text stringlengths 54 548k | label stringclasses 4
values | id_ stringlengths 32 32 |
|---|---|---|
In this work, we present the marginalizable density model approximator (MDMA), a novel deep network architecture preserving most of the expressive power of neural models for density estimation, while providing closed form expressions for the probabilities, marginals and conditionals of any subset of the variables.
In a... | i | efe8c8967aeaf6bff7cc1234fa358d21 |
Another important tool for modelling and verification is stochasticity.
Probability is often essential to effectively quantify uncertain aspects of systems,
from the presence of hardware failures to the unreliability of physical sensors.
Stochastic games {{cite:b2e899728827be5d9c02c19f7d60d4ae6317832e}}, {{cite:1324fa6... | i | 9027fa78cee6f986a49eb86e4069bef7 |
The superaccretor SS 433 belongs to a typical class of ULXs. The apparent X-ray luminosity is about {{formula:04976f25-1310-4153-9bac-66d6fd229ab6}} erg s{{formula:65d28a6c-9ac4-4c0b-9557-4242726062a4}} but the intrinsic luminosity is considered to be probably
{{formula:4524872f-1e1a-4ce6-8250-426183bb3b42}} erg {{f... | d | 9e3729b8f41d8bdac4381d66f32b35c8 |
In a previous article {{cite:329e6057f086b4f336565d4585f0a9c180567428}} by the second and third authors, we proved the long-time existence of Yamabe flow (REF ) on asymptotically flat manifolds. Moreover, we showed that the flow converges in a global weighted sense (defined by {{cite:ee5bd2ae1895f3f0b82ce59d9693985047b... | i | f05750ede9f5ef6f6a34223e2086e4b0 |
Our method also benefits from the learners’ diversity. Co-training will fall into self-training without diversity, and consistency training on the same prediction will also be meaningless for the lack of diversity. The diversity inherently comes from the randomness in the strong augmentation function (the unlabeled exa... | i | c3b8118c6a27c7aac1819d64cf3b35b4 |
It might seem possible to generalise the techniques presented here to study chimeras on rings
of nonlocally coupled general oscillators {{cite:87bbd826e5b8e2d3e76ec21cd7c7779cf15ea921}}, {{cite:7a63919b32b726c8888d76d1b41bb2f51b743041}}. However, while the locked
oscillators would be described by ODEs and the asynchron... | d | 1278b795450da3979dd9b5159c527115 |
This conjecture was proved in {{cite:981602c5e785eb20215266185a180dcf2e6b9949}} for {{formula:a3d829f6-f787-40e4-841f-18fafeaffbad}} , but for {{formula:c7fabaa2-7e8b-478d-ad4f-ed0176ed2db1}} , it is not even
known if these functions are minimizers of {{formula:907d66b4-d5e8-443d-8dd0-1a3d478600c6}} . As in {{cite:2f6d... | i | 36fdcf39e55a82aecb1bcb7dfe2dda9f |
nce is a powerful tool to predict mi and has been used in recent works like cpc {{cite:d8da07574bb9232a3d2f61f1c8d03fe7bf0641d7}} that rely on the nce objective to distinguish pairs of context vectors from the same or different time segments. This approach is similar to Time Contrastive Learning tcl {{cite:d3529c552ed7... | m | 1410503e60213e89cbe2590e9d4d08e2 |
{{cite:9728075e15452043d81f35a21cb8f7685a0d8237}}, {{cite:835500c48b6b790599eb9018d5c3e6553744a0ff}}, {{cite:dcb2657793cc0d22aa359723391ba6d72ed7baac}}
Assume that the minimizer of the extended Parisi functional for {{formula:a0adccb9-6f62-40d1-9c00-3ce08ac90f82}} exists. For all {{formula:3af02db5-3bc5-4f45-aa59-268... | r | fb2fe7f5b8d4640bf5e1d7851914c4b6 |
Batch Normalization for FedNST:
As reported in other works {{cite:2e6375e09e3a2e4bca9c7b430205ffc362846056}}, {{cite:2e7c5ead5a3614abdb0fa375ac56a4439ee75a3a}}, {{cite:5f28cc16374d217078dbfa88a88c5abaa701277c}}, we find that using standard Batch Normalization (BN) for FL gives rise to convergence issues due to data het... | m | 736e3c5a98eb99272f28b12fcd3d43a6 |
where {{formula:4bbadc1c-370b-4e31-887b-45d1bf40b3fc}} denotes the timestep at which {{formula:4da86414-5949-4dfc-a297-1d3f01a7840e}} is visited while
following a policy {{formula:5f800504-e999-4307-909d-93d0a3473695}} , assuming that {{formula:c50b7605-81f0-4d2b-bb42-b3f0b8942aa2}} is a recurrent state under all po... | m | 739cec936031e6b0fa0d53e21d9752d1 |
Then, we compare DCEN with recent generative methods.
Notably, the generative methods utilize prior unseen domain semantics to synthesize extra unseen visual data for training, e.g., powerful GANs, while DCEN only uses the seen domain data.
