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One aspect in which the second-order Trotter formulas that we use perform well relative to popular methods, such as qubitization {{cite:e4e0611b79197a5367df58053c3b39830027b792}}, linear combinations of unitaries {{cite:cdf774f112b2a33e27b05f75961426d09a7ed77e}}, {{cite:e4e0611b79197a5367df58053c3b39830027b792}}, {{cit... | m | bd694c1e9cbf2eac5835dfb951aae0e2 |
To evaluate our inferred spike train {{formula:6092d0c8-4205-466a-8976-3c7c15ddefcf}} , we employ the following standard practice {{cite:a11bde4a92e034c4cb8cc2ce88fe76303033f1d2}}: The GENIE dataset provides ground-truth times for neural spikes. Let {{formula:35bb3a8b-de93-43d7-a9d3-b0040c0785bd}} be a one-hot-encoded... | r | 193b7988ecf4028fd07e3637b58e5d69 |
The both massive and massless particles which are capable of carrying nonzero orbital angular momentum (OAM) are of a great interest for researchers from different fields of study. The first seminal description of the concept of such twisted particles was introduced by Allen et al. in the early 90s in Ref. {{cite:a85c9... | i | 61068aed07e5fc242ee8b3166d204e72 |
This type of method relies on a box-regression based object detection frameworks with word-level and line-level prior knowledge {{cite:468bc3a08883e0e36d1fa34f8b483cbeb2313439}}, {{cite:e0623cb9658a7ee5daaa192c24bf3dddfbe0dd6f}}, {{cite:e38d13488f9aa295d92f9972a25554534ec3307a}}, {{cite:0c18bfafbad0489da6ab7b7e3b41b741... | m | 2f2ac53a0fff32fa5b95583135ff5b57 |
Deterministic symbolic regression constructs a large library of nonlinear candidate functions to regress data. It identifies the relevant candidates by adopting a sparsity constraint. Two fundamental methods have been proposed: Sparse identification of nonlinear dynamics (SINDy) {{cite:42176f77235fb00602616b68b1bfc4110... | m | 4bf583551bb1a395fd72db57b3e9a2bb |
The spatial domain {{formula:11807f36-5641-4296-84c2-f50826db9071}} is discretized by a staggered structured grid, in which the first and third order derivatives are defined at the cell-centers, and the second and fourth order ones at the grid points. Following the natural order from left to right, adjacent vertices a... | m | 4b68d00015c9c89a129914d5eaecc933 |
It is worth noting that in contrast to the weights of a standard neuron, the weights of the compact support neuron exist in the same space as the neuron inputs and they can be regarded as templates. Thus they have more meaning, and one could easily visualize the type of responses that make them maximal, using standard ... | d | 0b6485d0f4bd15f58ccbb32c30d2631e |
Considering the generality of the DJESCC method, there are multiple architecture choices for the encryption network, the DJSCC encoder, the DJSCC decoder, and the decryption network. To prove the potential of our proposed method, the original DJSCC network architecture in {{cite:3da1999921f77eb034be25e11386e28ea8486929... | r | 027ba41ec9e4400d29e9919fbcef1d7f |
DeepLIFT. Shrikumar et al. {{cite:89a9a6a825be6ec7fc8d3b9d3fa25dcbc328e1f8}} take a different approach to attribution, introducing an efficient method for disentangling contributions of inputs in a neural network – deep learning important features (DeepLIFT). As opposed to LIME, DeepLIFT relies on comparisons to a re... | m | 3050177678ccea4ecd85b28ec20df4ca |
Our focus in this paper is on a mathematical framework of transfer learning and corresponding theoretical results related to geometric structures, minimax bounds, and minimax optimality. To provide further insights and understanding with respect to our framework and results, we now present a collection of simulation re... | r | fd5226e61c675b9d560a68764e6e1e82 |
We begin with the Riemann–Hurwitz formula {{cite:ce3f92f262123795eba574dcc85747325d0051cb}}:
| r | 86efeddd3c980c1c22125089a757b0fe |
A number of possible approaches can be used to tackle the learning problem introduced above.
