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The article {{cite:53034cafa47ab405bf63cbb218450e77dfef0258}} contains a general discussion of redshift estimates,
which we use to prove our results in the context of the Reissner-Nordström-de Sitter spacetime.
Similar estimates are used in the article {{cite:c5faf6703fdb36a597fb69c70817f5be18d5f0b1}} to study the wave... | r | 7a61fef64605d7717ba5368c5c4696ca |
{{cite:57ee3ea7e19b756a67b6b2c1908a1f42b0ee5943}} modeled the behavior of a jet propagating through the progenitor and the surrounding circumstellar material
and showed that the resulting light curves exhibited both short-term and long–term variability. They attribute the long-term
variability, at the scale of few seco... | r | 03a68c67102d1216bfe6b3f803fec85b |
Generalization of minority classes.
Besides improving the mean accuracy (reported in Table REF ), finer exploration reveals that most of this overall improvement stems from a dramatic improvement in classification accuracy of minority classes, while preserving the accuracy of majority classes. Specifically, OPeN improv... | r | 85e6575ac8805a82ac6ee51dbf9cbfb8 |
One limitation of the proposed technique is the potential difficulty in identifying causality in data with very high dimensionality (i.e., the number of observed signals) or very long temporal dependencies between latent sources. In either case, the covariance matrices required to identify the latent causal sources may... | d | 011039604f9e5cfa68959d98bb6cb4af |
Actor-critic experts are trained through a TD reinforcement learning algorithm {{cite:35a9d165337642d01a356cd2ed7fb91615af3ba6}}. The TD-error ({{formula:ea12ba41-88fe-4a5a-a51d-65a848fe8c6d}} ) is computed as:
{{formula:8b74c63f-008b-4cff-a6dc-efd4a305fa92}}
| d | 81dd99c031f1c24f6a608af07532e45a |
For other partial waves, no structures are observed directly
close to the real axis (c.f. Fig. REF ), while we do find several poles in those partial waves
located farther away from the real axis. All the poles obtained in this study
have isospin {{formula:7c1ab2e2-baac-42d6-9c43-c276d1a96d57}} , and no states with iso... | r | 0bcd64876269617ac99532f1a7a55276 |
Overall Architecture Fig REF shows the overall architecture of our proposed method. As Fig REF (a) displays, we first train an IND intent classifier using CE or SCL+CE objectives in the training stage. Then in the test stage, we extract the intent feature of a test sample and employ the detection algorithms MSP {{cite... | m | e4629b876c16be012608452991e77f7b |
The {{formula:80ac1162-754c-4899-b66e-2d2a51ef5966}} mass distribution (normalised to unity) is shown in Fig. REF . The solid line is the result with no extra {{formula:39460b49-e561-4011-b2cd-2dc1b8f3592d}} breaking, mentioned earlier, i.e. {{formula:e675a702-ae06-470e-aa75-edf58bbd50ae}} .
For the small values of {... | r | 66c15ae465e13af50c37726074c1a721 |
There is a vast body of work on Generalized Linear Models {{cite:95da132c94a3cbdde62e4b2a2bf879bde1e8f552}}, {{cite:ba44da99627c0e6abde102683727cb3b8c0f54ec}}. Classically, the focus is on the setting where the function {{formula:10a0ef73-ab36-40ff-bdc4-d2583813db66}} defining the Bregman divergence and hence the link... | d | 6d627bf86bf23761403e059529b98a5e |
Let {{formula:781aabcd-d91e-4a92-a876-2d213db21950}} be a prime dividing {{formula:52baa940-1630-4fc3-8506-675b6d43cd04}} . Then {{formula:ac072f37-bae3-4125-bc6a-bc06af98beba}} . For {{formula:0c6f2128-b63e-4686-a18b-4849f355b507}} , {{formula:1a45b264-f009-400f-b455-d1dbb5ec44fe}} has a single side joining the poi... | r | 32e1662b6e2c374b16ad0272fc194962 |
The integro-differential system Eq. (REF ) is usually solved by the conventional iteration scheme. Given the value of {{formula:ac7c28e2-a544-4637-9fd8-14163b77ee28}} at the {{formula:ca83970c-2676-4d2e-a20f-af80e5e0f317}} -th iteration step, the velocity distribution function at the next iteration step is calculated ... | m | b6cec42c0d5d60baec34f38071d6adb0 |
The base-field interaction Hamiltonian is given by a dipolar coupling, in the rotating-wave approximation {{cite:96a8f06f90d73c32665581190b6247d5e0163050}}, {{cite:91d65ef3bf2fdb54d81cba0e837a09f810e16a57}}, {{cite:172221091d592f3c2d6c5be20392d5dde9a017a3}},
{{formula:a928934b-13a2-4768-9613-c50b54d49f18}}
| r | ddfb29a8fd5d1e05b463983c0af91265 |
In our study, we adopt a Transformer-based model architecture which has been shown to produce state-of-the-art results for AAC {{cite:fce85d2c47d2d91710105a379debd8f6f3a8ce48}}, {{cite:8b8f20ffba88e53c86fb12e99bd5abd865b86611}}, {{cite:748681db1dc9d124e1be81a765f21375ab9e2f53}}.
