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Commonalities and differences between equivariant and invariant learning.
Equivariance is necessary but not sufficient for an effective landmark representation. It also needs to be distinctive or invariant to nuisance factors.
This is enforced in the equivariance objective (Eqn. REF ) as a contrastive term over locatio... | d | 06eeae1d70b6acdc8f25009f745778c5 |
single-cell RNA sequencing data (scRNA-seq).
Moreover, we analyze the quality of our curvature estimation methods in a data sampling scenario, where we sample points in the vicinity of an optimum. All test cases are chosen to assess the efficiency and accuracy of our methods.
We trained CurveNet on {{formula:fa0f59a7-9... | r | 72486267cb5899b1a0a36cc96f6e43ed |
This problem is related the well-studied expansion testing problem {{cite:954f380218c28b74ac9ff5229aad75f62dae45a4}}, {{cite:2b64010014d7f5f9f1d062acd786c53bc67d726f}}, {{cite:50107858058f35f81d013b71be4852ea1e365e50}}, {{cite:4c58782ea6b498eba4c76e5d598e5e8acf3f7424}}, {{cite:cebf75db334de3755a5ec09c081f001964bb9803}}... | r | e3beafb02748a009252feef09a2eb608 |
also called the REINFORCE rule {{cite:fbe15cc9a09b5166ef158c5442719b233ecc7726}}. This update rule has the simple interpretation of increasing the probability of choosing action {{formula:9c4701c2-93cf-44c4-a001-8f945c766909}} in state {{formula:2d630607-9e1e-48ba-b0aa-f240de26307e}} , proportional to the return {{for... | m | c5e799bb459efe370a67f9906f3d5777 |
Adding data from different types of sensors should be beneficial as well, as we have seen a slight performance increase when denoising Canon 500D pictures with models trained on both X-T1 and 500D data (rather than 500D-only), yet there was virtually no performance loss on the X-T1 denoised images when we added 500D im... | d | cc7cedb458dff76abe346832d9769c26 |
In this section we discuss the physical implications of the overabundance of SNRs in the rims of the H i holes. As indicated in Section REF , a remarkable feature in NGC 6946 is the existence and classification of 121 H i holes {{cite:2163b805b6b6584fcb3e0ebe89a931fac36da781}}. Our data indicate an overabundance of SNR... | d | dd8300038e58a5b05ab71c9ebbf4216d |
As an illustration of this phenomenon, consider distributions exhibiting sinusoidal dependence {{cite:55b28ba05d791f7f49ce65e9927e3d6129422a5f}}, {{cite:1c07f7d3ba574cf1b77850de5bf94718a89af214}} with density functions
{{formula:5616577f-c823-470b-80b8-3d7388403615}}
| r | 5d5d31712f6d46d3cdfb738cd8e2cd14 |
Findings from Experiment 1, where the language aspect of the fine-tuning step was evaluated, showed that performance from wav2vec 2.0 based models could be benefit from including speech samples from different languages, even in small amounts. These results align with the findings presented in {{cite:a240d97e8ab3fda0719... | d | 85e6d19a60bfafa508489f21669671e6 |
In this work, we use three widely-used post-hoc and modality-agnostic OOD detection methods. We use maximum of softmax probability (MSP) {{cite:4a50f31212b6892d7bf7d547816ae9302c39a6f2}} and ODIN {{cite:579db86b2a719a1172f4887f4673996683ed6590}} as confidence-based OOD detection baselines, and Mahalanobis detector {{ci... | m | afab7a88bacafc8df27714e57bb3b38a |
Models of the world have proven to be useful for various tasks {{cite:cbaa96fd66cfb46bc34d5111c34abcece1c1949e}}, {{cite:6d1470d666f17aec0f36725056c320932a6ab5cf}}, {{cite:52cafb61ce83df94d76ed493c80b9396f5bb3813}}, including self-driving {{cite:1249393405f0d5d498c93c4cf441d00f24efb9ab}}, {{cite:d7b5495fad872f4d02fe03f... | i | 111c707332bd38ed798c7dd447934a29 |
The fine structure constant {{formula:e00dc86a-bcc5-4ab8-8d6e-3f2edcf2099c}} is at the central position in the system of fundamental physical constants. It measures the strength of the electromagnetic interaction between charged elementary particles in the low-energy limit. Recently, the fine structure constant was de... | i | 00f85a0b36477ad67aaf9859b3182a7a |
Present mainstream contrastive learning methods {{cite:20c21737c9211946ba19280b349c06c2821a25e9}}, {{cite:b1d131b2355f31077e4b0cf0d3a70abdcc1104d3}}, {{cite:8935f2c58809346dde1c2abc52bdd00a9708bde3}}, {{cite:607e49433c81ee2c4580c31ed1d70c83bfdc8133}}, {{cite:c2299e2c499e2b5e420b1c90da28b6f7f793906d}}, {{cite:1df5cff2df... | m | 2802e5f344e0466086f09ea535287b40 |
Although many evolutionary models exist in the literature, here we adopted the evolutionary models from {{cite:138fd7604cf787166be298c0b06a83016ff1ce4f}}, as they are well-suited for lower mass stars and younger ages, reflecting our target sample. The dynamical mass estimates for the binaries with individual masses are... | r | 0c1e13089cbe906bba7e626818253f5b |
We have concentrated here on local- and non-local-in-time tail effects. For the sake of completeness, let us conclude with a few comments regarding non-linear conservative memory terms, which are expected to be indistinguishable from other local-in-time effects and therefore readily incorporated in the B2B dictionary. ... | d | 2629a788da11725bb260b82a442b0915 |
Other examples are the configuration of {{formula:aaef6498-e7c7-4884-8ce4-a8a3e01517ea}} vertices of a regular icosahedron inscribed in {{formula:e91e6be3-c6fa-4ded-a444-fa932407ecfe}} , the kissing configuration of {{formula:158eb5a9-42d9-45a4-b36f-41690fa266ea}} points on {{formula:b0c3f58c-3874-4e6a-ba0f-0dcc7d71e... | r | 26b7c2e5b8bb06b0237e694ad5758ef2 |
Ubiquitously across biology, complex high-dimensional systems interact with their environment through low-dimensional channels.