From Table REF , DCEN outperforms most generative methods by a large margin, wh... | m | 98b98c43573434520ae667bf92946b5a |
For transforms that are trained end-to-end,
there is evidence that the Jacobian of {{formula:cea56d07-5427-4bf3-a5d0-286c14428ad5}} ,
when viewed as a {{formula:ab69de20-14bc-4393-a832-21aecf383a91}} -by-{{formula:4f73fa91-8301-4533-b591-fca280d74e32}} matrix, has orthonormal
rows with high probability {{cite:5eee1c04... | i | 989548c4bd3771c11b4ae200533c5792 |
The E12 isochrone (high rotation) fittings to HRD/CMD derive turn-off age, 85{{formula:7a521c46-c77e-427f-948e-007d017fa0e0}} 13 Myr of Be 55, by taking care five RSGs/RBGs. For this age, the masses of five RSGs/RBGs from the E12 isochrone are about 6 {{formula:6a9c1171-4f34-4587-8204-056bc29923e7}} (Col. 6 of Table 5... | d | 1819ebb8f2823617f0872c39e9c695fa |
In astrophysics, gas and dust mixtures have been predominantly studied with grid-based codes. The gas phase is computed as usual whereas the dust is treated by using superparticles (e.g. {{cite:24835cffe7dc3fd7f4e65a2812b5fa689584ec1b}}, {{cite:8495a8e8a1bd08be1963c244b582f222eba4d2bd}}). Computing the drag is usually ... | d | 9d18e47ea350230ad903f3d8d20409e6 |
To further illustrate the effectiveness of personalized conversational response generation, we report random selected cases in Table REF . The SEQ2SEQ represent the standard sequence-to-sequence model, Persona represent the persona-based Speaker Model proposed in {{cite:464c0c7e9bf72d9339bc261d98bc53dd47df1356}}, and F... | m | 16b20ea157a93bd4dc4754724d5d8920 |
Step 2: We prove that the system of SDEs (REF ) has a unique strong solution before its first collision time by approximating the singular drift with regular functions. For the existence and uniqueness of SDE, we refer to {{cite:8c66509ae0b2d0b9cbd373fedc81be7f9bf6c617}}.
| r | 64239f0fb70a12e118363a690840a5f1 |
Colored dots and a star in Figure REF and Figure REF illustrate the CIFAR-10 models proposed in {{cite:ec5af6d1da79ecce4c204f9e94fbe989630e29b0}} and {{cite:2c8aa9cf9d9a216614e1b81d3d922b6680823a4b}}, where the accuracy and robustness values are taken from corresponding publications. We see in Figure REF that for th... | r | 44fe98569afd40e668d4da58905528db |
The risk measure CVaR was first studied in the context of portfolio optimisation problems by {{cite:577e576991961a9f4653d213d3b8ee3168b92824}}, {{cite:078f25ee61d9a31c1c4d287d71fe939dee054d34}}. They showed that a mean-CVaR optimisation problem can be transformed into a linear programming problem that improves the effi... | r | 0c8d49658f5e59ace598841085f63207 |
Coronal mass ejections (CMEs; {{cite:443f76aa134ba4b06be7c1a56fdcab9049729c01}}, {{cite:443f76aa134ba4b06be7c1a56fdcab9049729c01}}) are enormous expulsions of plasma and magnetic flux from the Sun into the heliosphere. The basic structure of the magnetic field of a CME as it erupts is that of a large-scale magnetic flu... | i | 4f852f6c16500bcc0d804c6d2fcc55f1 |
BERT {{cite:858f4834f0652de2048ed046c8806ea572f52133}}, a Transformer-based model, has now become one of the most popular and outperforming models for hate speech detection tasks, or in general, text classification tasks, and it is proved to be the most effective performing model in a similar subtask {{cite:8658836cfdd... | m | 0891ef4a7f9ca45a3f1fee14fa8e7b64 |
Theorem 9.3 (Perron-Frobenius){{cite:8c843bacfb5b52d34218a329dc39c291da3f4df2}}
Let {{formula:6d261685-e0b3-495c-9158-e8d57fd63a3b}} be a non-negative, irreducible matrix. Then, {{formula:3fb2e22c-f806-4e56-93ab-4fe956f79c4b}} is a strictly positive real number, and the corresponding eigenvector {{formula:55db4e5f-38... | r | c25bc801425c8274844a429dd4f34b8c |
Multi-modal synthesis.
Following SPADE {{cite:8da3e9e3c490c74e3b694d6c5a3690d9081693ac}}, we train an additional encoder for multi-modal synthesis or style-guided image with the KL Divergence loss in the way of VAE {{cite:6d6faaaacd74e85c11bf806c65e2329268f3c606}}. By controlling the mean and variance vector to sample ... | r | 4c7772c66bab679661749bdc09408ae0 |
Interest in this direction has been fanned by a series of anomalous experimental measurements, especially since the mid-1990's, which suggested the existence of new, light neutrino states.
