{cite:f1efcffdb40456f1318bbab1caaa433ca129553e}}. From (REF ) and (REF ), we have convergence in law:
{{formula:557641c8-7eff-... | r | 194f33b2129a21245e5bd615fd99468c |
The algorithm's "division of labor" can be summarized as follows. The parametrized quantum part explores the search space. By exploiting quantum properties such as superposition and entanglement, the exploration is typically much faster than by classical approaches.
The classical part is concerned with finding paramete... | r | cffee0215c57d31822dcfcd2e08bcdcf |
In this paper, we visualize the internal spike behavior of
two representative and widely-used training methods: surrogate gradient training {{cite:de4c38d3abdf5642d1db1ad5f4729c75f5873f14}} and ANN-SNN conversion {{cite:dfd60914297e9c8c100de6c0baec178159dce37e}}.
Since ANNs can be trained with well-established optimiza... | m | 86f391b60cc25e9c554f7194fbf41076 |
While there are a number of advanced outlier detection methods, it has been shown recently, that in many cases simple {{formula:15fc6ba2-e41f-4277-9c4a-d519d1fd1b27}} -NN queries do perform as well as the sophisticated approaches {{cite:30bbe446755d750fd50874f19ef57b8bf6fc369d}}. When using {{formula:11066cfc-217f-4ed6... | i | 16ee7e928d5f14f41872e7704722ed6e |
{ mH1, M0} [103,104] GeV, mH2 [1.0, 1.1] M0, mH3 [1.1, 1.2] M0,
where {{formula:8a1df655-d2ec-4444-8933-33a43c2d6dec}} runs over the fundamental region.
Here we choose small {{formula:33040db5-6bc0-4cd1-b160-dc617d2ae0c7}} values that are required to realize Baryon assymmetry indicated by Eq. .
Then, we perfo... | d | 323a2ee2f64f02fa9700fcab5f0c88d5 |
Table REF shows {{formula:74acb699-8c5f-4c1b-8ee4-7e26a216352d}} and {{formula:de6d4ae2-b7e6-4486-900f-81095e94ca24}} values of the models on the synthetic dataset. As seen, our pipeline outperforms {{cite:0c2181219beb1a7f0689a3c59df9ab93cd25e27c}}, and {{cite:71311f097a38cb3a60617e6ce0bff13d23b173d2}} by a {{formul... | r | dc0f83b07913ba548d16be4723e7733a |
The number of dictionary atoms {{formula:88b15de9-90b0-4a59-97e9-2d12f1392531}} is a central parameter of KDS. Because the dictionary is global, rather than local, it does not scale with the dimension of the data only. Indeed, {{formula:a240ddf2-5c18-4370-937a-547984ed0bce}} must be large enough to ensure that any ob... | d | fdbd3de27dfe53a8962cf0b3032986e3 |
The Tamagawa number conjecture of Bloch and Kato {{cite:bf6f13d8a50e0c9848dac6f94468dac52dfc369c}} expresses special values of motivic {{formula:b5c44b40-de3c-40be-90dd-6ccf33956e7e}} -functions in terms of arithmetic invariants. The following theorem is an instance of how one can tackle Bloch-Kato's conjecture via Iwa... | r | 498d02d00ddf673be6f82ec93a4c451f |
Table REF shows the evaluation of Polar on the program from Figure REF and 14 benchmarks which are either
from the literature on probabilistic programming {{cite:20c7843f58b64f910af24aff700098e1a2ea81fd}}, {{cite:90623d71bb900bfbf6e8896571c4aadfd1ad3fdf}}, {{cite:d63e1c6a103ce5617840b2b6aa77853a06d3f859}}, {{cite:411... | r | cdced787e97cb6b664c9ecc28abdb6c0 |
[Proof of lem:lemma3]
Recall that {{formula:d1113aea-0e4f-4a0a-8203-c7c67fd0a40e}} denotes the time-reversed walk of {{formula:e4c46652-66d9-45ba-8cf3-0ebbc3dcb9f6}} starting at 0 at time 1. Denote by {{formula:300f80d7-ab47-4283-b8ad-2b73bbbefc2a}} the family obtained from {{formula:ae4f46e3-45d5-4b7a-a87f-2122d727... | r | 5d236fc1941ebdb1d33e33489816a8c6 |
One must note at this point that though there is a maximum in the