The computational modelling of such setups has advanced considerably in the past two decades with the emergence of reservoir computing techniques {{cite:050c237aa15e2b6c832916bf8009f7f9b4b8314... | d | ce81405f4c1f2574742dc505de191f94 |
To improve the present theory, we can account for ionic size in electrolyte solutions {{cite:a96a34aa2c00e0a3292f1f9ab23f445df54d091d}}, {{cite:b5e2ce41f71f79c4606a604ca531b34ca9cb8562}}, {{cite:91253414b7ce70ddf2f8183c3b3a08ce9705ca12}}, {{cite:01d06cde28c1c167143e3fdd94a261ccd85ce28e}}. However, since we consider the... | r | e7909f0be8397a53b559fee4297521df |
In this paper, we have presented a comprehensive study of the lottery ticket hypothesis (LTH) for vision and language. Below, we discuss some limitations of the current study.
({{formula:e5aa87a5-29fa-4acb-9d01-a53ea9fcf035}} ) Efficiency: We mainly focused on the scientific study of LTH. For future work, we plan to in... | d | 7032328ff033cacf37b7e8e73c8ae352 |
Our system does have a number of limitations. First, the performance on novel tasks varies significantly. However, even for tasks that are less successful, the robot often exhibits behavior suggesting that it understands at least part of the task, reaching for the right object or performing a semantically related motio... | d | ce1227774b22fd9967ff5cf56ea72a0a |
Perturbation-Based Methods. Different from the above works that require the mathematical details of the model, there are works that treat deep models as black-boxes.
These methods usually localize the discriminative image regions by performing perturbation to the input.
For instance, Fong and Vedaldi {{cite:d5d5e1503... | m | 79f30932f8ef2c3b715e541283104207 |
To investigate the efficacy of the ViT backbone for visual relational reasoning, in particular on systematic generalization, we introduce new systematic splits to canonical benchmarks and compare the ViT backbone with the CNN backbone. Results on GQA show that switching to ViTs in MCAN model {{cite:b2d56538d12e37a32b2e... | i | 6b5b9a24b875a1c19a89110aec444696 |
Recently, a number of existing works such as in {{cite:d40145b21a43c2bec4a99262c9ae699d58783411}}, {{cite:0d0a43d37e8271cb364ced3e45299df80540d12a}}, {{cite:aeee7b76b60de521b2de42614273fbbb46e6f077}}, {{cite:c38b3f0573b9109dc2b26c3467c2980d572de38a}}, {{cite:ee72a58dd00271c213985bbbcbb8fca4ec3782d2}}, {{cite:b2c63cbdfd... | i | 03e02b976de791b3cf64d2671fab4db5 |
The first key feature of the proposed method {{cite:2b5143ec339a158f1e2ba3502e433970178f166e}} is the resonant ALP production via the {{formula:7e0118a0-a757-421e-a380-36d86c49c4cd}} -channel
exchange within the {{formula:b49ccf7a-95c6-4bc2-a611-4ed050fa042a}} uncertainty, which drastically enhances the production rat... | i | 016d125b2935d477abb8ab5c7a7a1678 |
In this paper, we use sparsity based regularization, where the a-priori assumption on the unknown object is sparsity of {{formula:eff754ba-1be6-446d-8dae-5b55062ba3de}} with respect to a frame {{formula:dc0ba17f-360f-47f4-8eaa-9ffdc0823a0d}} of {{formula:3e148a4f-5b79-4b5b-be6f-0c6a9c175ba5}} , cf. {{cite:b70f164bfed... | i | 1c4b4acb282d298cdec0cc0eb0f59e39 |
There are certain limitations to using the DRL framework for a fraud detection task. Agents trained on previously collected datasets without any active environment interaction are prone to overfitting as a result of excessive training {{cite:9c8d098752ce16916a4900cdb3712a88d3bf8bc7}}. Their performance is bound by the ... | d | 0959a06dd8af5dee648080302f6eb783 |
In the last two decades, the lattice QCD Monte Carlo calculations have emerged as a reliable non-perturbative method to study hadron spectra. For {{formula:d09d7f79-a2be-4057-941b-d46143f03a58}} systems it has been shown unambiguously that the ground state potential is {{formula:c1c49567-27d2-418c-9a25-69f656b394dd}} ... | i | c2f6a4b02ecee5889300beb89051c011 |
Our proposed ASiT framework is based on GMML {{cite:2220be187d838fba6df2bd796ae68f1d0c6d829f}}, briefly summarized in Section REF , and self-learning of data and class tokens conceptualisation with the incorporation of knowledge distillation {{cite:553bbc5ddbf77953574ff58fc2acf228d5c9c95b}}, explained in Section REF .