At the LSND experiment {{cite:4a9903e4d49e3abbcd70ddfe86be272d0b06b709}}, an unexpected 3.8{{formula:4686253d-8998-40fc-9ab4-59e0c2... | i | 9df2c224a61079bd06f64c8f78e4147f |
Having approximate analytic tools at hand is beneficial not only for theoretical studies but also for inflationary model building and PBH phenomenology. Using various shapes for the power spectra, we analyse the potential of future GW experiments for probing the GW background induced by the large scalar perturbations i... | i | e2a2e2bb99dade79edde1eee94e085d9 |
Besides SNe Ia data, compilation of measurements
of differential ages of the galaxies in GDDS, SPICES and VDSS surveys
gives measured values of Hubble parameter at 15 different redshift values
{{cite:eb29a9b2e0c958f6dadbf2c4714faf5a193ed569}}, {{cite:8de816390125a6acd88e3e2b9f9d3c279cda45fa}}, {{cite:43ddbadfcd32587e42... | m | a1bb00ad37491b7e6de3729d2ca36446 |
Additionally, enabling stable multi-agent training without centralized training could open up future opportunities for legible {{cite:36e0ef66f825a7a9188c9cdd9fc87d7e42fa4cfb}} agents in human environments. Agents with interpretable actions can induce more faithful human mental models, improving human-AI interaction; h... | d | a2ce9f445d3c82db4a573adae165a563 |
For PAMAP2, we adopt a CNN for training and predicting. The network is composed of two convolutional layers, two pooling layers, two batch normalization layers, and two fully connected layers. For three MedMNIST datasets, we all adopt LeNet5 {{cite:5d14b5bcda8f11ee3938c6601f084d6f29cb5207}}. For COVID-19, we adopt Alex... | m | 2a47315851c0c63b62357742020f4945 |
Centralized Training for Decentralized Execution (CTDE), where agents are trained offline using centralized information but execute in a decentralized manner online, has seen widespread adoption in multi-agent reinforcement learning (MARL) {{cite:78c9367b194421087c044af5867616f3630c0842}}, {{cite:908dca84a20e60347502af... | i | c77f1ff883fa69141c4a92da3c83c594 |
In this section, we evaluate the performance of different variants of PRIMA on eight tasks from the family tree and graph benchmarks {{cite:b98d0b8c826ce97987445b6125aaa9c8f1f42835}}, including 1-Outdegree, AdjacentToRed, HasFather, HasSister, 4-Connectivity, IsGrandparent, IsUncle, IsMGUncle.
These tasks are widely us... | r | 55211c97065ef501ed85fa8388931c49 |
We have performed the Riccati-type pseudo-potential approach to deformations of the AKNS model in sec. , such that the modified NLS is obtained through a certain reduction. In this framework it has been constructed infinite towers of quasi-conservation laws and discussed their properties and relationships with the MNLS... | d | a1cc4e437060295591a2f1737aed15ff |
The study of representations of a polynomial {{formula:cf1ed540-4cfc-4ca3-8988-53e0413b165b}} non-negative on a basic semi-algebraic set {{formula:b1b7ff98-a355-4773-bad7-9bf52353afa5}} (also called Nichtnegativstellensätz) is a central interest in real algebraic geometry with influential applications in polynomial o... | i | 0f0e3dca2f9ba3372bf693252c5af899 |
where {{formula:35638605-b675-4e8d-9049-329baaf2d431}} is the permutation group of size {{formula:4bdd1cf2-a0ef-40e2-8bee-113f236c0141}} . Surprisingly, although these two matrix functionals have the same number of terms and are defined in a very similar manner they behave very differently and, on particular, have str... | i | 6bb34e521e59274e1115de83371690f4 |
the lifting {{formula:8cdc3ed5-29cb-4667-91bf-081535e030cc}} can be approximated by {{formula:0d4f0212-e2d4-467d-8a27-4a43f70f96e6}} sinusoids whose {{formula:fba31c99-dac2-4665-a0b6-a26ce7653592}} -dimensional frequencies {{formula:669799ab-a9dc-4e9f-9bd8-39a8db92c728}} are sampled from a Gaussian distribution in t... | m | 7328a218d490e79043eba138042e295b |
As an alternative to MCMC, others have embraced variational inference (VI) in
which the intractable DGP posterior (REF ) is approximated with a
simpler family of distributions, which are often also Gaussian
{{cite:7609996f264fedddc2e7b5e0bb51970aae6bcfc9}}. Inspired by deep neural networks, {{cite:1188828f739f7ebed1649... | m | 2662b299d1719c74f29e96f418c076df |
Since it is relatively narrow and is seen as clear signals in both {{formula:77f7e9f3-5779-42e7-b13b-9110121bccd1}} -meson decays and {{formula:79d891b8-ae6d-4310-85cc-f5d5160b4da0}}
fusion reactions, the {{formula:b13f0735-d952-4423-8eca-c0b188902b6b}} is one of the most intriguing of the {{formula:4dfc6117-6a5d-455... | d | bb41cc1fbfd3644dc09f0d90ff8e3078 |
Irreversibly also increases the complexity of decoding the diary in the radiation. In section , using the conjecture of {{cite:c9f1a5147aadf3e570af6687e0acdcdec998c61c}}, which relates the complexity {{formula:3eeadff1-117f-4903-9fd0-0c0e57fff64d}} to the size of the python's lunch, we found {{formula:17e5d99b-df59-41... | d | 1d40694a9df6d5a78e5171684cc8584d |
Here, we briefly describe the general trends of Model S. We discuss the results as functions of various parameters in the following sections.
