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The uniqueness of such positive solutions was subsequently proved by Chen, Li, and Ou in {{cite:47dc9ddeaed6a1be8c6e9c2ae2d7c503dc69b3b1}}. A different way of looking at (REF ), which also underscores its geometric invariance, goes back to the ideas in {{cite:1f48512d1a283215e1acff18dea797fc16156498}}, {{cite:bdce89de0... | i | d4aa3fa63aeae404420d1a39e9deb34e |
Here {{formula:5ff6d75f-6b15-4d22-87e0-fa31ba7a5ab8}} is the angle between the bond vectors
{{formula:5378242a-8e7f-40fb-a300-cf05caad9b8e}} and
{{formula:8a60c8aa-0d33-4628-aeeb-03d0e20c2d41}} , respectively, as shown in Fig. REF . The strength
of the interaction is characterized by the bending rigidity {{formula:1b... | m | a163181dfa1d212ebf210e383a559815 |
For temporal clues, most existing methods only model either short-term {{cite:52f79bb599be56d37c44a43175ed1a57527d0cec}}, {{cite:7069c71674fa6e64ed8ede3027d64b857934c231}}, {{cite:32be6cb1b4cd34e3c7abec0072ba5af10d6740c9}} or long-term temporal relations {{cite:016d0bb32a07b683127898968403af3be92f7127}}, {{cite:35f0906... | i | 99e4141e783e5d397a1aca05f557d5e5 |
The outline of this paper is as follows. Section describes the conditional linear factor models with sparse time-varying coefficients, and how to implement the no-arbitrage restrictions in the specification of the random coefficient panel model. Section develops
our penalized two-pass regression with time-varying fac... | i | b908368b8c95777897c7d25a58bcaf5c |
All these algorithm are implemented in existing computer algebra systems, like for instance Magma {{cite:046e26b86e16d4e1a47014c4ef6b2db993ee05d5}} (see also {{cite:c31162abac1974e8fddc4028cbf5dbb53df33d71}}).
| i | 339c75cb04381bd56ba639d4f4158d57 |
As shown in the table, our method can make a trade-off between speed and accuracy. Although our MSCVNet is little less accurate than top-performance methods, such as GCNet{{cite:c724e24dbb5016419ce613c276d6487cb5e82016}}, PSMNet{{cite:b45bc90f8bd643da82c7a5a47185ee8213f1ab9d}}, GANet{{cite:02a7336b1d548c73673b324890496... | r | b80143c8a95efe7b2065a18a1d994280 |
Hyper-reduction refers to a broad class of methods that aims to reduce the online assembling cost for projection-based ROMs; hyper-reduction techniques might be applied either at the
continuous or at the discrete level — continuous-based and discrete-based hyper-reduction. Continuous-based
hyper-reduction methods (e.g.... | m | 1fdeb7ff25bc50d51ce24aed282da9c1 |
There are several limitations to our analysis. On the one hand, based on current evidence, we use biological parameters that characterize the SARS-CoV-2 virus that are not fully confirmed in practice. For instance, we have explored the unknown susceptibility rate (relapse probability) and found that its effects on our ... | d | d656e57ffd7d4a938d5c242dff38c18f |
where {{formula:d9df2827-017c-4d74-9f72-36f349e63c85}} denotes the set of safe policies, and {{formula:52cc56dc-6ccb-4284-a9d1-7875d896a758}} in some sense measures the “sub-optimality” of the policy class {{formula:04706e02-a7fe-4f8f-97e4-838f484df4cf}} . In case a fixed sub-optimality gap {{formula:49255ea7-3670-42... | r | a4d36ab50641b1d339bf650d5ded196e |
The limits derived in this work are compatible with other limits and sensitivities of future ALPs experiments, as shown in Fig. REF . Together with the SN 1987A {{formula:05566539-8ae2-4614-89f9-ebf17ccf87fe}} burst experiment {{cite:7eddaebb93e500e211f51df467b0025d369b3eee}} and with previous Fermi-LAT {{cite:e34c8d7... | r | 3a34a9f5d59c81e364eb50e7cd921c79 |
The HE gamma-ray light curves with a weekly binning obtained from the analysis of Fermi-LAT data are shown in
Fig. REF . Flux points are displayed only if the significance of the source is above a TS
value of 10 (corresponding to a {{formula:4d902531-29f4-43aa-9c18-e865521ae8a2}} detection), otherwise a 2{{formula:dd2... | r | 5aacdd44ccc14aa6c039aa6ce2873720 |
Projections of IPCC AR4 and AR5 and regional climate models
{{cite:01f253e45be73aa5b33ebe3f99397997476dcbd7}}, {{cite:e25e8126d14bf588711256f8fa726fdd2425e9d7}}, {{cite:5b6214acc42908a2df352f05a00d6493ab667b13}} suggest that the eastern Amazon
may become drier in the future, and that this drying could be exacerbated by... | d | f0c5268114f451f1c5dec506ec2cc4ff |
In the last section, we restrict our discussion to the case of HYM connections over general complex manifolds. If we assume {{formula:60f4870b-1782-443c-aa38-af1c5775ac75}} is the limit of a sequence of
Hermitian-Yang-Mills connections over a compact Hermitian manifold, then by using
the argument in {{cite:6454c0dfd32... | r | 7a10463af581e2ad66cdf083c03aa49b |