| m | 9e0859dbf958c50991827ea490d23ece |
It is empirically well-known that stock return volatility increases after negative returns more than positive returns{{cite:ec78715f8f64e739b1cc9c606a255db49c0ab52e}}, {{cite:a3b0062a8850ba5677f2202c839bc4839dfbe3f7}}.
This volatility asymmetry is called "the leverage effect" and causes a negative correlation between s... | m | 793350318090e4685d0a91bdcb4d3c8a |
Again, the parameters {{formula:0acc0827-8ad0-4012-88de-11d2ccda6f76}} can be optimized and updated stochastically, leading to the REINFORCE algorithm {{cite:d7a5c6e60876529eb71c25d8607a4c312c1e48ba}},
{{formula:16413698-698a-477c-9c7d-611049aafd3f}}
| m | adf93cf873d818f007f1580eb02d8a0b |
In 1929, Paul Dirac announced that the non-relativistic quantum mechanics is complete and approximation schemes are desired to simplify the sophisticated quantum-mechanical calculations {{cite:b5da8971fa1f74c28c2acf1ead2eea8f9e246467}}. Physicists and chemists followed Dirac's advice and developed the mathematical fram... | i | 943200443d52974fc364eb596d14d103 |
Cardiac pulsation is a physiological confound of fMRI analysis pipelines that introduces spurious fluctuations of the BOLD signal. To overcome cardiac aliasing associated with a limited temporal resolution in fMRI, we developed a data-driven technique to temporally and spatially resolve cardiac signals from the BOLD si... | d | 4c1050334e7700166cb590f2f887915f |
Popular set function classes such as submodular functions {{cite:c17550cd48d173ae1f8b3e55c83f67a2fd1cc9e4}}, {{cite:ab2ecc3510a0cbe2961635accefd3332d7b1bbcd}}, {{cite:bb7c5129ae92faa2d782dca065722ec4f2c2c8b3}}, {{cite:b286ed9436258febe1a8680051ece13139b34122}}, {{cite:43a21847868f9de7488bb216e5b25f85993859f5}}, {{cite:... | i | a392289c2b1547e46739807372ec3fb5 |
The first condition corresponds to the original
“swampland conjecture” proposed in Ref.{{cite:5dc4e2d01c8402e557788d8bcfb0208b10297c02}}. However, a peculiarity of this conjecture regarding these two distinct conditions (REF ) and (REF ) on two different quantities {{formula:16e54e99-5501-40ef-bd99-ae7d48108d1f}} and ... | i | 8394706ed7009f20dbe5d980d693e837 |
Based on our theoretical results, we predict
a prominent experimental signatures of moiré exciton condensate: non-circular polarization of light emission at {{formula:71584a7f-271b-4c90-a4e6-47cf8d36782c}} with dependence on
field direction. In experiments, intralayer exciton can be first
excited by circularly polariz... | d | bcfaab9a024d50db35d21a3cf0a1e632 |
Prior DFKD algorithms in natural language processing {{cite:aad677adae4ee0cff540be97fd2e308683877f1f}}, {{cite:f80f1006a1f043b82a77baa3ead52cfb4e0234d0}}
focused on synthesizing pseudo samples from the teacher's parameters through model inversion {{cite:6a7126bc9f9fc46ff09b1916bf44369e8618c9b3}}, where a batch of synth... | i | d1c793bd7bdaddc74f910602170f7dc9 |
The search for universal features of quantum gravity–also known as the swampland program {{cite:fed08ed17c0acc4b4de9d21f79fb6db277317237}}, {{cite:dd160e832254e52f7d1cf71b2f1820b4009092b6}}–has seen a resurgence in recent years. Strong evidence has been given in favor of certain conjectured properties of quantum gravit... | i | affd80d68287ed73d69e59b2b3d5a86b |
Given the importance of M31 as an anchor for the extragalactic distance scale, many studies have
presented distance determinations to M31 using different methods. {{cite:7beab308d482942e7e5de9cc8c9e01cff42e0438}}, {{cite:d2eca3820eaf6fe5aa843c84658901a1aaac6d80}}
and {{cite:db85d3d5827ecece3962dd3737a8e06330e71a29}} ha... | r | 3dc7305579f9a7186b95d40cb49e45bd |
Equation (REF ) is zero if, and only if, the two overlaps {{formula:eeaa1d8c-484f-4511-a6ec-a800deeeacf1}} .