distribution of flux ratios near the value {{formula:53897cac-cb62-41a5-952a-d7a886f63f39}} , the histogram
and correlation of Fig. 3 have a finite width. Thus there are bursts
with {{formula:2c8622cc-7aaf-4abe-859c-b890faa2615a}} values as large as {{f... | d | ecc6e87ad89c5f62f544b90740aec10a |
With the aim of extending the global reach of Natural Language Processing (NLP) technology, much recent research has focused on the development of multilingual models and methods to efficiently transfer knowledge across languages. Among these advances are multilingual word vectors which aim to give word-translation pai... | i | 0f5f5227923ec73957ad757b437c6fbb |
In Table REF , we compare model complexity and symmetric-scale SR performance of our LTEW for both in-scale and out-of-scale to other warping methods: ArbSR {{cite:bf70bd9224ce50c5836cc5ced52ac7adcad1c4e6}} and SRWarp {{cite:57bb2a0ecab8b78273b294d5e652342f8cbe2898}}. Note that ArbSR {{cite:bf70bd9224ce50c5836cc5ced52a... | d | 1c0031f2e141db60f2a4bccc7839babe |
On ESOL, we believe our CubeMol models struggle due to the limited data, the large size of molecules, and the lack of path and structure features used in Path MPNN. Even still, the ability for the CubeMol models to scale to molecules with more than 50 heavy atoms indicates the room for optimization. For example, a tree... | d | 92b0dd9f05ed55bd63a2aa434aef90c3 |
The extent to which the degeneracy leads to diversity in the clusterings, depends of course on the optimization algorithm, as was also shown by {{cite:fc194197fc3e57c14ab55b44641edb87910c662f}}.
To calculate and use consistency in a sensible way, one could argue that a large diversity of clusterings is positive, as lon... | d | ea4e2ce9d26c8846dae8be103d212388 |
Quantum computers are thought to enable calculations that cannot be carried out on classical computers {{cite:b0872dc54037986aa0f5098977e232c06f866036}}, {{cite:4da2c08e1b733392f5ff84c46e63a8cf9608f6cd}}. One challenging problem in many-body physics is to determine the zero-temperature phase diagram of finite systems t... | i | 8ba7c3b20e9c7da9cf116ee471cc03a7 |
To compare the performance of LESS against other regression methods, we have selected 11 well-known approaches. Each method is tuned with the hyperparameter sets given in Table REF during cross validation. Linear Regression (LR) does not require hyperparameter tuning. All these methods except, Local Linear Regression ... | m | 51247faabf511093c0e24a3911b616a5 |
Language is creative, it is situated, and has to do with our
communicative competence:
its users can give new meanings to old
words {{cite:f7a15a0a6b9817952291947052846375066d5329}}, produce utterance
within a particular time and place
{{cite:f629b05273cdd1691fe655f755e039f3eb993086}}, and determine if they
are appropr... | d | 53993a50ebca7c3ae01c1778523de399 |
In this section, we consider the nonsmooth problem (REF ). From Assumption REF and the definition in (REF ), we know {{formula:0afffce1-28a9-4874-a8ae-12a969354b7f}} , and {{formula:5957a095-494a-43ce-880c-1acd1a1ef4d2}} is equivalent to {{formula:57078f7a-45fe-43f0-8c64-2739466a255f}} {{cite:83a2ad4d4bdb9f0d970d0e5... | m | 6995d90364b8c1cca532a381027314fd |
Recommender systems have shown great success in both academia and industries, and so become indispensable in our life by helping us filter millions of possible choices. Recommender systems provide a small set of items from the underlying pool of items based on users’ historical interactions and their side information. ... | i | d6d8c793901473cde07bd38889631e83 |
Datasets.