First, we find that the surface brightness profiles become more extended as the total H1 column density ({{formula:59329afb-be17-427b-82bf-71eb5fed0970}} ) increases (Figure REF ... | r | 9029de6b1667da224f07d07c76b83b1b |
We conclude by remarking that the next step in our line of work concerns electron-ion ({{formula:c785e65a-70df-43d1-b888-cadfc9919bd3}} ) simulations. Studying the case of large mass ratio, and especially the energy partition between particle species, is fundamental for the correct interpretation of data from current a... | d | b62ccc20f989c2091fe14d9ca2b0b9b8 |
A natural but impractical idea to tackle the discontinuity problem is to let each client inherit its own client optimizer states from previous rounds. However, these previous states can be stale and inaccurate because they are evaluated at point {{formula:acfe6c48-e20b-4a06-b8c3-7a515adb8d8f}} and do not take account ... | m | 5fcb80f8d6343de5f6c3dab45d3298b8 |
Kernels have been found to be fruitful in a variety of domains, and the presented results on kernels on non-standard spaces are likely to be of interest to extend the applicability of kernel methods.
In particular, we enrich the collection of kernels on Hilbert and Banach spaces with classes enjoying proven integrally ... | d | db2892ddfffa85c1859d42fe5ce5648d |
After showing the details of these traditional adaptive gradient methods and Adam-type methods, we introduce their convergence properties as well as their further variants. {{cite:414766654c630d67c8c6efe5380d6cdbadceed79}} shows that Adam does not converge in some settings where large gradient information is rarely enc... | m | b9d49209d7e5a30a0bc707198e8d4c4c |
In this work, we consider a left-right symmetric framework with the simplest Higgs sector consisting of only two Higgs doublets, a left-handed and a right-handed under the two {{formula:acb1bf4a-90e2-42af-9508-8a68dbcf4a9e}} group factors. This leads to only two neutral physical Higgs states, one of them being the SM ... | i | f20cc68cb895ae2540c6693fccd4f088 |
A popular method for computing the DRT involves the use of the
Fast Fourier Transform (FFT).
The basic approach is to sample the 2-D FFT
along different radial lines through the origin
and then use the 1-D inverse FFT along each line to estimate the DRT.
This direct approach suffers from many artifacts that have been d... | i | e9ff47efc01d9e6b7b5e1a4c5c917d1e |
Therefore, in this paper, we evaluate the effect of using different learned feature descriptors in the pose estimation of a hybrid monocular VSLAM pipeline, shown in Figure REF . We evaluate two learned descriptors on the same VSLAM traditional back-end, similar to the proposed in the well-known ORB-SLAM {{cite:707d709... | i | 7acb9ba48e9adb1a2a18f5f5b85ffb66 |
FigureREF illustrates the framework of our proposed CLIP4Caption++ for video captioning. First, we use the pre-trained model of CLIP and SlowFast to extract visual features from origin video (REF ) (higher part in Fig. REF ). Second, we sampled the extracted frame features using TSN {{cite:f1dda6f63f781d505a6744ad27b9... | m | 763361dfd953974d5c6b878e96c5b7ba |
The second method is to use GAN {{cite:4a0ae18a313f560612dc78b24de1781b77d918f9}} to generate training images.
By using conditional GAN, we can generate images with specified labels,
Once we train conditional GAN on an existing small dataset,
we can generate any number of realistic images.
This method has been used as ... | i | 4e7f79991de0bcae7907d62febb5e3fc |
WebVision. WebVision {{cite:26d947258db06b89923afd30275d6156413a2b0c}} contains 2.4 million images crawled from Google and Flickr using 1,000 labels shared with the ImageNet dataset. Its training set is both heteroskedastic label noise and class imbalanced (more detailed statistics can be found in {{cite:26d947258db06b... | r | b35eccea615dfadf7e74bdf31baf7881 |
where {{formula:e7166527-7b90-4338-898b-af8e61fe53a6}} and {{formula:b486aa3d-4907-4687-aca1-21d4fcae0e70}} are iteratively increased, in order to find solutions that minimize {{formula:9aefc680-af31-4076-adcd-7452d4660ed2}} (see {{cite:6df3d1820c5479f97f7f0df2ed890506dbca3321}} for the full details).