The most classical is standard empirical risk minimization ({{cite:e7ca93388af62f47ed7c01520b49b179a7f8ab4b}}),
in which a model, {{formula:ff9a3078-c943-4ac0-ad0d-aa5583d3f30c}} , from a class
of possible ones, {{formula:2769c... | m | 4a4d0e8df35707002be22cafde3b4d31 |
Deep feature transfer learning is the idea of using a neural network that is trained on a massive dataset such as ImageNet {{cite:cb43a892230c6f282cd95fca2be317f1fdd566e4}} with hundreds of classes to predict labels on another dataset. This idea could be achieved by removing fully connected layers, which are used to cl... | m | 7a720ca749f7c7ac1fc1dfe5e38b18f6 |
We also note that resolving these conjectures, and better understanding entanglement cost in non-local computation generally, has important implications for position-verification {{cite:c3c7dc9fdd4c9ebbeb959f65482fa3978978e793}}, {{cite:c0a641ece1f731b2227a7acb49913b8de9b4b43d}}, {{cite:10f47b024571d402e2590238e7292110... | d | f1d7a0aa1863f40588ee44a9c30dd7bf |
We have described several dynamical quantities, including those that describe dynamical heterogeneity and the morphology of rearranging regions, that demonstrate a crossover in the dynamics, when the mode coupling temperature is crossed, with relaxation times better approximated by an Arrhenius temperature dependence a... | d | 0a8821966f5e4f35d86faf66c5fbb470 |
A smooth classification loss such as the one based on logistic or cross entropy functions is necessary for gradient-based training. Consider a network with weights {{formula:07e4f08a-f24d-40be-bc03-3fc2d0912739}} and corresponding fhsn weights {{formula:0b2b2164-d7cc-440e-bc50-9356c06211db}} . When we extend {{formula... | m | 38907beb3a10e20e9702769f9789fd7a |
Also, although there are large scale chemical graphs datasets available{{cite:f0f9c673237be7dfc5c384e5ef4785b8bb5e562a}}, {{cite:a3171733c25ac30f2d4842bd477302ca08b89167}}, a benchmark dataset that contains many large graphs is still missing. We plan to create such benchmark dataset for future use. In general, while no... | d | a373fec92102a33c3d680473872591d3 |
all solutions of which are meromorphic and are of finite hyper order at most (see {{cite:5393723e3f481af2c3ca858bacdd5e516cc11466}}). Below we use the method in the proof of theorem REF to study the growth of meromorphic solutions of the third Painlevé equation {{formula:244c09e6-9320-4ebe-abc5-e9b92f5bed9b}} and als... | d | 2c851ff130d6c856bb602ffc39afc55c |
No loss is perfect and improves performance on all the datasets. There are two major scenarios when recall loss does not improve performance: 1) when a dataset is very "difficult", there are compounding factors resulting in low performance and imbalance is not the most limiting factor among them; 2) when a dataset is t... | d | d642f66f34c14722b18f219fe3b387e7 |
There are at least two main strategies for handling missing data including omission and imputation {{cite:e88331f9e3cff48891d47434196c1aa2312f1415}}, {{cite:4301639223786df71e93c521d9a6f0b6a3e986c8}}, {{cite:925d619272d35225fdedcab3da0745c2d4c86446}}.
Common omission approaches include listwise/pairwise omission and dr... | m | b32b18a52cf7c966d1a25c5cae5e9115 |
In this work, we propose an unsupervised LCO framework. Our findings are applied to general CO problems while exhibiting extraordinary promise for PCO problems. Unsupervised LCO has recently attracted great attentions {{cite:ad819e30a283dd35fc2335a4b2a0106dd5354e91}}, {{cite:5403e77dec5f569432eafd9879193a61b4cb20b5}}, ... | i | 0b43dd6f0a4200694d7bcfc16fd192fc |
By Gershgorin circle theorem, every eigenvalue of {{formula:76aea285-4d70-4764-908b-426c774a2b5a}} lies within as least one of {{formula:8b81138b-ccab-4f72-9564-3d3a47ee516b}} for {{formula:dab4899b-7eaf-40d3-8dde-9fcfbf02fb03}} {{cite:c79af7b8f28f29d3029ef7a2214473c2c9d2206e}}.