We evaluate performance on ImageNet {{cite:a1790d21687338bf9cce3d715c0ec3fbdacb7edd}} and CIFAR-100 {{cite:ab61b8d3549583751c593627f127f6606300a1f1}}. ImageNet-Subset contains the first 100 classes in ImageNet in a fixed, random order. We resize ImageNet images to 256{{formula:0e6a307e-576c-4b65-a629-40c4dbb3... | r | a9e62c139e842b002576adf2e5df18c5 |
In Section REF , we detailed our model assumptions and
choices for training accurate and interpretable binarized
models using MIP and PBO. Similar to Rosenfeld et al. {{cite:6fd35953b3d85d87af1daebec43e595353acb8d4}},
we made the assumption on the existence of function {{formula:834ce456-2186-4ba0-80f4-d5a0fd000127}} ... | d | de324ca435b00a36612758e612607638 |
In this paper, we considered models with spatial connectivity patterns where each unit connects to all its neighbors within a radius {{formula:ceda0734-98cd-449c-9f05-a105cace57d8}} . In the future, it would be interesting to extend the current framework to study network models with random spatial connectivity. In this... | d | 207e70ed2ced857e76c4754f53d50645 |
Any physical mechanism, which may be invoked to explain the emission up to TeV energies in PKS 1424+240 and TXS 0506+056 will have to be reconciled, however, with the low {{formula:33fe2ce4-efcc-437c-9f61-12cb25516fd5}} and {{formula:2c7463d2-95cb-4c86-9637-8b39f560919f}} factors observed on parsec scales. Most solut... | d | 639fa02e9d57ad0a8fbd1f5cd779a0db |
Our new representation opens doors to new mathematical and statistical methods to analyze brain connectomes; in particular, taking into account the tree structure of the data. Topological data analysis (TDA) uses notions of shapes and connectivity to find structure in data, and persistent homology is one of the most we... | d | dbafa5f4bbc5d965d819bd1fc39e3f6a |
The jewel in the crown of the achievements of the LHC experiments to date is the Higgs boson discovery in July 2012 {{cite:56a619b73f9b35113b0a3eb892a1c69257fc282f}}, {{cite:e953b3cf8e1705d5e2696e490a41ba8de88ddb99}}. The discovery of a Higgs boson is, however, not just an end of a story - a quest that began with the t... | i | d0e329747213b800537870d784bb29ee |
Our data confirmed that O VI absorbing clouds are
ubiquitous throughout the Alpha and Beta quadrants of the Galaxy. The
O VI volume density {{formula:c9edd8d2-7a59-45f6-91e8-fcd28bf07aec}} falls off exponentially with height above the
Galactic plane, as had been shown from previous studies
{{cite:572723f4b11abc979e337... | r | 9e6613a902cd9e1627530a0acfdeecc3 |
In this article we derived the integrals of hyperbolic and logarithmic functions in terms of the Lerch function. Then we used these integral formula to derive known and new results. We were able to produce a formal derivation for equation (27) Table 27 in Bierens de Haan {{cite:e236f1746b8ecedb73878258fd5d85f74cc56310}... | d | 6b96b2b42853fc286a21453e6a74c04c |
It is common for {{formula:0e7cd02d-16af-4b85-8570-fceacc6747b4}} to be large, {{formula:dae874d9-b842-413b-bb31-fba121c6b219}} . Optimization in such a high-dimensional design space is difficult, especially when {{formula:205644c9-a577-4319-a144-9a2736f67be0}} is the output of a high fidelity numerical simulator
tha... | i | e9de6a907b34947e7e9574af6612388e |
Consider the following setup. At each {{formula:6c7c229f-1d00-4316-bbd1-b858c97ab1c9}} , a forecaster {{formula:038388cb-019c-419d-b183-78aec3d02e27}}
outputs a density forecast {{formula:ea44bfbb-04b6-46ea-b28a-f2f36eec085c}} given observations {{formula:a5a7caf2-6382-4e50-a166-606c9b0561d4}} for a continuously
dis... | m | f7e97c7833599f80a780d6a77f72ff24 |
We summarize our key contributions as follows. 1) We propose to harness the complementary features from both modalities in forming enriched feature embeddings, that are consistent with semantics of identity, thereby allowing improved identity recognition. 2) We propose to impose orthogonality constraints on the fused e... | i | 3654873bce56da9079c8de06b73ba1a8 |
Tables REF -REF show the ImageNet test performance with 8-bit floating-point quantization of the activations, weights and gradients for different EfficientNet models {{cite:ecd34da972a4a02678a7cd7d1e4b8d3cfd9f24fe}}. Following {{cite:ecd34da972a4a02678a7cd7d1e4b8d3cfd9f24fe}}, we train on ImageNet for 350 epochs with ... | r | 1c817b4910a3d70614098cb0e956090d |
We investigated the footprints of the planet in RVs by searching for periodic signals. To do so we used the Data and Analysis Center for Exoplanets {{cite:d0ad72ee60902a714a62f05262b47a0d0d1ec37b}} web platformAvailable at https://dace.unige.ch
and computed periodograms for the RVs (Fig: REF ).
| r | cb03251c37e90138ec84fd0f6e5555d0 |
Machine learning (ML) mechanisms were typically designed without considering security risks, as recognized by a plethora of studies {{cite:cebd266a25a60a256847dcd05a85b6669f20b862}}. Attacks on learners were introduced, many of which corrupt training data. The latter, referred to as data poisoning attacks {{cite:ccb726... | i | 116b7c8b723a7f00a82d3a1f8254ae2e |
The condition number of the cross-correlation matrix has been pointed out in several studies as a key quantity in determining performance {{cite:7f6ee32db1d6946ae91a4a9734a4c3c8c52d49f1}}, {{cite:3d3faa0a7209821048524af967594f8858163b5c}}.