Asymptotically... | m | 8d019345c7b1c579535ccd548a6bc331 |
In bulk, it is prestablished that a short flexible chain exhibits weak compression under direct field{{cite:80910312d3f32dad18fce7dd5503fd9b085d22b1}}, {{cite:d5148b7413b5a30b129a91864bcd53cec307c415}}. Figure REF elucidates the structural response of a confined semiflexible polymer ({{formula:1fb08f3b-9918-45d3-962c-... | r | 6c3762a912a63ea4a986ba88d9283206 |
In Fig. REF , we show the results of this comparison for both datasets. For VOC07+12, in Fig. REF a, we observe that starting from the first two active learning cycle, our method has a relative improvement over the random baseline by {{formula:ce788592-2254-4a60-b2c4-1b3927833c75}} , over the best overall active learni... | m | a1cd5a05a8c111e0de8d16e23734475b |
The performance of the proposed framework is compared with nine state-of-the-art learning-based and classic deformable registration algorithms including SimpleElastix (Elastix) {{cite:d6dfe020884284cb5f049a64d1564841d65b3b32}}, Moving Mesh (MM) {{cite:5de5d6cd2fc29f53428da165bc5d21c7eac46ac6}}, Real-Time Image-based Tr... | r | 3372ae663e860ba8639fd6a2a996db8c |
In this section, we describe the details of the proposed network and the motivation behind the design ideas while connecting these with literature and hardware limitations. As mentioned before, we designed our proposed architecture by hand and adopted an efficient building block for SISR problem inspired from {{cite:4e... | m | 7c678801b7cf4cef5ca93d31666406b5 |
For the LM to understand language in the context of suicidal thought patterns it needs to be fine-tuned on such a data. For this we word2vec representations on corpus of suicide related subreddits as as well as fine-tune LMs during training on the same corpus. Thus we obtain embeddings of the text contextualized to sui... | r | c782bd50d3060487b812c1f06a978d56 |
In this work, we introduced Smoothed-AND (SAND)-masking technique that improves the performance of the current state-of-the-art OOD methods over a variety of datasets. In fact, SAND-mask aims at addressing the failure modes that we identified for a recent major contributions in the field of OOD generalization, i.e., Re... | d | 66250a5785bb142b7debecf944309160 |
We show {{formula:fc3759e5-31ea-4dd6-b84f-b02149a459e5}} versus {{formula:0637b5f0-6e49-44cb-bde2-5e8989f42630}} in Figs. REF , REF ,
and REF for different parameters of the system. We can
see that {{formula:a34b34f5-ac3e-4b76-8c6b-4e4370361f0a}} reaches its maximal values in a plateau which spans
from {{formula:98... | r | 4078dc3e5cde0ca9fd63c7b227b6ca7d |
Our treatment of the AGN IR emission has also glossed over much of
what it constitutes an altogether distinct subfield of AGN study. It
has been suggested that clouds are also involved in this component
of the AGN spectra, however with different properties and at radii
larger than those of the UV and optical line emitt... | d | 99bd944652a8e7afc7054fd34d815849 |
To best address, the above question, a method based on Fourier optics (FO) and vector spherical harmonics (VSH) is used to computationally explore longitudinal coupling in structures resembling cornea. The VSH is the vector solution of the wave equation in spherical coordinates. In 1908, Gustave Mie was one of the pion... | i | dc7f6ace8d5081a7857e347a22dbdb7a |
Their values are not completely restricted by the theoretical constraint in {{cite:a8035f5b0e602f2d9b2647352bf85962f745706d}}, but merely focus on the actual FLOPs budgets in each scaling stage. This means the searching process of ours is more fair.
| d | 287934cdfc0a075eaf3df721558ccafa |
Motion deblur. Corresponding to Case 3 in Fig. REF , we compare our DMR results with state-of-the-art, Event-based Double Integral (EDI) {{cite:a29a13d1752551b2e02a991356703f2358f08abf}}, shown in Fig. REF . Compared to EDI, our results preserves sharp edges while alleviating event noise.
{{figure:85059c12-7127-4189-... | r | e43ef4fd3ad1f54490d3a737fd0e113f |
The ground and first excited states of {{formula:391b0efb-caa7-4675-8a25-9480d28f4022}} have been computed in a recent paper by a variational method using the ground and first excited states of the harmonic oscillator Hamiltonian as trial states {{cite:0fcbeea9ad441b2f32c73f898279362fc8ec88ce}}. The results from this ... | m | dbe1ef944da6d6b1126196a3ed78efe5 |
Existing studies on analyzing privacy leakages focus on either general machine learning models {{cite:0bf245cefbd6a7b4d86feaea5770c4eb2586e590}}, {{cite:d9306cd23e019d7d0dfa8a4d0613b56ccb4092f8}} or in a federated learning setting where model is collaboratively trained by multiple clients by sharing and aggregating the... | i | d2a1798dd89e3982f760f4ebaea9c0a9 |
Where M is the number of simplified input features, {{formula:4e8844c0-e71f-4a9a-9364-b289cf34a878}} is the number of non-zero entries in {{formula:2537afeb-497c-4929-9340-15b823189ccd}} and {{formula:26f09b61-0719-41dd-8dcc-28d505279e6f}} represents all {{formula:cff8cbf7-a5e5-4205-83c3-af5892c192ce}} vectors wher... | m | 4b5155d3ef535751026be6789a24a696 |
To demonstrate the performance of C-VTON, we first analyze Fréchet Inception Distances (FID {{cite:535e54ab82f6bd903a65c0d76a13db233752b7ec}}) and Learned Perceptual Image Patch Similarities (LPIPS) {{cite:eee24115062e3ab84e2c7389622835980d655bf7}} over processed VITON and MPV test images and conduct a human perceptual... | r | f47aa5f9c5b468e2f9e50526748a68a2 |
Once we have constructed the closed string field theory, we may couple the closed
string field to various {{formula:600fbe4e-d37d-4adc-8eb9-431db9fa6fc6}} -branes, which play a role of sources for the closed string
field. Solving the classical equation of motion for the closed string field, may leads us to {{formula:1d... | d | ab704e3371fbdb0e4cfe4fcdaf8aac77 |
The first approach requires having a large-resolution photograph (like satellite images) and produces pictures whose variations in style and semantics are limited to the given imagery.