One limitation of our study is that it considered only planar objects. We expect our approach will extend to 3D objects, like recent work that has successfully implemented tactile servoing to slide over complex 3D surfaces (3 pose components) and edges (5 pose components) {{cite:078c181f53f76f3dc492a69f4c2b0998db83d1d1... | d | 6b4be5b55faa5d2487180350e24d3119 |
The blockade radius for an ultra-cold atomic sample is defined as {{formula:e939faa4-e382-4275-a895-67c08dc041ae}} , where {{formula:aedc96dd-1f3a-46ee-ab2e-5067753d4aaa}}
is the effective Rabi frequency of the Rydberg excitation and {{formula:7b2224e2-c76f-48ed-936a-ef7ac302ec93}} is the strength of the van der Waal... | i | 87c7bfa14e6a34e1593cc606d4b14dd0 |
After considering these possible avenues for improving BENDR, we still do not fully discount the validity of some of the transfer learning paths we appear to exclude above in our introduction. We will reconsider these paths in future work. Particularly, given the success we had in crossing boundaries of hardware in thi... | d | 14d68c9387e259c96a91728866b80e29 |
Our two contributions for GLL follow the same simple idea. We leverage the GLL structure, namely the fact that these losses are effectively “one-dimensional,” to make a fast approximation of the Moreau envelope of the loss {{cite:b44dcd8a226fcc2665e25da0c5d8348fb60fa2b9}}. We can then exploit the smoothness of the enve... | r | 4e9af0ffcdb657909841fab531db0bc3 |
In this appendix we introduce several classes of heavy-tailed distributions that are considered for the job size in this paper, see also {{cite:598d2bbb4af72cf3f29e21dc9dff5881fa1ffac4}}, {{cite:e4985621594e55c466adc6bee13b247f1ac7af8c}}.
Let the complementary cumulative distribution function be defined as {{formula:8a... | r | 0ccc227853d277639f0c92e47471d5fc |
Here {{formula:4d64a58f-57d5-421b-af2c-63c228a5e62e}} is the density of mass,
{{formula:8306ef0f-9a81-4b83-a400-76153f0e9260}} is the current,
and {{formula:5fea1a7c-f4f8-48d3-9563-5c7e1dab0295}} is the constant diffusivity coefficient. Equations
(REF ) and (REF ) can be obtained
as the hydrodynamical limit of diffu... | r | 66663e5951e423f085bd62ff35f5fbb1 |
SA-RNN beamformer vs. Conv-TasNet: The MIMO Conv-TasNet (i) with a fixed STFT encoder {{cite:fb02e8de344ad43840bba59f4566d6a5ca403ef7}} is a variant of the original Conv-TasNet {{cite:a39fb7dee9f9f59ed69e9e6dca78bbe5a15da53b}}. It is the cRF estimator without the beamforming module as shown in Fig. REF . The input to t... | r | d59265f8eef9414a98abcad4b974171c |
{{cite:21d359380f029646bd43f08ede09b03e966d7dc3}} considered a special case of DG in which the target domain is a convex combination of the target domains. However, the number of target domains that this technique can cover is limited if the convex region of the training domains is small to begin with.
Extending on thi... | m | e3019151cc0506e5f6cb96b343e8d725 |
One of our goals is to explore the nature of nonlocality in open systems, in hopes that this can shed light on whether nonlocality can also arise near horizons for black holes. Nonlocality in this context traditionally means the extent to which the effective action (or Hamiltonian) is not simply the integral of a Hamil... | m | 20af1a4dd78ccf4a4c3eb15e833933ea |
Hyper-parameters settings. For all simulations, the learning rate in Eqn. (REF ) is set as {{formula:f8cda1de-81be-4795-bf5c-a15efc74ccfc}} . The implementation of the quantum encoder employs the hardware-efficient ansatz {{cite:43292d0d2db30313181703ee74ff5bab3dc98111}}, {{cite:fd4750272954f243f97f1047e6049d2aaf47d974... | r | d51a03c9f25aaf59f772414667c4449e |
We construct an ensemble of EoSs based on the piecewise-linear speed-of-sound parametrization introduced in {{cite:aa8bb512c022f956d17942b7248b644afd21523f}}. The model has been already used in several other works {{cite:d3d465affeceaadde3cefa6cbe63ca083010cc95}}, {{cite:515c82b41dd3833c4623a11b8dece9077efe51eb}}, {{ci... | m | 9a7260f1e2116cb6b18046deb5c7cd01 |
Artificial neural networks often give good results, but it is difficult to
understand what they learned, or on which basis they generate their output. In
the following, we will try to dissect the proposed model, understand its
workings, and see what it pays attention to. To this end, we compute
saliency maps using guid... | r | ffe14adbca72c44bca6572fcf1488c76 |
In order to estimate a standard error of the mean for the number of remembered items across participants, for each list length, we performed a bootstrap procedure ({{cite:12222992f843c5ff6e43524856844f6177a1b249}}). We generated multiple bootstrap samples by randomly sampling a list of N participants with replacement N... | r | 3cda99896c246166fa09f58efe9ec4f7 |