An overview of our method is presented i... | m | 69f5076bca49d82b362dea05a9ab0bbd |
Proposition 8.1 (Lemma 1 in {{cite:4bf0cd54eb6b3cfe82c6bfcc2289df86d5da9c56}})
Let {{formula:cc59fef6-a97f-490d-a192-5cdcc5614b2a}} be the importance weight for two distributions {{formula:68898709-a071-490a-980d-b7621d4c56f5}} and {{formula:de84e50a-e88c-4333-84d4-3d58baf64776}} . The following identities hold for ... | r | c73a1f0ee513dcb0ae6b04227c1b2eae |
Over a relatively short period of time a plethora of explanation methods and strategies have come into existence, driven by the need of expert users to analyze and debug their DNNs. However, apart from a non-exhaustive overview of existing methods {{cite:d703e92d5a4466cf045d356a08feb873c2f0c0bb}} and classification sch... | m | cce01503baca1b1e7ecf1cd428fddd0d |
For each tracer, we calculated the temporal variation of the total unsigned magnetic flux by summing the absolute values of the magnetic flux density in pixels multiplied by a pixel area. Before calculating the magnetic flux, the maps of the magnetic field were preprocessed by applying a {{formula:eeb578e7-f2cd-44b7-97... | m | 736dc5a2685e24f17b5478d9fa5be37d |
Main results. This paper makes two main contributions. The first is to introduce type-theoretic awfs's and show that they give rise to models of Martin-Löf's type theory with {{formula:5c35c642-6166-486b-99b4-6904e6b3a0e7}} -types, {{formula:6e7c8cbb-d33f-460c-b12b-4c36faad31c3}} -types and
{{formula:a8ed3027-17bb-4007... | i | 588499366372928b2eff335d2d70fc27 |
CSSL's simple training pipeline enables the algorithm to be analyzed theoretically.
To do this, we extend analysis of the neural tangent random feature (NTRF) {{cite:2b626d0fbc6f9e7d2caf128b29864c14075fc7d4}} to encompass multi-layer networks trained via streaming to perform binary classification using a replay mechani... | r | f8dbb934e132aef4f0b4b1c79df020fa |
Video Frame Interpolation (VFI) aims to synthesize intermediate frames from a low frame rate input video. It plays an important role in many applications, such as slow-motion effect simulation {{cite:c21f49570d137e9e80b56dbab04218d9a86523b3}}, {{cite:13e81d8358b33349c558fa8bc8cd2ae84b992fab}}, {{cite:0b97d5692ce5c709bc... | i | 894c68e47217fb8d92dad8d84f4f6203 |
RIS-empowered wireless transmission has recently drawn substantial attention from both academia and industry {{cite:a0e4e2c7ca1353c1d457b3a34cd8a43a1cc09304}}, {{cite:7bc928b85ab2a4560fec8ba35acaa9d6446aaa44}}, {{cite:23cdc860f3eac1f981fa1136f2377064ab129f90}}.
There are many research efforts examining the differences ... | i | 0b4f92bee6438c5eda6975d607fc97cf |
According to Cori et al. {{cite:4e24760371b0fc0b31961d42e747b26329f470ea}}, the size of the time window will impact the estimations of the instantaneous reproduction number. Small sizes lead to faster detection of transmission changes and higher statistical noise, whereas large sizes lead to more smoothing and reductio... | d | edf9dc41b4f5c6d38f2f0dc22587583f |
Comparative Performance Analysis : To best of our knowledge, this is the first ever proposed multi-modal biometrics deep learning segmentation network. Hence, we have not compared our results with any other method. Although, one can compare it with the existing techniques, such as {{cite:75f0876d50047ab7d90efd8650dc7e... | r | 74da7b35b30d5edb29376f1a8870c793 |
The grand-canonical TB evolutionary search is performed using the USPEX code {{cite:b02d489a6bea3472b6296d509e10753ca7c36061}}, {{cite:346ced512dbe796f1f140795f3bc5f9d58dcdfda}}. The details of this method are described in the work by Zhu et al. {{cite:332678c55caca10be2dd937be7ef157d16cfb33e}}. In this approach, vario... | m | a4df7e622dd07b3d72cd6368c78ce52c |
The privacy risks of DNNs have already been pointed out, where a DNN is prone to memorizing sensitive information of the training dataset {{cite:615e4f8e5cf534237c0e71fabfffe72883152267}}, {{cite:dc717e91ad3ea671fe7e8cd5bd2bf548b86af385}}, {{cite:4212dd5c67ccab54451184a6b0ab52ba8ddeeb1a}}, {{cite:6cc931377e6fdc5c911dcf... | i | 1f4a168e890c41228079ec4eda3bbf27 |
e) The Hamiltonian describing the odd system (REF ) involves a term {{formula:c8f33ab7-39c9-4fe9-b4c0-5facbdd53488}} which describes in a realistic fashion the neighboring even-even system. Indeed, this has been used in Ref.{{cite:61cbe98051a6b47777e820869b83eb776c85a4fd}} to describe simultaneously eight rotational b... | r | 061f15b183051b122c6d33fa1911af47 |
It is possible to define the eigenscheme of a tensor which is not partially symmetric, as in {{cite:c58286be2f267608432002181ca35fd8c9541a64}} or in {{cite:0451cd3618591e46bc35035434031d7569977a11}}. However, with the choice of a basis described in Remark REF , it is apparent from the definition that for every tensor {... | r | cd66bc32a31b322c7418ff3f5583d69e |