The second solution can only perform some limited extrapolation since using a single global latent code cannot encompass the diversity ... | i | 3b07d2751dc6b7711c73a1fcc83f51ef |
It is recognized that CNNs are generally more efficient for their intrinsically-biased architecture designs, such as parameter sharing, local information aggression, and spatial reduction. Therefore, to enhance the light-weight property of ViTs, recent works mainly borrow the inductive bias from CNNs to develop various... | i | 8782e0aaf6280d3216a39cb7ef0377aa |
Both Monte Carlo Dropout and Quantile Graph WaveNet require an additional calibration step, however the advantage of the latter is that we can re-map every tau value, as suggested by {{cite:214ab0281ab78b3d0872ae9606275b1a668f1f1b}}. The underestimation that is present in many Bayesian Neural networks {{cite:214ab0281a... | r | 22b4c3d42499968a6228f2b0a9678770 |
To eliminate the impact caused by distortion, one strategy {{cite:c08209f5fb9c52ff1464cd51f8b864b573a065d4}}, {{cite:282d9b388dd66500404d82e2a48993e6f3ac6dfa}}, {{cite:8351667de0a6dd5c6aa0c1efc22da615ff631fbf}}, {{cite:8ef5f5504dc61dc76a7de40d28a27d05624c5daf}} is to convert the 360-degree video into multiple perspecti... | m | b29caa87ed8107a4f169386b350a4c29 |
There have been benchmarks and datasets for interpretability of machine learning models {{cite:e6d8b751e07565104d4f7755cc5a62843b6e234e}}, {{cite:82c13fbfc26794ed8360fae3c55389b82ae87f9a}}. The rising number of applications of GNNs in several sensitive domains like medicine and healthcare {{cite:33c6ec67ea2ffb720ec5107... | i | 1dcb14216f72615edf5a65c7920c4dd4 |
We also use a Transformer-XL style cache, which holds the keys and values from the previous training step. When doing self-attention, the cached keys and values are prepended to the current keys and values, and we use a sliding-window causal mask {{cite:b0488cf0261f435a2373c282cd7bb5c50ae75180}} so that each token has ... | m | 6272492ff5adeefc7d7e0f843e85d80c |
In this section, we test the performance of state-of-the-art baseline feature selection algorithms in their feature identification capabilities in control. We choose eight strong feature selection baselines, including classic methods, gradient boosting methods and recent population-wise methods. We also provide one ins... | m | d46f78c28843fc8e2ef254c181a52271 |
Several applications of sparsifying transform (ST) learning and various extensions have been demonstrated in the field of medical image reconstruction such as magnetic resonance image reconstruction and computed tomography (CT) image reconstruction {{cite:5e887ca614e8c771372cd65fea8af6dacef3157d}}, {{cite:b607315bdd816... | i | 70d8aa73ab0a7b465402e444277c50e3 |
For the Bardeen spacetime, the Hayward spacetime and the solution of Ref. {{cite:357ce2570285dbda81a28e99394debea540fc5ef}}, increasing of the spacetime charge parameter implies monotonic decreasing of the imaginary part of QNM frequency; For the two solutions of Ref. {{cite:660580b9c1c3e6f2e0cf1e11fe0a26d282f255da}}, ... | m | ceb95d9ce87ab5ed554404e53acaa211 |
In this sectionOur code is available at https://github.com/anonymouswater/HAML, we challenge the capabilities of HAML in practice by testing HAA2C and HADDPG on the most challenging MARL benchmarks—we use StarCraft Multi-Agent Challenge {{cite:524fc552dc4d53bab9225c94c2994cfd618ab5a2}} and Multi-Agent MuJoCo {{cite:2... | r | c16c93a3d9eddc303f49a53fdfbabc2c |
Analyzing the behavior of coupled oscillators is hard even in the absence of uncertainties, due to the non-linear nature of their dynamics. One approach to reduce the complexity of analyses is to map the oscillators' models to their phase dynamics. Such a transformation is feasible under particular conditions {{cite:52... | i | 3e5afa4c00cf2e524f549dbb2e22a166 |
Here we highlight the keys differences between the CGCT and PGL {{cite:79bc3e8185d422cb25194fc1d40640335fcb9f30}} as well as the dual classifier-based methods {{cite:8d429b776fc854e48a1f760676f1424b85fd41d7}}, {{cite:ace0a2ba3b775d6cd2d576e66311f939d72e9e4b}}. The PGL {{cite:79bc3e8185d422cb25194fc1d40640335fcb9f30}} e... | d | 4f5ad3aa1e4359b8c8745db0cc9c4ccf |