We choose the optimiser RMSProp {{cite:c3ccadeecb27927e1ae4ba0005081db39b1576ed}}, which dynamically adapts
a global learning locally for all the network parameters.
Training a medium-sized ANN with {{formula:f4... | m | 65aa7035c2c84a211fa6a125192dd760 |
We have adopted a number of simplifying assumptions to model the generation of super-harmonic secondary waves. Firstly, we have considered a region of small spatial extent near {{formula:2138f524-5e8e-4036-908c-675bf591858e}} , and employed a local Cartesian model instead of global spherical geometry. Secondly, we assu... | d | 75a71e13813b8b09f3b2cb309c6fff4a |
aasjournal
Calculating interaction strength factor {{formula:60bd49d8-38fb-410d-b1cf-34f998c2e409}}
The plasma flow-obstacle interaction strength factor {{formula:23bed83e-3ea0-4502-9fc9-b2e6615be777}} can be determined by considering the ionospheric Pedersen conductance in the case of either a magnetised or unmagnet... | d | 16e3488db0268e3752a017956d734807 |
Kernel methods can be thought of as instance-based learners: rather than learning some fixed set of parameters corresponding to the features of their inputs, they instead “remember” the {{formula:7220e53e-4294-4651-9da1-5a46a07ccf5d}} -th training example {{formula:21b3d0db-4681-4b39-ae5c-fcbc4f6963f7}} and learn for ... | m | acf20a4f15d2c9ec122ffe47cdc61d57 |
In Table REF we summarize the results on wordings unseen during training and the corresponding statistics on training and test sets. The error rates indicate that the proposed architecture is to some extent capable of generalizing to new wordings and attaining decent performance, without further tuning. As expected, e... | r | ac64484c0109165b7357689bd9abc8c8 |
There are three very important open questions in neutrino physics that can best be addressed by next generation neutrinoless double-beta {{formula:8f22d56c-f681-4ef3-a9bb-b13906597055}} decay experiments. First, are neutrinos Majorana particles that differ from antineutrinos only by helicity? Second, what is their mas... | i | 4978d2a51e336db04325d20be3651a2a |
The part of the wavefield that is traveling at a smaller angle is reconstructed properly, even at large depths and at the edges of the aperture. The events in the center of the model are reconstructed properly. The amplitudes and arrival times of the events are not correct everywhere, which is caused by the use of a sm... | r | b1b4b06108f49e01ac354eba722ce708 |
From Andrzejak et al. {{cite:679219f58a4afe9b829b1e90079bfe77e3a88236}}, 10 Participants (5 Healthy and 5 Epileptic Patients)
| d | 2738538329bb83c72ba3845b34fc442f |
The nnU-Net segmentation network was trained and evaluated using a five-fold cross validation on the training set. As in {{cite:612d70ea253d152ad278e315b18d5bb8bffd4699}}, the network was trained for 1,000 epochs, where one epoch is defined as an iteration over 250 mini-batches (with a batch size of 30). Stochastic gra... | m | 7fbd130c477c33bddc2c6d47c744da9a |
For the quantitative assessments, DEER had better SSIM and MAE values and a slightly lower PSNR value than FBPConvNet. FBPConvNet achieved the best PSNR value due to the implementation of the Mean Squared Error (MSE) based objective function. However, the literature has discussed that higher PSNR values do not guarante... | m | 3a83d303e2b073f4aa10d473108514c2 |
where GRU denotes the GRU cell {{cite:69fd8caf18b56bb633aea9a045a78fc6329e2c36}} with LayerNorm {{cite:d70947fefc688410fe4bf80af990afe7d05909dc}}, {{formula:86a92186-e250-4f8d-a34e-e8272a140c2a}} are other learnable parameters, Aggr represents the neighborhood aggregation function (we use Max), {{formula:4ee8406a-6d17... | r | 186b8f3b77ee76adbe7a9d260fd75711 |
In this section, we introduce ASiT, a self-supervised framework based on vision transformers for general audio representations.
Similar to {{cite:c85e9902708cad75d7e544c4edba7889c339b0ab}}, we employed log mel-spectrogram {{cite:9a4970a9b1d4d132414395e5cb6b643ba381f925}} as the input to the ViT instead of using the raw... | m | 1905038acf591f87e752442d368c159b |
Different from interpolation methods, machine learning methods are learnable which can learn physical correlation of the heat source system adaptively and the learned models would be more fit for TFR-HSS task than interpolation methods. In this work, we evaluated 4 commonly used machine learning methods for TFR-HSS tas... | m | 563e86c0dbe89675b84507d5b4e3a969 |
The idea of exploiting the implicit regularization properties of optimization algorithms has been studied, often under the name of iterative regularization, in the fields of inverse problems {{cite:8b6d8c5c5d3616261d8a1fa57cc44b2dd8950a19}}, image restoration {{cite:b64b7f6491efb1c53069b4be27dcbffd8e389e0d}}, and more ... | r | 56d8d0a43c9ba4d801d9f2dea2a3a925 |
The {{formula:b2d05a96-ccca-473f-b3e6-4c1295401170}} operator's value depends on the advisor solution.