Our work analytically quantifies those empirical observations in the framework o... | d | 17cce215648ba67b439590be7dc6b7d0 |
We see in Fig.REF that the binary separation is
correlated with the mass of the parent gas structure.
It seems that no correlation exists in the case of the
fragment mass, as can be see in Fig.REF .
This missing correlation is expected on physical grounds, in fact, for the Taurus
dark cloud, {{cite:18232d3d748882166f3... | d | 017f8f94cb4b861dd8afb3e7aaeaa368 |
The starting point of our training pipeline follows the proposed methodology in {{cite:dc1a11df373a009e8ef9022accc36a91c0d389b9}}, difference being we utilize batch-norm (bn) during ANN training and subsequently the bn parameters are fused with the layerwise weights as done in {{cite:bc7a84c04a2051d5e82260bf9dd8346c950... | m | fed0accda1840b013c294ff10d12f56b |
Since our data is collected with the old policy and value estimation (off-policy), we keep introducing more bias as we train the network off-policy using batch data (i.e. {{formula:ddfca3bf-a49e-48fd-a5bb-c968b05af591}} ). This is because when the difference between the old and new policy ({{formula:c216cacc-41bd-4610-... | m | 94a7fcf3965e2d260af408cff779bbd0 |
Obviously, given the significance of the above result it is highly convenient to compare it with previous analyses of the XCDM reported by the Planck and BOSS collaborations. The Planck 2015 value for the EoS parameter of the XCDM reads {{formula:d3876d48-04b9-4acb-abc9-a532b35b015d}} {{cite:1e0a05971d4affd43d8a0aa851... | d | 40d81e9fd148b89ee6fed1b187112d8e |
However, in most publications the research focus so far is mainly on a weak coupling as pointed out in {{cite:1e2a228a546a3af817933bb2ad4c16116068f0bd}}.
Works considering strong coupling especially in the 3D case are rare. In this direction, for several reasons, port-Hamiltonian systems provide a good framework to sol... | d | 490c81b6a4cbac0f367d4f95ea724635 |
Conv-KNRM uses 21 kernels, one exact match and the rest soft match {{cite:f9b6048fc5b85bbf3bd1898155053359d17d0ffe}}; the uni-gram, bi-gram, and tri-gram of query and document texts are considered, the same as the previous work {{cite:ed006a971c184519a4dbfd1eaeaed3ffbdcec5a1}}, {{cite:b5ae2981ed15142a2a46ab5387e6f090b2... | m | 443aca44316160c3366cf094c8a8ded4 |
In all these settings two key questions are speed of convergence and error of computed versus true values. For the latter, the algorithm converges to the exact average {{cite:4d1077cffb46677f6499fe7fe5c8cf7f776a191f}} in a rather general setting {{cite:e8cb79721102821319b1e153f3b673caad241454}}, assuming no packet loss... | i | 35e4203062bb03c9f9f9e60836cbd4ae |
Many classical algorithms {{cite:dbf03dd7f5970e141668ff9664e879b26eee348d}}, {{cite:bab0f9263de017420a82e7465df9816398f0b610}}, {{cite:4c3c0397d7f3a06b14484072c223bd9a1975a5ca}}, {{cite:d4ba4a445d0e699f010555fdbbf63ebac8737d63}}, {{cite:ac6f190d94604bdfea0154da7f63b4463bd7b31e}} heuristically search similar patches for... | i | d9821d77d43b9672f4e02da1029b28d3 |
Dynamical dark energy models in which the EoS of it varies with time have been proposed as alternatives to the cosmological constant. A wide variety of such dynamical dark energy models are abundant in the literature, a few of them are quintessence, k-essence, phantom, chaplygin gas, tachyon models, holographic DE mode... | i | 6280e353bcac2d3ec79ae94e3337c286 |
Setting aside the large influence from the local dynamics, we propose that the topological features which play major roles in shaping the interaction between two nodes are: ({{formula:4eee07c1-9e89-417d-98bd-378b0e1bb2e0}} ) the strength of the links, ({{formula:097b5085-ead6-4365-8cbf-8e80979d915a}} ) the length of th... | d | 6eaeff171c6674c40f6b387f8816b95c |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.