Thus, it suffices to show
{{formula:6... | r | 193e2093fd816397604b71d772b64d81 |
However, when the neural network is adopted as the solution ansatz, the trained model is often found to satisfies the governing equations but for overfitted boundary conditions {{cite:3be5129605df9e8f7be892a41a1244d765373b3a}}, {{cite:770e218577b2f9520f21bb19a443861037b00e2f}}, which greatly differs from the traditiona... | m | 9c9bc0118dff967f89e7a9478339d817 |
We extract the central value and the scale uncertainties for 136 processesAll {{formula:74a36f33-e15b-49a7-a7ad-75b261105c55}} -initiated processes other than {{formula:bea10091-3024-42a7-8c25-97d31e7b8cd5}} , for which the NLO cross section has a typo, confirmed by the authors of Ref. {{cite:dd9da7878c5e3bb3dae75d6fda... | r | acebdaec64f60c0aaf381d628f8f1ec9 |
We obtain a 4-approximation for private {{formula:38ae8a52-85d0-4327-8b2a-a84dd126547a}} -center with outliers (5 for the supplier version). This matches the best known bounds {{cite:f1c8edf2ce1148840e034d8d1b59795eb30c2ba6}} ({{cite:5825851707943bc541cc061ab237fd77d20adbc9}} for the supplier version (this also holds ... | r | dc4d2dc1028686fe81ad828885ccc504 |
The previous best bound was {{formula:bbb4b394-4ef0-4cd2-8b56-07554d21e9bd}} iterations {{cite:aa8f6b57e0558aa112e3471ad0aec6aee94daea4}}. This result is one of the key surprises of this paper. Although this problem has been studied extensively with specifically-designed algorithms and analysis, we show how to get a b... | r | aba907af66a3d5cbbdc7bec05b4a526e |
The {{formula:aa3db965-e194-41b7-8e58-c9178f1c68f1}} -ray detector response functions were simulated with the geant4 framework {{cite:f43a8c71a26f43fa5041ad1f30c462f446847ec1}} for
fitting and comparison with the experimental data.
| m | c3c86c24f08a0b6a29c221830fb3727b |
The second case of interest corresponds to systems with {{formula:611b0ca3-9858-4b26-a687-d5557b74cfc6}} and {{formula:df4e3fc9-195a-445c-9656-e52ccd64be39}} .
The corresponding U(1) gauge-invariant model was studied in
Ref. {{cite:e5a3d999af56300d778cc4937799d828640ce51b}}, finding a charged transition line, where bo... | i | 2b974ab6784417191b2ad3c2c814c71e |
Time-preservation requirement {{cite:f2d50dc0c5d529f46640713d3ee18f26c7c87ede}} of the primary constraints {{cite:7a7f7712c8fc91afe3be1a2796f579796067da12}} for (REF )
{{formula:6bb87385-e954-411b-8ff4-0892ac4f2d0d}}
| i | b9438fe28631409840a7b25b3a769b41 |
Fig. REF shows the learned weights of the first block of the deep maxout neural-kernel networks applied on the Motor dataset. The the learned weights of the second block for the same dataset are also depicted in Fig. REF . It can be noted that the magnitude and structure of the weight matrices corresponding to the fir... | r | f22b5e63140a0e8c67e348a0e3baead1 |
Over the last decade, technological advances have allowed the emergence of Artificial Intelligence (AI) solutions for many applications. This emergence has been accompanied by an amplification of AI research, for which major tech companies have largely participated in its democratization. This democratization has allow... | i | e2d30a6e061a40ae0a432ba94f0dc766 |
The equilibrium geometry was determined for the primitive unit cell with a
{{formula:59fc455d-8d1f-46fa-b49c-abd6853fe6bc}} -centered {{formula:71921f77-c07c-4a69-9875-cf78d418d479}} Monkhorst-Pack (MP) {{formula:b3821f9e-25b4-4f98-8863-136b9fecd254}} -point set
{{cite:443b2cc5ec35c767f86053d0db25573db58b7b3a}}, based... | m | fd6d509ea4686bd3bf60b64503046895 |
The experiment is designed to asses the MixMatch's accuracy results when using the proposed methods to filter the OOD data in the unlabeled dataset. Hence, the MixMatch's accuracy is measured using the filtered datasets with an AlexNet model. Table REF shows the results of training the AlexNet model using MixMatch wit... | m | 2c7e22536dee6b272d79e0cf22652d10 |
However, the aforementioned studies are all limited by the homogeneity of the data that was used. They were either single/few centre studies or they used publicly available databases such as the UK Biobank which have uniform imaging protocols and few pathological cases. As such, the datasets do not match the variabilit... | i | 1f269c8b1c8b988289f7462fb622f3f0 |
Arbitrarily-shaped dataset.