To answer these questions, we first extrapolate the gas production rate in our KB from the most recent extrasolar models. To do so, we compute the dust mass loss rate in the KB due to collisions using a state-of-the-art model of dust in our Solar System {{cite:1f8f8b478def452a4fe7262577d4aa17a64ed9bf}}, {{cite:b00ad50e... | r | 576a96acfef8121d6cc68a77e8df1498 |
Although this work focuses on the MPO algorithm for policy optimization,
our results also suggest that the benefits discussed in this report generalize to
other approaches that make use of a critic (e.g. DDPG). Recall that DDPG
with a C51 distributional critic is the very competitive agent known as D4PG {{cite:9ce1da24... | m | 1f6876d731500d9f952e679e8daa169d |
We then study the entanglement properties of states from these ensembles, focusing on the bipartite case (two fermions and two bosons). We analyze the spectrum of the partial transposition {{cite:35a046778a830786fe08145635152808340178a5}}, {{cite:8a6967c3b32ee3b065bf3bc9c2612b4d99544886}} of these random density matric... | i | d3c7389054228c5c3b847ed0262b2b54 |
We consider a multi-label image classification problem with a training set {{formula:68c460eb-17df-4f4b-9814-7f0adaca1b65}} , where {{formula:9fc62614-c81b-4c78-8d6e-a3c600562c4b}} denotes the {{formula:150c41dc-c267-4582-8347-7f8aa93e4cc1}} training image labeled by a multi hot vector {{formula:c9675bac-86c4-4420-91... | m | ec590e8508731411820c575cd823b15c |
In this work, we introduce a regularization method for contrastive language-image pre-trained models which encourages shrinkage of the image-patch and text-token similarities. We demonstrate how our regularization method can benefit zero-shot performance of these models by training a model that achieves SOTA zero-shot ... | d | 857c479104e5e91e9869bf24f14c2ede |
It would be interesting to extend our analysis to other types of inner boundaries. For instance, one can imagine cutting a disk from the cigar at some {{formula:45239b2d-e128-41e7-b3f8-7090ee2ebf58}} with {{formula:d71948dd-9f39-43f9-aa45-1bce9008bbd8}} and compute the inner boundary contribution to the entropy of s... | d | 44824e16e641f2daa0408196e1e706ac |
Our proposed TokenMix consists of two parts, i.e., token-level mixing and label refinement. We decouple the two parts and then compare them with the previous methods by fixing one part. In Table REF , we compare TokenMix to ReLabel {{cite:7f595cac7597d9f34ad4e37fb7a6497dede5b0f2}} and TokenLabeling {{cite:f5a3876d915a2... | r | 0c9c51e9cdc4b08f8f39f403a9338042 |
Effect of Backbone Network:
In this experiment, we evaluated how our model behaves with respect to different encoder backbones. For this purpose, we employed MobileNet {{cite:e9b95f88a1b1ef608b170489af79eeaf20fec810}}, VGG-16 {{cite:06a12389ff19f84a1c67618a1420272352863528}}, ResNet-50 {{cite:f4d9ee1b16aa16fd3f9cba8c93... | r | 8340ab7771d7bfeaf489631bf5f48ef9 |
Supervised learning has achieved remarkable progress in many fields with the help of a large number of labeled training samples {{cite:7b8c102199bdc20f6f2da942eaa3730fc7df73c1}}. However, when there are few and even no labeled samples, it is difficult to, if not impossible, induce a supervised classifier. Rather, there... | i | baca8a431fa0b17c9e3a2848962a5e44 |
To tackle this issue, an intuitive solution in conventional SAR ATR algorithms is to collect the distributions of strong scattering points of the target in full ({{formula:d758a913-42e5-400e-bce9-391f274f335a}} ) aspect angles uniformly {{cite:1c0a4c7d9c6d900f0910792b13f3de6b7b61b38c}}, {{cite:66cae3efcaee5e468644f1a6e... | i | d0085831e8d44eaed591c19f06cf3716 |
Inverse source problems are of importance in several scientific areas including biomedical engineering, antenna synthesis, geology, and medical imaging {{cite:665c0f41c78f60658e6b65afdc425649ba7c65dc}}, {{cite:a19ce347dee387fc1004ddf17a72c8bc95ec9f5a}}, {{cite:cacc73a0c4f295712245f3f0a18e3ea47de96561}}, {{cite:50592102... | i | 4e60359953bb62fa62621e98ead9abce |
From Definition 2 any genuinely multipartite entanglement in the biseparable model {{cite:59b439d9a622d82a7a4d6db83aadac6cdd8e93f2}} is 1-CGE. Moreover, the present {{formula:1beb4804-fb79-47ce-b31b-ffe13c12d52b}} -CGE is stronger than robust entanglement with the robustness-depth {{formula:6dafde24-f48d-4c30-bff7-ba8e... | r | 4de74a696b3eb74113adfe293a8aa344 |
We introduced the quantum theory of measurement to the process of HHG, which establishes the connection between the two mainly unrelated fields of quantum information science and intense laser matter interaction.