Next, we will state some assumptions. The first two are commonly used in RL {{cite:0d1a1a724dce90673507032ad9658c2789e00c5e}}, {{cite:b0d0284bb4e35f4278fb33fae549aa5c78f54d07}}.
| r | 1c9b6de90b6d4df6393b8faba1c682f1 |
Understanding videos is a prominent field in computer vision research. Event (action) recognition {{cite:4e86c93ca1f349214cb0b2f1a59195a0249b43d3}} and temporal event localization {{cite:169e03263a1abb14f2ccdf6217a4d8c2887ea938}} are the two main issues addressed in the literature pertaining to video understanding. Act... | m | 0440c6f0e1d7bc52ccca41441bbb35db |
Remark 4 (HB case)
The set {{formula:8653e5f6-01c6-4d5b-afba-f27beffa8a31}} given in Theorem REF allows {{formula:1c77af41-94ec-4d77-b5ec-91e67ebd64b4}} and it can be easily shown that {{formula:9a8a7239-ac3a-4912-b93b-8bed99cc0abf}} is contained in the stable set {{formula:8dccb830-eaec-42c4-a170-ffa454181d50}}... | r | 0e9596b9cc623b98fe4697a1dbea9ef5 |
In this paper we calculate the probe-limit phase shifts {{formula:c96fff17-6717-4ce2-b606-850ac3fb7a61}} explicitly by solving a relativistic wave equation in a background gauge field/metric. See {{cite:061f5d23bf7153f5694bd5ed95493cd710a4f478}} for a related connection between wave equations and classical scattering.... | i | 76431c636c1616f07a774520bd17d94e |
In order to create graph representations for the presented scientific news network, we implemented a baseline graph neural network for relational graphs (R-GCN) as proposed by {{cite:6bbd09cab4c6ae2d62e33ba1f569c6c8e3cf4f7a}}.
For the link prediction task, R-GCN is comprised of a graph auto-encoder model.
The encoder c... | m | 6ad73ba83de8b5d45c4ca3af4666fe05 |
With the recent success of neural diffusion models for the synthesis of natural images {{cite:93cc2af5caf86020d7c5d0ae7b00ea230b0a3c48}}, {{cite:d7bb08ea16d4359fcc2ef57f078c9fb4b10710f9}}, there is now an increasing interest in exploring the potential of neural diffusion models to generate medical images. For generatin... | i | 95960067c11773bddbec8d46b1b94e03 |
For instance, the {{formula:77c1d980-ab6c-442e-822b-ab30e22f0a8f}} 10 {{formula:a29c9026-3ea5-44ac-9f7c-88a8ee67ddc8}} m feature peaks at 9.8-9.9{{formula:3f6fff03-499f-46b9-9dc1-8148e3494a87}} m for both
Enstatite and “Cosmic Silicate”{{cite:acd934b43f24eb91fcea191759bf2b647b6ddaab}} produced infrared spectra of a sil... | d | e3c572e8d76e0b666d3dae629fe83a5b |
It is clear from this study that care must be taken when deconstructing
observational spectroscopic data. While radiative transfer modeling may be
able to provide a more accurate representation of the temperatures of dust
involved in the emission of photons at each wavelength, RT modeling is
hampered by a lack of appli... | d | 4b2b9df46b69bf6df117de51d46f75fa |
An overview of Preservational Contrastive Representation Learning (PCRL) is provided in Figure REF . Generally, PCRL contains three different encoders and one shared decoder. The encoder and the decoder are connected via a U-Net like architecture. We first apply exponential moving average to the parameters of the ordin... | m | c40cf1a0f86e2b4ead08a91f8156eb32 |
being {{formula:53ddbfbf-a2a2-44b2-80bc-f5939aa76b1a}} an integer that ensures maximum order of accuracy near the discontinuities and where {{formula:669577a3-095f-4dff-9463-d3c450859b6f}} is defined in {{cite:535fd595f24698d596ed72b5d1965dd0994fc258}} as {{formula:197be30c-07dc-4c73-b4db-faa5f3825de5}} . This is a c... | m | f4b1dc424a0d13ed58f6b81f6122a09a |
This result is a field equivalent of the Harris {{cite:82b9353cbac6d61dd63b8ae179c2ca6a70cbf9df}} or Fortuin, Kasteleyn, Ginibre (FKG) {{cite:cd19da7cdab5afeeb7aeb1c144e5275dcb263206}} inequalities that give positive correlations of increasing events in percolation. Such inequalities play a very important role in perco... | r | 0d1edb5a47e092b3a448d54e06a9f9d0 |
The curvature-dimension conditions {{formula:3366129c-996e-4b7b-810e-db3ba7deecb4}} and the restricted curvature-dimension condition {{formula:d8b2c560-528b-4152-a78d-6ef0154fd4dd}} for an essentially nonbranching metric measure space {{formula:f48bea79-8933-4992-97a1-841831c4246b}} are defined in Definition REF . ... | i | cce884cb1c2d91e6adba13fa848a1511 |
Several dimensionality reduction techniques have been developed for the subspace approximation problem over the past several years (see, e.g., {{cite:5ab5c629eacd197aab15b4f9aae3682112063377}}, {{cite:a979e6f38972fac494e425684eb6d01a92e62c9f}}, {{cite:134371c8d2772ea0705728eedcaa332d2fbbbd4f}} and references therein). ... | r | bf7d45303af1e205d0a8907d950b8e39 |
Our proposed attack outperforms state-of-the-art methods in both attack success rate and training overhead.