We further compare our method with state-of-the-art approaches on the benchmarks containing arbitrarily shaped texts, including Total-Text {{cite:beca25e35652fc12c07e07f9e6e616a9329194bb}} and SCUT-CTW1500 {{cite:d4f58f38680af9f26829f95438f4e7c263983883}}. As shown in Table REF , SPTS achiev... | r | b86bd4df3928aa1e09ffab6e8968f2c9 |
(1) Classification of AD and CN subjects: To train the proposed multi-stream CNN, we randomly select 70% of the MRI patches from the two classes of AD and CN as the training set, 20% as the test set, and 10% as the validation set. We also compare the trained model in this step with 10 additional approaches, including r... | r | 94d46eb1c0cb95099e016d780bbd1d12 |
We have also compared the sensitivity of the Observatory to the neutrino flux
observed by IceCube between 19 October 2014 and 6 February
2015. The analysis of this
period resulted in constraints for the normalization and spectral index of the
observed fluence {{cite:d761a81326d2060570e00eb10cacdedfb11201de}}. This peri... | r | 0b704405a4d8b99ca0422a3b52e5f9ec |
Aside from designing specific models for different tasks, the advent of pre-trained language models (PLMs) such as BERT {{cite:3086b4481339186fcfc4d102f44ad7eab20b259d}} and RoBERTa {{cite:c7cb804ab5bc7bc22d87cd08d6116bd693dd3428}} has brought substantial improvements on a wide range of ABSA tasks in recent years. With... | i | f76fbf5f6843752a7b113355e9d64f73 |
To reduce the computational costs of SDA model, Chen et al. {{cite:1c2e41d154c1a960415729ffd7ff5663ef841554}} introduced a marginalized SDA (mSDA) model to denoise the marginal noise with a closed-form solution without using a stochastic gradient descent strategy. Multi-task autoencoder (MTAE) {{cite:7efe35529cb28a73f4... | m | 7d4133ef9e593a4b817829693a103f3b |
In this work, we use the microscopic Monte Carlo (MMC) method to perform model simulations.
The MMC method is a rigorous approach that can treat the finite size effect.
In this section, we only briefly introduce this method and refer to {{cite:fe573a1325079fde818504db2b0c0ee26752f946}} and {{cite:afd165337c9fb0c10e6b84... | m | 3de713c4ac97e56466c2b83e83aff0e7 |
A large spectral weight in the superconducting state can cause an instability even in centrosymmetric systems, when the inversion-related pair creation of Bogoliubov quasiparticles is allowed by multi-orbital effects (i.e., when {{formula:52dce426-b873-4e8b-8bc9-7adf1931c2af}} ).
In this class of systems, Bogoliubov Fe... | d | 818b37c5f73673715a931bd70b02ca04 |
In order to further illustrate this effect, we also considered other
analytic profiles, which were proposed by {{cite:d9f3e262d2efda880152b95270b9614fc326cd77}} and {{cite:a7cada76d20a73bb20c51a5efcbd61f48791c6af}}.