In this work we focused on NAS for medical image segmentation. Due to computational cost reasons, we used a 2D segmentation paradigm and quite compact architectures. However, it was shown {{cite:c86f5831a12b01bc779299174aa70c8e505585d4}}, that using a 3D segmentation approach (i.e., train on 3D volumetric patches inste... | d | a60163dee71b7a27c3cafa77d11a0b10 |
This was confirmed for {{formula:82bc2968-fc26-4df5-9f0f-fe5a7e7b75ab}} by Singer himself {{cite:5903bda55c6391d79fd614648c33e85e0f5083bc}} and Boardman {{cite:52babac0648788f7544a39f218bc76d81a1618e5}}. Our recent work {{cite:eb27426a734873ad328d92c31a5f4384c6191262}} shows that the conjecture is also true for {{form... | r | b8191114f76339ba915dccb11fcd9b30 |
Quantum computing and machine learning represent two of the most significant fields of computational science to have emerged over the last half century. Over the last decade, attention has in particular turned to the ethical implications of machine learning technology, resulting in the emergence of a burgeoning multidi... | i | 163839b129952a71af87a19d0794cdf5 |
paragraph41.5ex plus1ex minus.2ex-1emCase 3. Next, in Figures REF and REF , we present spectral properties for Case 3 in Table REF , where a spike detaches from the bulk after large-step-size training. Notice that Figures REF (b) and REF (a) imply that the bulk spectra for weight and CK remain unchanged over training ... | r | 26d4d37a74c6cf69d94aaa504c18fcc8 |
where {{formula:e09f0a6c-691b-45be-b126-8dde5813687b}} and {{formula:b4264c16-fbdb-4ccd-a10f-c3f8d7593f85}} are BCPTP channels, and {{formula:fdf20d9f-e17c-44f4-86d3-18addd66956e}} and {{formula:5b47eaef-3a38-4dca-a044-6b1946b9bd83}} are probability distributions. Eq.(REF ) implies a universal circuit with two-dept... | r | 2cbcdadebd6879d5145e13f0052bdae2 |
Referring Expression (RE) is a widely studied cross-modal task in both computer vision and natural language processing fields as a vision and language task. In a RE task, the agent needs to localize a specific target object in the image in response to a given natural language referring expression. Most of current studi... | i | ef08cc64f96dc28b0089208627f9421d |
For our numerical calculations, we discretize the Hamiltonian to a tight-binding model on a square lattice of spacing {{formula:05368fb8-eb07-485b-8c41-cd44afc606fa}} nm. Simulations are performed with the following parameters: {{formula:af56039c-a966-44e9-9a42-cdb0c58e7fba}} , {{formula:59dd77b3-1521-4532-8298-6aceaf... | m | bce9a348b9626ed58f3b7370eb6b90c6 |
In the light of our results, there are several directions to pursue. Although the extra part in the single copy equation (REF ) was shown to vanish for all the examples in the literature, a general proof or, at least, the conditions under which it is true are still lacking. The resolution of this might lead to a better... | d | 7ec80c14a566145b14124015d88104ad |
Figure REF (right) shows the temperature {{formula:03824a63-8917-49f3-963b-79f24c54445f}} and density {{formula:406f62fc-a01b-427b-8791-efb5c1f7f299}} as a function of radius {{formula:e9c1b1ad-055d-44cf-b416-6884b32d3591}} predicted by our maihem simulation for an adiabatic example. It has four different regions d... | r | 5f692517189326c52bee640590316e9f |
Here {{formula:b9f33a14-2af8-48e7-b0e5-78090ef48c64}} depends on {{formula:83c8f577-3267-49b8-b3c9-eeb4ad1e805e}} , historical search direction, and {{formula:a882bee0-f838-4574-bed4-aa230a36e702}} is a diagonal matrix.
The diagonal form of {{formula:495d10db-521e-4d23-8cd4-75ea7fbd3533}} allows for different effect... | m | 5483fa96aff5369c7dd579de466c94f5 |
It also comes with a collection of pre-trained models whose performance has already been reported throughout the paper in Table REF for voice activity detection, Table REF for speaker change detection, Table REF for overlapped speech detection, and Table REF for speaker embedding. While speaker embeddings were trai... | r | ba479dad39f370da62b594ddcf8d5cce |
Although there exists an extensive literature on these kinds of image-to-image translations {{cite:a33725aa1341cec1aa99afa021f1dfd52178657f}}, {{cite:17aa2f70f1b87ba5564506787fbec09c20d114b2}}, {{cite:1dee070415a23149be59cf53e8ca1f5b758ae2e6}}, recent works also focus on the multi-modal domain translation such as synth... | i | f524e91e3c8fa0b8beef444e1141f5ca |
Table REF shows the recognition rates for
multiview DAIN that outperforms three other multi-view classification method:
FV+CNN{{cite:d64b949980559c4df50f8c86a785e84a342a818b}}, FV-N+CNN+N3D {{cite:ea6ab6f71fcfbe7b9193aa931cc7293c75857e0d}}, and MVCNN{{cite:1c3d9039226d7a3ada044f1685f5be8bab9b2ef4}}.