The rest of this paper is organized as follows. Section surveys related efforts and motivates the need for the proposed attack. Section describes our proposed backdoor attack. Section presents the experimental... | i | 0d76454244c4a3dced628815603f7225 |
From the point of view of the architecture, compared with muzero {{cite:3a70fbafb87e25e3ef59fe656db6e9cbabf061f9}} or imagination-based RL {{cite:ff18a0a45f031aa11fbb5b092ef28bad815b7a60}}, {{cite:b24e752f47ba07324e7f1e63604e178a4ff71cad}}, the approach presented here only requires training the SSM and not additional c... | d | 966792267a1964c986468977c0f734e8 |
Our Method In this work, instead of starting from the prime Kantorovich problem like the Sinkhorn based methods, we directly deal with the dual Kantorovich problem. The key idea is to approximate the original non-smooth c-transform of the Kantorovich potential by Nesterov's smoothing idea. Specifically, we approximate ... | i | 3f17b389246b969ce5a8c65964421f28 |
These results qualitatively match what has been observed in misinformation literature. Even when exposed to factual or scientific evidence (e.g. that wearing masks would mitigate the COVID-19 pandemic), people who are already skeptical of mask-wearing are not able to be swayed. They often instead rationalize their exis... | r | 32f9cde9bb96b11b04d025050e7ba5ef |
Optimization problems are one of the areas where using quantum computers is considered to be advantageous {{cite:d5c66b61cecd8fc88d43396de77aba4e002ec558}}, {{cite:915a1b9028c313b0f157ef0c2d247e72ec06265f}}, {{cite:bc334144756a6cbd5cd31f909dac78833dcd5163}}, {{cite:c35321abea31f3ddac9512c66f8e5aeb033f66b6}}. To solve a... | i | 859dc18301be384238f0d2a63ad462a9 |
Noise2Self (N2S){{cite:3e735e0a3600aa8d1f05072899533d5c22c041fd}} is a blind-spot network and is the first such method to present a blind zero-shot version of itself. This is achieved by restricting the training set to a single image. Other methods can be similarly adapted, such as N2V {{cite:f2b0fc1baa5ee5d71d0d01f3b2... | m | 95e0cd67b4850562cea0f2645e025a52 |
It is well-known that a convex combination of NEs is a NE (see, e.g,
{{cite:6f03e7606cb80d19e09346aa7c9d357fe5d33686}}). Since the string operators {{formula:feab5f86-db0b-4fa7-8da3-1d949e5ab8c2}}
in Algorithm 8 appear inside an infinite series that resembles a convex
combination, our next aim is to show that, under ... | m | 84c13356d5ac362d95d3dabbd93f9b10 |
This algorithm is similar to the one proposed in {{cite:a37ccccab5d762b3632ecf024cbcf78d99b16f18}}, {{cite:9967208c46bfce97b41d589b39720d0128581af1}}, with the presence of the homogeneous stepsize (REF ) as key difference, and the replacement of a negative stepsize with the last available positive stepsize. The converg... | m | 026544981d7fbe5ca293852611e2ab7f |
The majority of EicC SIDIS pseudo-data points are essential to improve the DSSV14 {{formula:585aa4d8-7bb4-4c92-9de5-e0f49ae43fa4}} distribution. Fig. REF shows that the first optimized eigenvector pair (EV1) dominates the {{formula:434563de-3735-4c7c-b2e2-cfbc7e010435}} error band, while Fig. REF clearly shows tha... | r | 6dcefc66572dc7604729c92ebae6e684 |
The division into three groups with either mostly winds and weak accretion or mostly accretion and weak winds or winds and accretion roughly equal is an intriguing result. In particular, the existence of a significant group of objects disconnected from the cosmic web with {{formula:2db72d55-7004-4f69-8c64-df557a012212}... | d | 9d235244d7a131f4b830d875133b848a |
In section REF we described and gave possible resolutions to the Bekenstein bound puzzle, and discussed the puzzle in the context of islands in evaporating black holes in detail. We did not however give a complete argument for how the early time representative {{formula:49c58c22-ce25-42fa-b3d3-7919f567563d}} of the i... | d | 5ce3baf7ab05c6a96d6c82b00196829a |