Using the general quantum operation in terms of coherent states allows to conceive new light engineering pr... | d | 32951c56661bb58c2dc3a889a537c3d0 |
It should be mentioned that as for the unconstrained tensor decompositions in {{cite:0f7026177f523b7df506f4f6a40835c0d11a4bed}} and {{cite:aabcebba9d88b3b87f73b0b1c8ace00e5e5e3276}}, there is no guarantee that the proposed constrained Tucker2 decomposition is unique or that it will converge to a global minimum. In fact... | r | b77b0d081f0591270b2db6136b417d1d |
We conducted small experiments on only models described in {{cite:d1a65c6c744156342350e8bc7d96da1d501eeccd}}, {{cite:92f9fdeef234f0eb56d30c909b9dad4a28b18d8f}}, {{cite:f2688cda9b4f3de7d651dc628035821d42731a3b}} using the same experimental setup described in sec:experiments. The CIFAR-10 dataset was used, and the result... | r | 64d27b19efd8daf051365824f2ff8b0b |
The TAP equations have many important consequences in the study of mean-field spin glass models and related applications. First of all, based on his iteration, Bolthausen {{cite:2e7cbc69fdd8c37bc6d6e7c8e66da1f628489404}} performed a conditional second moment method to derive the replica symmetry formula for the limitin... | r | c0767253c259d5a4dec47c7ca2bca148 |
The Standard Model (SM) predicts a neutral Higgs boson particle whose couplings
to other particles are proportional to the particle masses, and that couples to
photons and gluons via one-loop-generated effective couplings. While the Higgs
boson mass is not predicted, the relation between the Higgs boson mass and
its wi... | i | dfd9be75c6a6ab5668b1162b118bdb4b |
We evaluate our late fusion multimodal classifiers following the cross-subject protocol from {{cite:e07d9d332854c28a5f800eec4b9229c749179b48}} for Toyota, the inter-dataset protocol from {{cite:d7b0a7f5c182e3387c04b717a0525333856fc702}} for ETRI, and the official test split for Sims4Action from {{cite:895c362968aef6496... | r | 7d198847249fb740b132f41824cb3cc7 |
In TABLE REF we provide the reconstruction results, training datasets, and model parameters of these models (lightweight models and large models are separated by the bold black line). According to the results, we can find that: (1) using a large dataset (e.g., DIV2K+Flickr2K) can make the model achieve better results;... | r | b0c527f48ee29ec0e402dd652246cae3 |
More recently, in view of its impressive performance in domains such as computer vision {{cite:e209c330dbcec6d0461037d8f847ae7227325321}}, speech processing {{cite:6532c68cc7b638c0d3e25314adb845f211863c7f}} and natural language processing {{cite:5a81ce5e4bef64de15c16234ef54ddd41d57bb51}}, researchers have also been usi... | i | 3cb5b5b3f86677ea705d8ce991384e79 |
Case 1. Since the DP equation (REF ) also admit the peakon solution {{cite:698e9855e8e47becdd57d9cdf8dd45641732932e}} {{formula:384691a2-1000-400a-b279-7ad10a8cee7d}} , thus we consider
the initial value condition (REF ) and periodic boundary condition {{formula:9c38b843-e880-469e-aaff-3b4ad1a88495}} . The considered P... | d | 9be1f7a8bfdbbd8729f732a3a0bc6867 |
More precisely, the process {{formula:024dd82c-ca7e-448a-8955-3a384c2a62c0}} considered here is
non-evanescent due to the periodic boundary conditions of {{formula:92847d08-732d-402b-8aa0-bd6d9dc867c8}} . It
is Harris recurrent if it is in addition a {{formula:cff6e1be-cb77-4c61-b38d-cdb909a42253}} -irreducible
T-proc... | r | fbb27b872aaae659dfdd094c56b410a1 |
Approach 1 - Stratified batch sampling: This strategy aims to modify the training sampling strategy to remove the discrimination before training. For each training batch, the data are stratified by the protected attribute(s) and samples are selected to ensure that each protected group is equally represented.