The table shows re... | r | 0135318dbfc879631c17bcbdae9264f1 |
Due to the relevance of the topic for applications, it would be important to understand stability properties of such inequalities.
Isoperimetric inequalities in quantitative form have a long history, see {{cite:d16d16de7c8da0b8261bc2cd080f7b576ceac81a}}, {{cite:7744c174386c0d512cdf9dbfcb535b45972fdc56}}, {{cite:129b955... | i | c8bc73cbba3a4b53511953a852f7033f |
It is well known since the works of I. Newton {{cite:1c2383ce954f8762a3f531ef6284d9ccc215064e}} and L. Kantorovich {{cite:56f0f7c5f82403a3c1a7c00c2e2f69fedb20c1eb}} that the second-order derivative of the objective function can be used in numerical algorithms for solving optimization problems and nonlinear equations an... | i | a4cc8c861b59eae1048405fcb43af650 |
To the best of our knowledge, this is the first instance of non-supersymmetric but perturbatively stable AdS{{formula:dbb942ec-3889-46ff-b6af-0eb5b53e155b}} vacua continuously connected to supersymmetric solutions. This makes them very interesting cases of study in the context of the AdS swampland conjecture {{cite:36... | d | 1fd0b4a429028605fd5179802fbb6464 |
We proposed a generalized CNN+LSTM model that incorporates an environment-specific LSTM cell for potentially enabling learning across different environments. This apparently simple yet more general architectural change, compared to a baseline that uses a single-cell LSTM, was found to be even more accurate and robust, ... | d | f89c74ab0070dcfb8671007ba3976856 |
Apoptosis is an attractive model system to study fate decisions, with a neatly defined outcome and a solid background understanding of its regulatory network {{cite:24262078443119b0cf154df2d8afafb303b7ed60}}, {{cite:afff2ee2ff3de0147f366f9283c4db8a6fcdae95}}.
However, some key questions remain open {{cite:3b2803b2ab001... | d | 8fc5adc5a3a7db9af0ae173c2a5e8e83 |
We next focus on the problem of Robust Principal Component Analysis (PCA) in Section . Though this problem is not of the form (REF ), we will see that flat solutions (appropriately defined) exactly recover the ground truth under reasonable assumptions. Specifically, following {{cite:e694f8449072550bcd7d64b4becd8f251981... | r | 0d8e2bb675719e57eb6bec456f11e8eb |
A classic projection method with BDF2 time discretization is used to solve the velocity and pressure. Denote the numerical solution at time {{formula:82f0cf43-4d09-4241-99fc-358eee16a188}} as {{formula:68831ed3-edc8-42cb-8b0e-3ff8b6262e38}} . To obtain {{formula:36c42f3a-267a-4ac2-b0bb-feace7d8e03c}} , the so-called “... | m | 70d909b2c8f756d93650ce128361686b |
In Figs. REF a and REF b we compare
the model predictions for {{formula:8428cad2-34ee-44af-86ba-a4a499516fd5}} to the recent JAM'20 parametrization
{{cite:d24a2348e487d318a912a691eecdb0c1e1eda14e}}. The CPM describes the sign and magnitude of the
transversity quark distributions well. For larger {{formula:78006313-12... | r | 25bf2c283e1ca8b2c9d1a607a458ee26 |
As discussed in Section REF , traditional forecasting-based anomaly detection methods are primarily based on auto-regression-based models such as AutoRegressive Integrated Moving Average (ARIMA) {{cite:4ced2419a7243801b78aed54e61194db5c3b0359}}. With the recent advances in deep learning, LSTM has been used to replace a... | m | f254ebdecdcb7f84aa421bc9840b6cfc |
The masses and widths of the isovector-scalar resonances {{formula:149cd817-8ed6-4c86-aa18-fcf397a15fd9}} and {{formula:2d60d8da-af74-4230-8d03-d1cc150bac34}} are presented in
Table REF . They have been fixed during the minimization of the {{formula:b5a8212b-6aca-4f6b-8897-a836fc119925}} function. The parameters of ... | r | e0205d0e7c2718fdc10cc1e27287a4e5 |
possibly paired with a regularization/physics-enforcing term {{cite:f818bb1a55b4910e8ff400014ae7c99987609ac8}}, {{cite:244ccbb4931a5d5ecd18eaac5ff4df8de838036e}}. Regularization aside, the differences among objective functions typically come from how the estimated output {{formula:3465a15b-f753-42d8-b4e0-148323057c77}}... | d | b0634e772469d0769503ac8139ae3b04 |
Both methods employ an encoder-decoder network {{formula:e91a6706-036a-402c-b6e0-e1f3b5f047c6}} that maps the input image {{formula:dae9dd36-ddb1-48ca-b008-17bfbbe07273}} to pixel-wise weight mask {{formula:38350ee6-764f-4151-ad48-da8a1b9f9d00}} . Such networks are typically used in supervision segmentation {{cite:d1... | m | 298ae287488b82e87abf61420b86e8af |
First assume {{formula:9aace4cb-5138-4174-9a6b-fcd375d3ec24}} being holomorphic. Then {{formula:63e23aee-c00c-4db5-990c-a3b51d8b8d93}} must be zero, because otherwise {{formula:992d7540-81ef-4438-83cc-e6dc8d6777f7}} . If {{formula:a8886837-9718-43cd-9911-419672702281}} vanishes identically in {{formula:d340768a-9a24... | r | cb86d1bd5d375e8cb7488804f841bcf9 |