Recent years have seen a nascent, but growing interest in leveraging deep learning for RF applications. One such application is “spectrum sensing”, where DNNs are trained to classify the modulations of signals in an RF environment {{cite:7a143efee3e061c9354204f64809ceed5a97ee2c}}, {{cite:adfe71c63392d9c5e83553dfd8d02f1... | i | 05e68e532cc7a2f2bcb5d6fc12e6751b |
Since the introduction of AlexNet, roughly a decade ago, Convolutional Neural Networks (CNNs) have played a significant role in Computer Vision (CV) {{cite:540fd20e02084b1c7aee91f808c47e40bd5c9c10}}. Such neural networks are particularly well-tailored for vision-related tasks, given that they incorporate several induct... | i | 17f8411304d7ad584ed477bb15e78782 |
In general, good performance of DNNs for some tasks is usually associated with their ability to change topology {{cite:cbd4dec77933eaf0dff1fb6ba87a0cdc150d7cbd}}. However, when one wants to use the latent variables or codes of DGMs for further tasks and not just for generation, these changes in topology might become an... | d | 8135b37f0693fe57c9971b0320eba052 |
Entangled photon pairs are typically generated via spontaneous parametric down-conversion (SPDC) {{cite:783aee17ae615888843b98a5d8a325d8f36fd52b}}, {{cite:680694ace3a8e541d7eb0b03191e200b576e88d0}}, a second-order nonlinear optical process that is tantamount to time-reversed sum-frequency (SF) generation {{cite:a11a654... | i | 3146ac99ee7020776fb29e5b6e0ecb5b |
In the experiments we used the RSICD dataset {{cite:a91dedc3ded13cfd8513e19fde2f82fe127ea240}} that includes 10921 images of 31 classes from aerial orthoimagery. Each image has a size of {{formula:249f9425-854a-4987-a16d-a5d747a50dca}} pixels and has 5 corresponding captions. Only one randomly selected caption for eac... | r | edfb900bf335752726d8868cf88f3296 |
where {{formula:98f77c0e-5fc3-48b6-8960-5b40b35d4f4a}} is the time interval between two atomic
configurations, and the summation is performed over the nearest
neighbors located within {{formula:5d17208d-3e79-42c7-bbe8-cc1167486364}} from the position of the
{{formula:32ac7a4f-c8ed-46a3-b9cf-3ea22558a175}} -th atom at... | r | b4fd8148efd18128e38a2a1ca57e76ae |
In methods GP-AIC and SRP-AIC, the criterion used to optimize the {{formula:e8850c69-a530-4f2e-83b3-6e314ebe1698}} values is another formulation of the Akaike information criterion ({{formula:dda04c4c-6b8c-4ab1-89f7-3e5412e6c1fe}} ), adapted to penalized splines, as proposed by {{cite:203ccf6282294953bc1a65e214060049d... | m | b597995dbd0e0ac952e902320490dbf3 |
We compared our Aspect Controlled
Summarization (AceSum) model with several extractive
and abstractive approaches. Traditional extractive systems include
selecting as a summary the review closest to the Centroid
{{cite:1133128f3dcfb1265e7e27560a5bf0fd6efcdad0}} of the input reviews and LexRank
{{cite:874cea6523f53b777e... | r | 8af5efbe24bc493ce5cf6d027e81ebe3 |
In Sect. we report the results of simulations in which this systems starts from a flat-space configuration with {{formula:6cf01554-6503-4961-8c38-a5d8c6205914}} for all {{formula:65b0b1f7-4f8d-4000-8be6-f2545ca93d60}} , and then at each step one of the {{formula:5a5c8434-1c4e-4762-b288-0c40aed88156}} is randomly cho... | i | d8852c83ce496c18bfdca2304b317aa6 |
Despite our best attempt for a fair comparison to the baselines, we recognize some of our baselines used a different setup. For example, AFSD {{cite:94128cb425c79264410f3c9464a60a0742ef2356}} took lower resolution input videos (96x96) when extracting I3D features. PBRNet {{cite:fc391044040e7cb25bdc9cde0b5844d89cf85caf}... | r | 0de7d7d0b35b323cdcadc25a8bf3b29d |
Finally, since the radio emission of J0849+5108 arises from its jet,
the jet activity and structure could directly lead to the QPO signal.
The source is a target in the MOJAVE survey and multiple Very Long Baseline
Array (VLBA) imaging of it during 2011–2016 are available {{cite:3f9129dc12855240e6543da1446d55a8df1ec581... | d | 29e7ff7baa70489d0543fdce1566b5f4 |
SOIE {{cite:475c1b9d2d59ff7f9e8b203cad8bde527e719b02}}: It was developed by a Stanford team and was one of the most popular OpenIE tools. It leveraged linguistic structures in dependency parse trees.