Also in these cases, we note negligible effects on the Doppler
factor evaluation, obtaining relative abs... | d | ca597695cbaa891d6fb65909c7ac59ba |
Finally, an interesting open question is to provide perturbation results for other algorithms, such as (quasi-)Newton-Grassmann method {{cite:1eb1a7a094b2a7d4d753522ee8e45afcb37c48bf}}, {{cite:d04a5a63e0b968e18067c6a16edcefc580cd9f85}}, geometric Newton method {{cite:3a5dd1142033f758befef017c3a5f4053abb4650}}, Riemanni... | d | 09f88426b9da750538507907b0455f01 |
The {{formula:e9aa390d-b5cf-4a90-912e-22e3e9047014}} -analogue of Beta function for {{formula:93a0f77c-30a4-4c34-afc6-d845ff59617d}} (see {{cite:0e2dcd86b7c8fd5a9a24fe59cad9bdbb9300ca01}}) is defined as
{{formula:765f729a-9420-4919-a094-66f29db53055}}
| r | 1365520f37e6998089d6ff5226531595 |
Under the assumption that the {{formula:13130c00-5f43-444a-a231-0058b8de6f4b}} is a measure proportional
to the smallest causally-connected structure associated with a GRB
light curve, it is then possible to interpret the scaling trend in terms of the internal shock model in which the basic units of
emission are assum... | r | b305641d10e66e39229213e6170d3615 |
where {{formula:22ab5059-fb68-4939-ad1c-95caba179358}} accounts for the isospin admixture within the modest shell-model space, while {{formula:f3722d55-3b71-4699-adcd-3b9f70bdf1b8}} accounts for the mismatch between proton and neutron radial wave functions, simulating the admixture between states that lie outside the... | i | 1913d803e9ea107624295d27b66f4af3 |
In summary, by applying the first law of thermodynamics to the
apparent horizon of a FRW universe one can derive the
gravitational equations governing the dynamics of the universe in
a wide range of gravitational theories including Einstein
{{cite:56e8134c8ca62edcbd6e21cda858d540121cbb71}}, Gauss-Bonnet, Lovelock {{cit... | d | 3f5086e29b756fbd26a7865d6dd31e88 |
AI agents capable of having human-like conversations find various applications such as providing an automated help-desk for customer service and technical support, serving as language learning tools, personal assistants and as a source of entertainment/recreation. The research community has accessibility to a number of... | i | 1fbce54f6f7e7f04c108007a8afdc0a1 |
Counting-by-regression {{cite:00ae20ff1b1d22d758b2f01ee4e53a7c093b07b3}}, {{cite:cfbb5f15daf02b78f679d13f5e51b7c9e497160c}}, {{cite:84314f25c2559f5018bc5ef3f54ad462809ab158}} schemes learn the mapping of the input image or patch to its crowd count, whereas the density-map estimation methods {{cite:75241d26c54f671e510ee... | i | 19663d645b1d415d3eecb94639363348 |
The SemanticGAN {{cite:9accf92ff4115d160420707d3a59601b9dbaab6d}} authors hypothesised that modelling the joint distribution {{formula:d0c54d35-cd28-45f5-81be-fb4c4d18b412}} of images and segmentations yields superior robustness to domain changes in comparison to discriminative methods, or methods only modelling {{for... | m | 3cd45dbcffa087bed673f241f5de435b |
Elastic weight consolidation (EWC) {{cite:61671df2eb65e910526e641bb5f3dcc9d8d17e2d}} is a pioneering and the most cited regularization method. EWC quantifies the importance of the learned parameters by estimating the Fisher information relative to the objective likelihood and preserves parameters with high importance v... | m | ae52815bbda0b7a8e38c32030f8177af |
The pure YM expansions of EYM amplitudes, together with the gauge invariance conditions of gravitons or the cyclic symmetries of gluon traces, induce nontrivial identities for color-ordered YM amplitudes {{cite:298628e713cbeacb50cd9a0804391909d44ac7a1}}. These identities guaranteed the localities in the Britto-Cachazo-... | i | c6a1685ac74765c147ef1567aa32a0a7 |
The SoMoF benchmark {{cite:8b312c7b0eeff21f9ed69aa4b4bfcdba46f5c0b8}}, {{cite:0bbcc1d4f5f7dd26fa8fdc7e9e0c41ab6ce8779e}} provides a benchmark for multi-person human pose trajectory. Each sequence has 16 frames (1070 ms) of input to predict the next 14 frames (930 ms), where each frames consists of joints positions for ... | r | bcb9209e5c7bfb934105ba8448cd17ed |