This appro... | m | c22e20c2335b24dfe96d3fad16e60c25 |
JPEG {{cite:725bab6993a0291bbe4d9d8998a90fda15ba5f60}} provides a lossless operation mode which uses a pixel based predictive coding scheme for compression. The current pixel is predicted from the immediate neighbor pixels to the left, up, upper-left and upper-right. There are eight predefined prediction modes which ar... | m | 44056e75e38f46dd1b9991fb02c7d197 |
The screening effect from rare-earth atoms {{cite:aa9c545c66674b7fc76e8e63ebb76ee8cbddce56}}, {{cite:11797884b5776a8f238c21eca3f40bd9f68c114c}} could be
responsible for the competition of magnetic orders. At small {{formula:33559b3d-c50a-405c-98dd-e874a60308e6}}
where the screening effect from rare-earth is strong, th... | r | 86f07f8348ba01ff7ed9b7117525dca1 |
This work features a low-complexity version of the framework to assist with network comparison and reducing computation time.
For comparison purposes, the number of deep learning techniques applied to any NN architecture was kept to a minimum: only dropout, which itself was inspired by the stochastic Poisson-like firin... | d | a2fe9160a4cd32b959ec49b773526308 |
Generalizing our work to the learning of other minor subspace analysis based tasks, such as slow feature analysis {{cite:0bd202d7f63f4d565d1719e02b7cd44b722fee5f}}, will open a path towards principled biologically plausible for invariance learning {{cite:e078bcebe35788a9360632ea6d1536f09ceada53}}, complementing the wor... | d | 7a1a7b1d67db1b44c635efe0405e786d |
The practical scenario considered for simulation is illustrated in figure REF .
The AP is located in {{formula:f5745e82-e419-40ed-a50b-cf0fc56e6330}} .
The multi-antenna user is located in {{formula:55bb0b72-8cca-435d-a909-5a59e3e76336}} .
The distance between the AP and the IRS is {{formula:702b1ff1-c919-4b1b-89cc-bfe... | r | d093ab8b6858d14b301983ae523361be |
Various GNNs have been proposed to achieve higher performance in low-homophily settings. For instance, Geom-GCN {{cite:25d0fdf82b83ee0b5dd6803a46892da5900cc48f}} introduces a geometric aggregation scheme, MixHop {{cite:6fda67ceb8d69ea3603b8c64d2350808624f8ddc}} uses a graph convolutional layer that mixes powers of the ... | m | f7c7808aabce4afea426819407d36568 |
The Hubble constant, measured by the SH0ES collaboration to be {{formula:4dbed65c-6df2-4c6a-961c-679173afe998}} {{formula:451fc64e-6c47-4bf0-bf45-d23564b1e11d}} {{formula:afa19582-b3de-4817-9b1b-6d4cf3da6af2}} km/s/Mpc {{cite:4f3038a857e9687a08a83288159a9e312f88affd}},
is in conflict with its inferred value from the... | i | 5cab9fedb1b47ca66e3d8315ed48afbe |
The angular averaged sensitivity is one of the basic measures to characterize gravitational wave detectors {{cite:3ba3fc8086fd69b6ae10788e79ac8b6a8f0317b9}}, {{cite:bc0a172c7f5444cd7cec5805bfb7ae38665dcd46}}. If the noises of detectors are statistically independent and have an identical spectrum, the angular averaged s... | i | 09e74813635e4dce3f4984588b426750 |
To the best of our knowledge, we are first to propose a GAN-based framework that is able to learn an abstract representation of the market that can also react to the observed market state.
The proposed framework leverages a Conditional Generative Adversarial Network (CGAN) {{cite:dbe38ee530e9ffb565175fe7894b6fb97a146b4... | i | b28d47561becff539048de8637918133 |
Comparison between the present formalism and that of Ref. {{cite:e662682e45ba00181fb60b93579a6bad94b469a1}} reveals the following features:
| r | 59d42d30312a79d5186ae9cb6e8adf46 |
Most studies consider long-ranged topologically ordered systems in two and three dimensions.
The models predominantly are located on lattices that discretize a two- or three-dimensional differentiable manifold.
In this paper we construct exactly solvable models for quantum phases on connected graphs which do not fall i... | i | 6297778e856548a815d4fbcc6c7e818f |
Subgraphs are studied to understand underlying mechanisms of graphs like gene regulatory networks, food webs, and the vulnerability of networks to attack, and sometimes used prognostically. A popular example investigates motifs, subgraphs that appear more frequently than under chance {{cite:28d66f7a091809791ebd4320724e... | m | e91b28fae685454750e7156c96b7b522 |
Human Judgements. For more comprehensive comparisons,
50 inpainted images from GC {{cite:358be4d3b510bcb2e32a9af3167f1b871efbb99a}}, EC {{cite:4ccd3f33e38d996f9a20cd964c819be9f5096f58}},
and ours are chosen from ShanghaiTech and P2M randomly. And these
samples are compared by 3 uncorrelated volunteers. Particularly, vo... | r | 11631004b0ab4302d7b6ea910d0f4c9d |
Representation learning of patient Electronic Health Records (EHRs) is the foundation for data-driven personalized healthcare and clinical decision support {{cite:de55622024d2e286cd042f35d4d6f8b7af2ae07c}}. Many approaches, in particular deep learning models, were proposed to learn EHR representation {{cite:0a194b2e5d9... | i | f557e7ca1008db385901471bc3e25bf3 |
Neurons in the brain must operate under highly non-stationary conditions. In fact, most behaviorally relevant sensory stimuli as well as internal signals are rarely constant in time but may change rapidly. In the presence of noise, such dynamic stimuli can be reliably encoded in the time-dependent population activity o... | i | 760de8d1652ac603fc702fee4092fc15 |
In this paper, we have calculated the Rényi entropies (with a spherical entangling surface) and entanglement spectrum from a class of hyperbolic black holes with scalar hair in terms of the conformal mapping approach {{cite:88670ea665a47140abb33fa7eea92fd08d09d269}}, {{cite:083f0ecdf7e814b9bee1db22bc71472df7925771}}. T... | d | 007081ff65b09ad8139276aebdc4a586 |
For our future plans, we intend to integrate the proposed method to a SLAM framework.