OpenIE6 {{cite:e2c71536e694a58c715c082a63559d61769d76ef}}: It performed iterative grid labeling and coordination analy... | m | 58e73bf6cae397129b244481298a857e |
First, we observe from Figure REF that when the training domain is used for model selection, no method performs significantly better than ERM. The performance gains for domain generalization methods only appear when model selection is done directly on the test set. This is consistent with prior findings {{cite:975101b... | r | 614779cdd52745f6e43a938eca1398c6 |
Our calculations of the stability, equilibrium structure,
polarization and energy changes during structural transformations
have been performed using the density functional theory (DFT) as
implemented in SIESTA code {{cite:a70a74dd76400f71456d3b8b1fd154bd0cc2f3fc}}. The 1D systems have
been represented using periodic b... | m | 239a154633ab85c19a8552490dccdf05 |
The Fréchet Inception Distance (FID) and Structural Similarity Index Measure (SSIM) are the metrics used for the evaluations. Lower FID or higher SSIM indicates better performance. For each class in each dataset, the model under evaluation generated 1,000 samples. Those generated samples are compared with the test samp... | m | d02b13996a76e8459a58d461ca0bc603 |
In this paper, we consider a different type of limit for alternating sign matrices where the discrete process is no longer visible by moving away from the tangency points. We introduce a directed path picture for the alternating sign matrices and show that the fluctuations of the maximum of the top path, which separate... | i | 63394b1f8364bfe1924a292de9cc75f0 |
We verify the convergences announced in Theorem REF through a series of ad hoc numerical experiments carried out by implementing the finite element method (cf., e.g., {{cite:4a8120bd88f7bfb761466bd54d912dfa5903e9e7}}) via the software FreeFem {{cite:7fd5c38338de1ddee5b2711fe22dd7a03d3e5818}}. The results are visualise... | r | 43cdd6d9b77c96a80922dcd31b37f208 |
First, the perturbative RG-restoring subtraction terms, like in Eq.(REF ) typically,
are missing in HTLpt. Accordingly the latter lacks perturbative RG-invariance formally by a
leading order term of the massive theory pressure, {{formula:ac3ab7c4-9c00-43dc-b303-c2fc780b934d}} .
Now since for any (gluon or quark) therm... | r | ca36f7f23d76745d434d9d5ba54c4936 |
where {{formula:ed8ddfa5-72e3-436f-9bf1-22a91d6d5031}} is a circle encircling the origin, but not any singularities of {{formula:35cae92c-3169-4ed7-b386-4c8c0c6bb9fb}} .
Expanding the contour {{formula:37b0a5fd-3557-462e-8251-cec917ab5521}} if possible,
and applying the residue calculus if a pole is encountered, we o... | m | 23c31cc3062f943ea97365ddd15c9d01 |
We select a set of commonly-used IQA methods to build a benchmark.
For the FR-IQA methods, we include: PSNR, NQM {{cite:db103b2ebba05a6204f0df599e8841b07515b90a}}, UQI {{cite:43977bf7f1b5e999d7cd1c86f76a7bf57d11c3f4}}, SSIM {{cite:14db4aab83677b59e89b20ada21405312dc9eb7f}}, MS-SSIM {{cite:96f48975d4858eaf7c51e05e2fe609... | m | eb7d8e779a948aca5ea48fc90decd513 |
Taking dissipation processes {{cite:a32349f17fc1ef494dda2c962179691da251bbab}}, {{cite:71b69a100ef51dd84161c092cb0f58f538f09030}} as well as angular momentum transport in stellar interiors into account (see {{cite:39076490cc2703c6aa77878ca003e2c3661a23b0}} for a review) is also among the perspectives of this work. In t... | d | 486d488ed2f3a3d16bd089d5cdb930bf |
The set up presented there does not seem to apply to our case since the phase space considered in {{cite:5c98cecf1e2c25247874fa70a8fcc755472da627}} is the space of continuous functions on an interval, namely, {{formula:fd5654e3-3ffc-4c16-bbff-fc90ea13e459}} , and they require differentiability properties of the equatio... | r | cb75b0928733196e1a80c70efe71890c |
For the SemEval shared tasks on CQA, several authors used complex recurrent and convolutional neural network architectures {{cite:09efaff9ec3be2d50ee84a9e40baf593d82cf5cc}}, {{cite:b656224d72ac1047e936378892f4860ec446be01}}. For example, {{cite:b656224d72ac1047e936378892f4860ec446be01}} used a convolutional neural netw... | d | e01b441b46efb56cc64d3778ee99e760 |
with {{formula:dd8fdf2c-001a-4155-adc9-a99b00136bd8}} .
This is the so-called orthogonal projection of (REF ) onto {{formula:07699557-02fd-4a5e-91ca-c7cd91adf395}} , and it ensures that the mean-square error resulting from the finite representation of {{formula:6cc5aabc-7ad1-43f6-9f06-af240c7e29da}} using (REF ) is ... | m | e6ee5f34591ff7a2b2f6c1a98964b8ef |
We compare our method to our re-implementation of SSF
and two ROI-based baselines. The first, dubbed ROI-aware loss, is SSF trained with our ROI-aware loss as described in Eq. (REF ). While the codec is blind to the ROI, it is expected to implicitly learn it through the training objective, in a similar fashion as the s... | m | 209ba6f1943f46f43a03acf0c4acb505 |
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