In the past decade, we have seen the emergence of various Knowledge Graphs (KGs), such as YAGO {{cite:4de54cb845a58931cdd9f76ebd7e4b03968c3684}} and DBPedia {{cite:e88770968c4b3bd054d631a7fbc63976a049b7b3}}. They have achieved great success in both academic and industrial applications, ranging from recommendation {{cit... | i | 8fa32a61504054b8c5b3f2898477c5ae |
where {{formula:32e53c4d-998e-4cc3-ab58-6c1af4388c39}} is the two-loop coefficient of the QCD {{formula:1a4fd2bc-89cc-467d-a82b-a857bc397067}} -function. According to {{formula:2427803f-e076-4f51-801d-60fcf228d74b}} {{cite:39bbe387fc19aac40d5daa875d507e4c87f2de36}}, we obtain {{formula:66c45744-bc9c-4c4a-a5ba-c8ea6d8... | r | d381890e7d758a08a533c9c53aedb743 |
Our proposed scheme focuses on improving the accuracy of absolute pose regression. As such, we evaluate it on multiple
contemporary datasets used for benchmarking camera pose regressors, and
compare the results to recent state-of-the-art regression-based absolute
localization methods. Other classes of localization sche... | r | 034e03abe1f8fbbe7110bef6f816ab39 |
In terms of classical {{formula:54e0ed31-49ee-48d1-b5fa-3ece533a2cc7}} -SAT, as mentioned above, in stark contrast to {{formula:506ee89b-9e79-4b25-ac2f-2348e84c8178}} -QSAT, solutions to {{formula:dcbde4af-e6d4-49e3-8ed1-3214682c06dd}} -SAT instances with an SDR can be trivially computed. As for parameterized complexit... | d | 22c05eb2af8d9b1abd36c44b72af64e5 |
We can immediately recover the proposed Lyapunov function in {{cite:9657d1a93f126c12dbace92e89a6cef805327b12}} by setting {{formula:2b3de27a-7c27-4be0-8eba-532e1f1beb59}} to
{{formula:e98531ec-96f9-4c8e-b537-0be528a9df14}}
| m | 599a6d88531e9044add0e6ae52926da6 |
To formalize the encoding process with solid physics backgrounds, we try to find an appropriate way to derive the thermodynamics of encoding based on the nonequilibrium second law of thermodynamics. One can find a reverse derivation process from information quantities to the nonequilibrium second law of thermodynamics ... | d | 42a879b5d5f8767c333d64127da0792d |
Among the deep learning literature, medical image segmentation has emerged as a specific research topic, owing to the typical size of the available training datasets. Bridging the gap between few-shot and data-heavy learning-based segmentation methods, the infamous U-Net architecture {{cite:c0ad1cf985948c812444e0f88376... | i | d9c34ca4f459841c27bfd20d570b019a |
Methods for inter-frame (temporal) prediction have been proposed to achieve efficient compression of dynamic point clouds. These methods can be grouped into three main categories.
In voxel-based schemes {{cite:4ab839a95671bb95e0d79d77a523f7d4031e1a4d}}, where a motion vector (MV) is estimated for each voxel, a few poin... | i | f3b284922d8407b7fa419c8fbefb984c |
Dust thermal emission in PNe peaks between 20 and 30 {{formula:82d8b989-494f-4cdb-87d1-67fc461094fb}} m and represents most of the integrated intensity across both SWS and LWS ISO spectra.
In cooler media dust emission shifts towards the longer wavelengths and can be estimated from LWS spectra alone {{cite:993ce8d3a709... | d | 7db0f8587d78e3ee467c74d855e5af42 |
Figure REF
plots a 95% confidence interval on mean log-regret versus the budget used thus far. We perform experiments using a single budget in each problem; the goal
is to minimize regret at the point when the budget is fully exhausted at the right-hand edge of each plot. To focus attention on these budgets, we plot r... | r | e5c5c4765e40b53d237812f0ae396ca7 |
The purpose of the present article is to investigate inflationary cosmological solutions consistent with latest Planck and BICEP2/Keck Array data in the framework of mimetic {{formula:d0a634ce-b87c-4ada-9450-f7aba44730f0}} gravity with Lagrange multiplier and mimetic potential. {{formula:9c5728f2-ad64-4ccc-b126-c026ba... | i | 9bde696db721abfc4f5d024ac1b23590 |
One challenge in federated learning, as noted by {{cite:88098a6196fbebdf0cc0227bff74acbc5ca68647}}, is the quality and data distribution of the sources being used for the training tasks. A related challenge is the
potential for random failures or adversarial parties to disrupt the federated training.