Moreover, an increase in the classification accuracy will lead to an increase of recall in the whole system, therefore using more powerful networks, such as ResNeXt {{cite:fb210c3842964539a424d7d21705b05031ff71e2}} and ResNeSt {{cite:... | d | db61d4e24b652491987770cd9fa52412 |
Neural representations have also been used to learn deformation priors that encode the variation of object shapes across semantic categories using direct 3D supervision {{cite:5c28fa5a77867a32b8598b9ab851c79ec1ebbded}}, {{cite:6e7ec6ad0ccfaa37ef456752dde03986c09e744c}}, {{cite:076af639acfefdfc6713e581255743f0e192b089}}... | i | 0ae78d159286014e8c9b50dd57abb62c |
A simple shape of primordial power spectrum, obtained through deconvolution (hereafter referred as Reconstruction) solves the lensing amplitude anomaly.
The Reconstruction also solves the closed Universe anomaly and brings back cosmic concordance.
Importantly, we find that a solution to the anomalies within the Plan... | r | 1cfd738a98b017c0c95f8aac01cbcd2f |
To bridge the gap between pixel-level classification and image-level annotation, it is essential to localize object classes in the image from image-level labels.
Most WSSS methods rely on the Class Activation Map (CAM) {{cite:6511a0201ac130fa9f0bee03ceb7fc65e397b85a}} as initial seeds from the image-level label to lear... | i | 197862b77c5b75b249c1f5d3dab86356 |
From Fig. REF ,
one notices that most of the BPs are excluded by these experimental bounds.
From the 4.47 k viable BPS used previously, 3k are excluded by the experimental bounds {{cite:3c5dbe17c98b727bc103b15fced9d58bdc18e9f9}}, {{cite:b8be1362ed6c9a3cd1e6d94b622b0d02fca26643}}, {{cite:1498e7605c4f8d98d6870ef7c4b7846f... | d | c295522c6064d7b4f8924cef2ab2d140 |
The results for MoNuSeg Test-1 and Test-2 are shown in Tables
REF and REF ,
respectively. All benchmarking methods except CBM are DL-based. As
shown in Table REF , one can see that HUNIS
outperforms all DL unsupervised approaches by large margins in Test-1.
It also outperforms CBM by 0.0245 in terms of the AJI score.
... | r | 38691527a820984f92104857c80ba253 |
Tab. REF reports the conventional PQ scores between our method and Mask2Former {{cite:2122a3db2ff660d25734d85d12551f2b704dd6c3}}. As mentioned in the main paper, this does not take into account the instance consistency across the scene, since matching between ground-truth and predicted instances is done on a per-frame... | r | e51122b6bb59871e0f03850f5909a3a3 |
It is known that the prograde 1:{{formula:d5d4b655-2722-40cb-9b86-a10f303b8a6e}} resonances hold asymmetric libration centres with the usual critical argument {{formula:1dd29caf-4c3f-4a03-bec9-d2133b850004}} different from zero or {{formula:9e2c82be-a92d-42c6-81f6-bfa1b757338d}} {{cite:d7a1951193cde79ecf3868236b9358... | r | 5c68759212d47cb778b3a5e468d021c0 |
In policy transfer a previously learned policy is used to learn the new policy. One way to achieve this is by policy distillation {{cite:92ab275ef696eece38ceac9bc1b8a4fbcf30a8b0}}, which means that the agent will select an action by minimizing the divergence of action distributions between the `teacher'(source) policie... | i | 8e6529f610cab79be42c28b72171ba86 |
for any smooth parametric submodel {{formula:7c60e4d2-7c47-4d11-99ac-14cf8a30cdb5}} {{cite:8cfa98dcb0b347927f9a8b5753e89b22e7b5585b}}. Thus, the candidate influence function satisfies the above pathwise differentiability condition and hence, is an efficient influence function. Moreover, since the model is non-parametr... | r | 89ed906bfa0600ee2cb00258d4e57da1 |
As could have been expected, in particular settings SAM is dominated by algorithms specifically designed for this setting, such as
CAM {{cite:981429df2a7825c2571fec1cb683ed895c580125}} in the case of additive noise model and Gaussian process mechanisms, and GENIE3 when facing causal graphs with feedback loops. Neverthe... | d | 8ccb80b3678f5bfb81f8076b76e99c75 |
As shown in Fig. REF (a), previous SimSiam {{cite:a3d7592e439cd5506e8400e32b7fa2e2abfbc34d}} adopts a siamese network directly predicting the output of one view from another view, where one branch adopts a stop gradient to prevent from collapsing. As shown in Fig. REF (b), our framework contains two kinds of positive... | m | c8232371568d9015c3635dd7cdb34b26 |
[h(x*;)] + (1f”(1) + f”(1) (f*)”(0) C(f, ¶*)2)Var[h(x*;)],