The goal of our algorithm is to take a series of {{formula:904bc4d9-49b3-4782-8942-a8b431b62189}} synchronized stereo image pairs {{formula:fc97195b-97ab-41e6-b98a-3eece977a4c5}} , thermal images {{formula:0db95152-464e-468b-8227-42a38d12021d}} , and laser point clouds {{formula:387dc1d9-b8af-4034-ada2-18dba90005c7}} ... | m | c900d89c4b6d06806344d27b2d1d09d1 |
Answering Q1 to Q3 is trivial by parsing original and augmented sentences using a pre-trained dependency parser and calculating descriptive statistics. To answer Q4 and Q5, we propose to calculate NCPTK for each pair of trees in an augmented batch. To perform the calculations, we transform each dependency tree to GRCT ... | m | dea07892f29abc56f2fa28d23b198e8a |
In particular, it has been suggested recently that the soft limits of the {{formula:5040dffa-b1a6-437c-9a6b-dec749a78722}} -point correlators can be the discovery channels for heavy particles and new interactions with masses up to {{formula:b0987e68-3025-4e95-ad65-264c3a0999c1}} . These studies are based on earlier wor... | i | 9e5084a137b357cc883b4ba2f1761987 |
We propose an alternative utility model based on facility location optimization methods {{cite:ecd4fe70c7593a3c64b0d94dc59f3c729d708a4b}}. In the facility locations problem a utility can be constructed that uses a greedy algorithm to minimize the cost, or maximize the reward, of building a series of new facilities in a... | m | 83934eddbcc0b336d58cfc5a70548223 |
We evaluate our method on the test dataset derived from the KITTI dataset, and compare it to state-of-the-art video frame interpolation methods quantitatively. The same approaches for 3D-2D projection and 2D-3D reconstruction using Equation (REF ), are applied into the video frame interpolation methods under comparison... | r | ccc185315d0db84d3f6b150c5dad6607 |
In this paper we constructed and studied a three-dimensional model
that exhibits global continuous symmetry breaking at arbitrarily large temperatures.
To our knowledge this is the first example of a UV complete unitary 3d model exhibiting persistent breaking of a continuous global symmetry. It bypasses the Coleman-Hoh... | d | 8847d3b7ab659ed260fea800401e61a2 |
We consider a uniform linear array (ULA) and a uniform planar array (UPA) for the configuration of the BS and IRS, respectively. In particular, we have {{formula:3f2f0f7c-b7fb-4b99-a870-06f728933f11}} while {{formula:27d6dea9-dfbd-462b-bf01-adfa8fddda2b}} , {{formula:214b3f16-5f07-499e-acdc-60169bc9bb5b}} are uniform... | r | 4b6598784b640da39907ed95f03c2d1e |
We used the DCASE 2020 SELD dataset {{cite:ca78da32ae36baff79464d91463244cc2d5aaf8f}} for our experiments. This dataset provides both FOA and mic-array format with 4 microphones. The dataset consists of 400, 100, and 100 one-minute audio clips for training, validation, and testing, respectively. There are 14 sound clas... | r | 1f428be11ee413d8b2f5925c41bec625 |
Multi-scale training. We summarize the multi-scale training as PyTorch pseudo-code in Alg. . The training scales are randomly selected from 25 patterns, whose height is ranged from 512 to 1024, while width is ranged from 640 to 1280. We also randomly crop images from [0.83,1.0] of the original scale. The global batch s... | d | d4a14f86fa14aabc954fe9b8fe40168a |
Since the last hundred years, there have been many attempts to modify or test GR {{cite:bee31a404760e80ecc57bc71074d1549d516b0db}}, {{cite:d5c74ad3673d818c5178c16aae6870c3d8b578ed}}, {{cite:f96bdc942c65116a838da41022cac5a1c87ef40b}}, {{cite:8898f95c04f55aca71028dcbcc21fa3b2800618b}}, {{cite:c60a9561b81fe895386f9b7d7b2c... | i | 85c4e4afb147b5eeb07de4ac0a42e767 |
Equation (REF ) defines the quantum gravity state over the whole superspace of metric configurations on {{formula:77919da6-5b49-4386-aed1-5a0d830978f9}} via the deformed partition function defined in (REF ). However, the fact that the deforming operator (REF ) is irrelevant means that this theory has a cutoff beyond w... | d | 232e7b51c1cdef66cc989ccb163d1748 |
With the rapid growth of deep learning, handcrafted features and predefined filter banks in the BoW paradigm were replaced by the `deep features' and have achieved state of-the-art results {{cite:20f58d0a6a71b65f1d507621b4900aad37476433}}, {{cite:2a939667502641b95b68f97228eb90ed5f78e761}}. The main challenge faced by t... | i | 9f5a8b327be3a7bc8ce150b4e672d360 |
For visualization purposes, we consider a simplified example with {{formula:c750021e-8368-4e43-b317-bd8a635e5830}} nodes, as shown in Figure REF and demonstrate the difference between the regularity functions. Figure REF shows the contour plots. Under a lasso regularization ({{formula:6278078e-27f0-4efa-9091-781ce77... | m | 689b040478cd560336abae1096fbdbb5 |
CASA {{cite:7d897f88c3ba0f44df98c416e46199c2a935ba74}},
Astropy {{cite:ee247b82f0013ab8b51e7d19688bb9399a307170}},
SunPy {{cite:88263d681889b9ddbc923a2e4cf88708937e26b3}}.