For these reasons,... | i | d822879b3c95e6a1da5bcfb9a5b71f4e |
In high energy experiments, the power-law or intermittency behavior can be measured by calculations of SFMs of baryon number density {{cite:79c8bc9c0f64b46b162234c063d2547c9766b58e}}, {{cite:02d61bb146f3e06315e2f1c97d98ab911ce7a2e5}}, {{cite:99ea7b3f5e03ed0e7b7f38f7f5fbbe8306237a0d}}. For this purpose, an available reg... | m | 3db8e4c1ef57b83c20ab9a0d3a20ba46 |
Batch-independent normalization methods, such as GroupNorm {{cite:3ff430ad04a76ec2bf374a80ef77d562a54be80f}},
Filter Response Normalization {{cite:a6c451cb7a9a973703ac5dbe59dc8cfc8225dc16}}, EvoNorm {{cite:db3dae15ccf2cf7f5c827e2aab6931c12ae1d484}}
do not suffer from training noise and have competitive accuracy.
Howeve... | d | de165d489521652dbdc9642eec3976c9 |
In many clinical applications medical image segmentation is an essential step that foregoes quantitative analysis of clinical features and diagnosis. DCNNs showed state-of-the-art performance for segmentation tasks in case when the training sets are large, representative and correctly annotated. In case of noisy annota... | i | 58ba2ecabd4b82231a53181d03ae29d2 |
Theorem 3 (Positivstellensatz {{cite:7d8152c35647dcca350a32809687b078fdaf2537}})
Consider a collection of polynomials {{formula:f373be6a-1d1b-4bef-b358-01f1038c34e1}} , {{formula:728db01a-1256-406b-a576-b2414d2a226d}} and {{formula:3017a920-0a48-458e-bfcb-68acb3eec2ca}} . Then the set
{{formula:76a74a2f-b2e5-45e7-a6... | r | d07a154962b0c764e04f63ff905c3167 |
In the experiments, we used the RSICD {{cite:4054fa5236834e61af047c5e8956b20280f4252e}} and the UC Merced Land Use (denoted as UCM) {{cite:a1d79d832cf3512b268282b9d963ab65ca5702ce}} datasets. RSICD includes 10921 aerial images, each of which is a section of {{formula:5b2b1c42-b564-4180-82f5-efa99de3ba9f}} pixels and h... | r | b0c48cad70f28b852dd0b54993b20989 |
Despite the motivation for using margin-free classification, we also explored the possibility of using multivariate Gaussian densities in a similarly constructed Two-Stage Gaussian Mixture (TSGM) model. We used the R package mclust {{cite:e40e6f0a88e88549dab4600e046f349a844d8912}} to fit the data
which gave a lower acc... | d | be4d89f7cf67629c8d07e2c7d95ccb70 |
Many traditional ZSL methods rely on a predefined class hierarchy for explicit knowledge propagation. ImageNet, whose classes are a subset of WordNet, becomes the ideal benchmark for these works. With 400M image text pairs, CLIP{{cite:22153572cbdcb96b37b39751e91b90462cf3da38}} vastly outperforms previous methods. Our m... | r | 58914637a3d2debdb1c0fd3b68c4a366 |
We conclude by showing how quantum volume relates to – and can be shown within – the VB visualization framework. Obviously, making a rigorous connection to quantum volume as defined in Ref. {{cite:b490002224c41635557b4d2bc9507b58f9f27de2}} requires choosing and performing a specific benchmark (the one proposed in that ... | r | ea7f41805a612245725cc01dc95a240a |
The previous experiments performed within range 0.48-0.68K have only shown the general features for returning the crystal facet growth kinetics to the normal regime {{cite:57ada1ee5952952ff41bb8aa6706fac7f3dac3cf}}.
It is shown that the significant relaxation takes place for time {{formula:2b667eb0-1672-42e0-addc-50d9... | d | 21ea9d7566034f928dec710bc2f0ec6c |
where {{formula:2bb3948d-bac1-464c-9830-dff13099ea75}} is the advantage function associated to {{formula:c43cff5d-1aca-468b-82aa-fc0d32ea9faa}} , and where {{formula:a6adbe13-983d-4c28-a113-403cb3c5e010}} and {{formula:d0ee769c-1da6-4acf-b506-80ac4c500b52}} are the action-value and value functions for the policy {{... | m | 0cab29f0e11a0db855e3aad5f06fa955 |
Our method outperforms the conventional method in two aspects:
(1) information of subsamples at high-redshifts is conserved. To pair subsamples with a slight difference of redshift is a simple and commonly used method. For example, {{formula:50f028e4-9551-41c0-a3d6-92252db922ec}} {{cite:792392fd8098f479437a13eea8117a9... | m | 8c054df374d11481e7658cf76075dcdf |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.