where the first inequality above is based on the weak duality condition, i.e. Theorem 1 in {{cite:0e00abf15a8450644af305df7bdbb9a54b098c07}}Although strong duality holds generally in this problem, we only need weak duality in our proof., and the second inequal... | r | afa2ad64a927a5b976d3d7cf9f15b5fa |
Since its introduction by {{cite:54f6e1a7482ee32c9461e6d4550660a0ad486456}}, the BIC Bayes factor has been a popular method for estimating evidential value from empirical data. One of the attractive features of the BIC Bayes factor is that it can be computed with a simple calculator. This makes it easy to implement not... | m | e02d382e1fc69dedfaeb0f620b6af8e3 |
We use the CosmoMC package {{cite:e6cbbaa36f982c690e536b2548f7af0145a7fd44}} to infer the posterior probability distributions of the sterile neutrino parameters and other cosmological parameters.
| m | 7f1ab36d6762b91728b2a827afec55fd |
Margin has played an important role on the design of learning algorithms from the pioneer work {{cite:f75449a3135555484b3374a8391ee4b074cf90d8}}, which proposed the famous Support Vector Machines (SVMs) by maximizing the minimum margin, i.e. the smallest distance from the instances to the classification boundary. {{cit... | i | d45d1156ceb3535f1136b6b7cdc2e355 |
where {{formula:fa005d8b-f955-4468-be4c-f9c6605d8870}} is the Dirac delta function, {{formula:4c6f7567-96a2-41f2-b37f-bc0dc1b3cc45}} are the weights of the particles and {{formula:c295dc8d-4bb6-44c0-8848-7b5d5d73593e}} are their corresponding positions {{cite:842da3ecc6aad91ace214f03dc358f54045c44e3}}.
One can make ... | i | 826de13e4c03db1b13f13a4685b2b823 |
Local UniBlock vs. ST-Adapter {{cite:b33586ddd02e13ebf8eca48ca32072b03d087213}}.
Our Local UniBlock is motivated by the style of UniForme r{{cite:c95d8931cb36d5f728d2681dd3041ca4f63e8867}},
i.e., we treat temporal depth-wise convolution as local temporal relation aggregator.
Hence,
like UniFormer,
we introduce extra Ba... | d | 265af2faf16fd3788e548534f091b74b |
The remainder of this section is dedicated to proving Theorems
REF and REF . As stated in our
proofs, several of our
arguments involve computations performed via GAP {{cite:e68b7efa1a83da0799db4d0aa7083078673d2804}} and Magma
{{cite:c10c6f0e64a2af60ba685b0f63b473234de345bd}}. Our proofs also contain details about spec... | r | 061d933aee443d56d5e7ecc0112f8472 |
Proof [sketch]: The assumption of this lemma implies that {{formula:a2dcd218-9da0-4930-893e-24081566f07c}}
has a subalgebra (induced by {{formula:f332978f-747c-409a-979f-45e0b210f19c}} and {{formula:fc7718e0-dff0-427b-942f-4c747888c603}} , respectively) such
that all operations of the subalgebra preserve the relation... | r | 353cf2ca2305c949485cbd25ad2e0d5f |
Ultracold atoms in optical lattices provide a fascinating platform for quantum simulations of correlated many-body physics {{cite:c480ed209279193a26bd439b17a4c88ed50ab86b}}, {{cite:cb1446434032cfeca1ccccb411e535bd1d951d03}}, {{cite:0c0f57e85e67939da50735adb0564641c3664c5a}}, {{cite:bebf470a4fdb6e1d9350dfc7eb269dde6868b... | i | 763cc99225b1599ee1da91af0392d584 |
IRAF {{cite:14636a3c2cc0f39c5e86dba7e6eee95a802b9e5d}}, {{cite:ac00e7a4f3facff7c4c121bc46a1c72fd6892276}}, IDL, python
| d | 12627a8a23beff5fa7a94acd76b125aa |
All the experiments were executed using a vocabulary of {{formula:fef35a97-cf03-4a27-adba-2a236b75fc54}} visual words, created on Paris dataset through the application of K-Means {{cite:106fee2de91476f00a5bceb422f92a85921c5fc9}} clustering technique, initialized following the K-Means++ {{cite:6f113a871f5edcd73925c8dc9... | r | 7aed83edce6187cc0afba16ab1751e58 |
Our structure learning algorithm runs efficiently on a standard desktop CPU, while providing structures with competitive classification accuracies and network sizes. First, we compare our method to the NAS algorithm {{cite:9395955fdc88672c95e0b1ac09996a04737aae7c}}. NAS achieves for CIFAR-10 an error rate of 5.5% with ... | m | f26d160b86a5e2bec5fd9af3d4a60032 |
We compare our method to the state-of-the-art model-based RL {{cite:968fc361f3d9bcc0ac5320be713fd10e05232f90}}, {{cite:aa1134106de1c76ece6bf999840ba91cbc5fa223}}, skill-based RL {{cite:88dbfaaee604e3e8a5f248697b96835ef8eb01a4}}, and combinations of them, as well as three ablations of our method, as summarized in Append... | m | b6562fa84c7cddd7ad014ca351e2645d |
Previous work has shown that random seed is crucial in the performance of fine-tuning {{cite:2412f7a634b0ec0c7cff43da77e2cad75e10baf5}}. Fine-tuning also benefits from ensembling or selecting a few of the best performing seeds {{cite:8232e1ea9c70ef3e348446af8c7f9cd0c8f53d7f}}.