| d | 4808afd70768d0f84275536614345eee |
In this note we introduce a category of finite strings and establish
some connections. First of all, we notice that the category of
connected strings is equivalent to the augmented simplex category
{{formula:b1627b18-096a-460b-b304-0537a10a1840}} (cf. {{cite:6bcd398cb102587e9a364d2b1b65a853d3e9eb16}}, {{cite:2fd2a5c87... | i | 7d93a8c0a35eed4df32626341365a2db |
Furthermore, {{formula:89e1a61b-083f-43a1-a7f8-4abceed5b036}} might itself be unknown. If so, {{formula:3d04bc1e-e6cb-4194-87a7-a453738cc802}} can be calculated simultaneously with {{formula:9e0ffd19-c3c9-4901-aee5-610723b0deb3}} using value approximation techniques like MC policy rollout, TD({{formula:82730946-f2d4... | m | e962c0c8fb3f1067d62552fd280f6144 |
Besides, the aforementioned combination of {{formula:b515c159-1460-47b0-bff4-c0d7cbc4a128}} plays a key role for the hadronic contribution. From our calculations, a steeper proton spectrum would require a higher density within the PWN. As for photon energy {{formula:4a776f39-a4b8-4457-93fd-2c7938878d9b}} TeV, all thr... | d | a9fc6210c5b86f7f7fe0733f1b3cd89e |
where {{formula:451e2b47-a230-48c5-90a0-6ebde6f97372}} is the probability of the system in configuration {{formula:02a059df-1969-450e-87cf-0d1b916fcd9e}}
and {{formula:65521d7d-34d2-4260-bdc2-f89f79f45b23}} is the probability to perturb the configuration
from state {{formula:eb4ef810-a1c8-44fe-bba8-e67126d0af6d}} t... | m | 99e53f82727d3b2897f7b45881d774e6 |
To illustrate how the derived expressions can be used, we take the example of an isotropic Heisenberg {{formula:d79f87f0-c361-4402-9f67-45f811af8b00}} model submitted to an external magnetic field. Here, we estimated the temperature and the magnetic field of this quantum Heisenberg model in which the Hamiltonian descr... | m | 0f27223005c12533568fd36840147d0c |
Definition with {{formula:97363084-c238-4476-a6f8-8c7b287d7c3a}} polynomials.
The classical Hermite polynomials are defined {{cite:544415e7fbf81af2ceafecc7cfa9a70027e06200}} by putting {{formula:dfe33732-5b18-4db5-be8a-f7f211ff8e12}} , {{formula:4b8226ab-24d4-4d25-b04c-67f50d84e9bb}} and subsequently
{{formula:210aff... | r | 8616b20beed2acac1a236028357e6465 |
In this work, we further conduct data-free training on semantic segmentation tasks to show the effectiveness of our method. In segmentation, we only use the feature regularization loss and adversarial loss of REF for data synthesis. The mIoU of the student model, as well as the data amount and synthesis time, are repo... | r | bdfcc10113d5c5a031fd47f131976853 |
Respiratory motion during Magnetic Resonance Imaging (MRI) has been a long standing problem that leads to considerable reductions in image quality {{cite:547ec3b22cdfc8a32ca4fc21e7ed26e86602bcfd}}. The solutions traditionally proposed in the radiology workflow are breathhold or respiratory triggered scans, which are ef... | i | fed85e78efb47a9fbab83527fd86df62 |
An important aspect of this connection has been the role that quantum error correction plays in the encoding of the bulk in the boundary {{cite:e8bb20f036fea7460c962e3ac53c1bb6e7676d3e}}, {{cite:c5a802b955b64a02cb1ddce4aee901601dce6a05}}, {{cite:54f30288dfce1fec5773d0e772fb0acb1a1ab490}}, {{cite:45a6636d226085663b5cc90... | i | 124656176d38f533a6aec2fe9853eeb2 |
Figure REF shows the increase in quality of community finding (SN-modularity) over iterations of the SNIC algorithm. Recall that the SNIC algorithm decreases the distance constraint at each iteration. As the geographic constraint decreases, such that community proximity becomes more important, the quality of communit... | r | b4ce84859174b95774e7032e9896a9c4 |
Figure REF shows the quantitative results in LDP mode and RA mode. Without the loss of generality, we take Figure REF (a) as example.
We choose VVenC as our baseline, and test its performance from QP=35 to QP=62.
Higher QP value means higher compression ratio.
As mentioned above, our method can implement dynamically-a... | r | 491bc94c617cbb49119b97ac4edb19b1 |
SEMPRE {{cite:4a34ba99abd5ceccd17d76aa9262feec9eea269b}} constructs queries that can be executed over the KG using semantic parsing of the input questions. Using a lexicon to map question surface terms to KB entities or relations and a set of composition rules, it recursively constructs more and more complex formal que... | m | 43e1ae2af52abf17f84e8c388d8ede73 |
We used the Gaussian mixture model{{cite:53105e83130a3d591497ed7a959fac831919cf39}} for clustering, where the goal is to find a set of K-normal distributions of mean {{formula:22371661-a326-4c53-a427-2ed7fc7d33b1}} and covariance {{formula:f3e456de-9e40-438f-9985-d67bbda31660}} (where k=1 to K) to best describe the o... | m | 39d3511c9d8fbf1e04c98faf99b2bbc7 |
Most DL methods for trajectory prediction do not uncover the underlying reward function, instead, they only rely on previously seen examples, which hinders generalizability and limits their scope. In {{cite:81f3b28c38cfd213be43c1b794581b58034a1469}}, inverse reinforcement learning is used to find the reward function so... | m | 31bc221521df8534831f6581651c75ea |
Due to our investigation being innovative, we are unable to compare the obtained results for different values of the fractional parameter. As a consequence, we recover the classical solutions of the DFP and its shifted by setting {{formula:c10fa3c5-5495-48af-bb45-04edcf34038e}} and comparing our results to those found... | r | 8c6a9382e8847d4990de4852edc82d57 |
Figure REF provides the block diagram of the proposed methodology. The proposed framework for explainable event recognition is composed of two main components. The first component is consisted of fine-tuning a pre-trained CNN model while the other component is based on the Grad-CAM, which generates the activation maps... | m | deade7f491d174d809fba4c118fab521 |
The supervised baseline we consider refers to the ResNet50 architecture trained with cross-entropy loss and full access to labels using the same set of basic data augmentations for 1000 epochs as proposed by {{cite:91198b7f71ce6165aa85c0c9596406e915641c89}} and used throughout the representation learning literature (c.... | r | b32fd494a1e7f121cdbd4b4fe42c125b |
is always tight in the Gromov–Hausdorff–Prokhorov topology, whatever {{formula:523f3027-66ea-4f29-a5fd-fbd617b394a2}} and {{formula:1453ab8b-d7ab-4493-8b78-1b941fc6b09b}} , thus extending results due to Le Gall {{cite:78b915f49e2dc3c186ce70f9f4c855f0dc10201d}} for {{formula:663b1133-3133-4dd7-b713-7ddc79600efe}} -angu... | r | 3a27388d20f89d2b4511ded6c1473fa1 |
In the plethora of opinion diffusion models, threshold-based ones are certainly the best known, cf. {{cite:383756a23248c66907b18888db69de1f4d4d2baf}}, {{cite:c3b3a2b578f359290c3b0e259a6ae8e8dc1f4fb2}}, {{cite:58c8900c9aadec52c04c0f2f3eb32b88c440b877}}, and {{cite:54e5ae19dc28e44949346f0048dea13c70d3273a}}. There, nodes... | i | fe8b43bdfc4810af67636f72176f06c9 |
EL with GCN encoders
Graph convolution networks (GCNs) {{cite:d499a9cc8a86b08d83a83fa286c558d7912fa457}} generalize CNNs to general graphs. A GCN is an encoder where node representations are updated in parallel by transforming messages from adjacent nodes in a graph structure. A message is nothing but a parameterized ... | m | efa909e643e35b6fd5fd235f76e57075 |
Numerous techniques for smartphone-based indoor positioning have been developed, yet there is not a single solution that can guarantee a reliable and universal service {{cite:ee5b815b11d49193900ebba3cb36ec9f474a2401}} on its own. Most techniques exhibit their strengths and weaknesses under different conditions. In comb... | i | 30983eb15358414cda4246c398c3037d |
{{formula:5a1f8aa0-b6c9-4a7f-bcd2-dfad14943bdd}} baryon :
Table REF and Table REF shows our estimated results for natural and unnatural parity states respectively in {{formula:3189486a-e307-45ef-b2b8-a921887c393e}} plane. Our calculated ground state {{formula:f0abada2-e835-4fbc-a49b-f2482b41d9a7}} mass is in good... | d | d5e012727a3302651c7185f1968abfd8 |