It would be interesting to study HPO's per... | d | c5f6850acdee1e81800a8692e2ee5ba1 |
All our calculations were performed within the DFT scheme, using the projector-augmented plane wave method (PAW) {{cite:e01f85c648b3d851f5cecf5b447279ce9edd4dfa}} as implemented in the VASP (version 5.4.4) code {{cite:87f6fe3747bb6c5c0ab5ff3321a49b976e2db760}}, {{cite:cb814a0a60b761b220164bd29d3d8b650ddd5b02}}. The val... | m | d57e54333bcfe7bd0d28c2f01726a29f |
Miyato et al. {{cite:6630bedd8364cd1ec04dfc137701d5bc12d56ec5}} incorporated virtual adversarial training (VAT) in semi-supervised contexts to smooth the output distributions as a regularization of deep networks. Later, virtual adversarial domain adaptation (VADA) improved adversarial feature adaptation using VAT. It g... | m | 76a8cc8957614c9a2909656d33d6946a |
The classical model of communication complexity was introduced by Yao {{cite:b14bed259d7fbfdc61d563ce1da199a48b411190}}, who also subsequently introduced its quantum analogue {{cite:1388219b2821400b2c9ca582393c5ea33fd5908d}}.
Communication complexity has important applications in several disciplines, in particular for ... | r | fad9ad4e85007cf8bae4448b9f8b425b |
In the bottom left panel, we show the rate of change of configurational entropy ({{formula:57831098-e319-4b45-884c-23c1803767d5}} ) where we find the minima transpire at a larger scale factor as {{formula:57c353d1-d21f-45ff-b146-2cd5c2bbb327}} decreases and {{formula:d2f198b8-38e6-4110-a0d3-b8ea9793c269}} increases. ... | r | 31e8b1f4e3f5097a3af5b142d5752c82 |
Data and Models.
We use the publicly available Breast Cancer Wisconsin (Diagnostic) dataset {{cite:0b60030b3ec13a49a9947a7d5b62b01ff69aae52}}.
It contains 30 numeric features which we standardized by removing the mean and scaling to unit variance. We explain Scikit-learn {{cite:a584617c269d0dc82bc686572c2f674dd8959a04}... | m | d7ccae2be13f0bad6baafee7461c9c77 |
The QCD light-cone sum rule for the magnetic moment of the {{formula:42ed095f-d1c2-4e36-b547-94385d836d72}} states contains many input parameters whose numerical values we need.
The values of these parameters we use in the analysis are given as follows: {{formula:c5b7d5fa-0623-4c11-a929-0fd89b564c90}} , {{formula:e11f... | d | f19a6ded13ad55ef99ff4f8fdf01455a |
The act of fuel cycle benchmarking has long faced methodological issues
as per what metrics to compare, how to compare them, and at what point in the
fuel cycle they should be compared
{{cite:484c4accbcb455a84af6c17d836125a59c3e6d65}}, {{cite:5b18ab0832e2cf3c6b27d8f662eb1593ec92d444}}, {{cite:1536059c48a8f3c66d0a28d06c... | i | 8e78afaf1988c877aa8403fa04e450a5 |
The systems level approach taken here means that no individual component stands in isolation.
Rather, the properties of each component are shaped by the system architecture and operation. In our case this lead us to make important assumptions regarding the features the individual components provide: disentanglement and... | d | 42d48133c15bd9cb49194cf1edfd0f9b |
One of the hallmarks of language is flexibility, which contains two aspects. The first requires that the generated language should be a variable-length sequence, but many previous works {{cite:3b5e518340d206fb70ee476ce39969ec8f5bcb16}}, {{cite:13ee73de26102c61601b8b2fc0cbd61e0daebb04}}, {{cite:51eefb9f5e3cd2a935e53f945... | r | 1555d0ff77c913808083eef802f71ef9 |
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