CVC-ClinicDB is another commonly used dataset for colonoscopy image analysis. FANet architecture outperforms all the SOTA methods on this dataset by a large margin with F1 of 0.9355, mIoU of 0.8937, recall of 0.9339, and precision of 0.9401 (see Table REF ). FANet achieves the best trade-off between recall and precisio... | r | cf8d5d8ba287bb3d50dc224f5a85daef |
The attention mechanisms fully simulate the observation habits of human eyes, which always concentrate on the most distinctive regions for observing. For example, we can easily pay attention to the head and the wings of a bird and ignore the other common regions to identify its species. Based on this motivation, many m... | i | 9eb4ce8e8f48deb06ee8cf184e23e4ab |
Feature Selection methods used in this work were implemented using “scikit-learn” {{cite:059027e636f54c9cf9cf5acba92c330dfe66fc58}}. For Variance Threshold, features that do not change in more than 80, 85, 90, and 95 percent of the observations are removed. For Chi-squared and ANOVA, features are select according to a ... | m | f17ed945d8a56b1ed96d7d1f2893585b |
where {{formula:a0d8da61-f29b-4f6f-bc60-92c9a2188645}} is the raw pixel-colored monocular input image and {{formula:8ff04cf1-839c-44eb-94e0-53b2e3bcf35b}} is the velocity applied to the UAV.
For Depth-CUPRL, the Equation REF passes through {{formula:ee047736-a474-4c7b-b0bf-8e8130df8aee}} which generates a depth ima... | m | 32c3f7ae527b86f6f4ea55e3fb0d651b |
One must note at this point that though there is a maximum in the
distribution of fluxes near the value {{formula:3c4657ce-5530-42d1-891e-2b7356e7c11b}} , the histogram of
Fig.3 has a finite width. Thus there are bursts with {{formula:fb4810e9-d755-4fe8-8411-cc8f9869cdcc}}
values as large as {{formula:7c00d6cc-9538-4a... | d | 87ea15e2db9e0d818d8ed71c06fe77d9 |
Unless otherwise noted we adopt a set of “standard” parameters for
our model, in which the stellar separation is {{formula:e6ac4c86-e691-432b-ab50-656a92c08c02}} cm
and the viewing angle {{formula:3430f785-2794-4b4c-a398-d063f1a722fc}} {{cite:4192cc2971a3d39f365e7c71e6f441a9a5d8cfce}}. Other parameters of our model a... | r | 68efe31204e1600c78542c16bdf24639 |
The lower bounds of Theorems REF and REF are satisfied with equality by a specific first order method that is an approximate message passing (AMP) algorithm, with Bayes updates. This can be regarded as a version of belief propagation (BP) for densely connected graphs {{cite:2216bb301aa7c5bb9e496d0673f2e087fc02a21a}},... | d | b539df69ef0eb9ee09af58081dadfef5 |
In Ref.{{cite:f1b279f735e8b41a3b616662d860180fc25557ca}}, Chen and Zhu study the hidden-charm tetraquark states with the symbolic quark structure {{formula:a279b9a2-7dbb-4f31-840e-e6d7be025533}} via the QCD sum rules, and obtain the ground state masses {{formula:8ebcdb81-bcb7-4591-a8f1-40d9337c74a7}} for the tetraqua... | r | db5b9d786a45c91d8b3bada2add93ae5 |
Our approach targets at pulling nearest class samples together with calculated prototypes, this is similar to the idea of some clustering based methods. So we use intra-class similarity as an indicator to measure our learned features. We compare our method with two classic clustering-based learning methods, SwAV{{cite:... | m | 78a68f8feb2402aaefdd49be0db8cd6e |
We demonstrate the framework experimentally for the case of localizing a ground vehicle in a reference map, using only cars as semantic objects.
Only stereo camera images from the KITTI dataset {{cite:3a35dd45a43937f30d815b5b5c9f537c58682731}} are used to create the ground vehicle's object map. We consider reference ma... | i | 5e7295ee0eb96e4d271bfb9726857d5e |
We observe that the accuracy of the model drops on the held-out LTL commands. This problem of zero-shot generalization (specifically, the ability to generalize to samples unseen during training) has been widely studied {{cite:6056656cffb34dabef98465ab965314ab3f88e2f}}, {{cite:2b871cafb58bc937e5082057cbc49eb20a56785e}},... | r | 071c3501945f91bffcb3c6965078e609 |
Here VE is the anticipated efficacy and {{formula:3f04aab2-d5ee-4ea7-ab90-5c16e0b7885b}} is the expected difference in VE in absolute terms. We showed, however, that at low prevalence rate, equation REF significantly underestimates the variance. Using an inadequately small variance could lead to underestimation of th... | d | b32be19493e3e62c22a537951f35b470 |
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