It is noted that these rogue patterns as reported in this article are universal when the {{formula:d7fb2b31-e3dd-48c0-9032-d41305037027}} functions of the underlying rogue wave expressions can be expressed through Schur polynomials with index jumps of 2, which is the case for the three integrable systems of this artic... | d | b339a552898ba52e91a6584710c40704 |
Baselines on carton datasets: To establish baselines, both RetinaNet{{cite:ca6d30ba4487959bef16fb47e1ec3758a22eada7}} and Faster R-CNN{{cite:6457c633cc4c8939a98b53591079bd25918aa5ab}} equipped with ResNet18 are employed to fine-tuning on training set of CPLC and test respectively on 500 images from ECLC and 492 images ... | r | db8a343a514bdbee708d7d56e242aa29 |
Proof. Since the sub-problem of HOOI is the rank-{{formula:c8dffc95-f32d-4938-b838-b252f9bef82e}} approximation of {{formula:e062dbbb-8b61-4f1d-af8b-d0ae5bba133a}} , and by the Ecart-Young theorem {{cite:54bac1b1336db00f10d9a95d5d6139c6ef844f10}}, we know that the rank-{{formula:e819b852-271a-45a0-8072-6c13983d825e}} ... | d | 53d9795531b07ed069d5939013231d4f |
This is the condition of uniform negligibility of {{formula:aab01cfe-7684-4829-a867-1ead4e4125e9}} in the sums {{formula:f3eb9af9-8ce6-4e76-bed8-e44ce195475c}} . It is known, that under this condition and (REF ) the limit distribution in (REF ) is self-decomposable (see {{cite:64f01fb6c162cfd8f0bae1118611dbbebe5930a0}... | r | 9b4d8f89d3ed819794760fac0c426e3f |
This specific choice of the rescaling comes from the fact that the macroscopic density of {{formula:6601b462-6c89-4dec-af50-ff2326a027fd}} with respect to the area measure is {{formula:afccdee6-38ed-446f-8fd9-469964f75d43}} see {{cite:243f7569f9e0972cd41af622b8e989d7861d9e87}}, {{cite:de2dfdb58cb0fd60fb940e53b5d4b1db... | r | 593a7d90bf05a91d95bf5d0676479c9e |
In table REF , we list the masses, decay constants and total widths of the mesons involved in the quasi-two-body decays.
We take the masses and widths from PDG {{cite:232e6af604a335f7957ed471ddc99ead75aaf795}},
use the decay constants updated from the Laplace QCD sum rules for the light and {{formula:a35c8f79-b432-45d4... | d | 0a6bf2dfd91e6e6acf24beedd2f9b692 |
There exist two systems having two interacting electrons, i.e. the helium atom
and the lithium ion. In these systems the external potential acting on the electrons
is the Coulomb potential of the nucleus. The Schrodinger equations of both systems
do not separate into the center-of-mass and relative motions,
and in orde... | d | d432c3bac49d7a8a64aca86651c9affc |
Thus, the matrix in (REF ) is nonsingular if (REF ) is valid. Now, applying Theorem 2.1 from {{cite:76831462f43a204a970d1beb9884347402031f31}} to the parametric generalized equation (REF ), we can assert that if (REF ) is strongly regular at {{formula:ac1ad935-4e74-48c7-ad4a-027b7f538a36}} , then the implicit multifunc... | r | d832d2c28ca67a9e6f88c4e528447ce0 |
Given an {{formula:f381c786-1fb0-4407-a365-d80d21905645}} -partite generalized GHZ state {{cite:42fe96b413644790c359da95aef1a1e021322519}} {{formula:16772935-f553-4182-981b-423a46bc6ad8}} with {{formula:295b6f41-393c-4823-8eb5-2e8607f47738}} , the corresponding Werner state {{cite:8f27d139007d00ccfb21f1da97c3b21ee9c18... | r | 1023ba56de9c0f920ddaf6a7029ec6f3 |
Generally, when it comes to a learning problem, there are two most important metrics that people care about, which are directly related to the optimizers being used.
One is the convergence property such as convergence guarantees and convergence rate, which offer insights about the fundamental quality of the optimizer i... | i | b0f14c07123a9279f713e17b43ef1b1d |
In recent years, end-to-end (E2E) modeling for automatic speech recognition (ASR) has been intensively studied and significant progress has been made (e.g. {{cite:7eed136a36642c045b8a79d8ace13f9134b3293f}}, {{cite:d294c5bf3e957bafe8d737ab79d039f6efad9502}}, {{cite:45234ea3cc17af29f6c3c96ae91c33c16bcbd7c3}}, {{cite:e268... | i | 3f4d4363c9e3beebef12154c531a03f8 |
blueNote that problem (REF ) differs from unconstrained distributed optimization {{cite:2db15859d38d392a49163086a4de7ab14adb8341}}, {{cite:44f2007042b4b3a44c40e29727cd737a716151e6}} due to feasibility constraint {{formula:d14480dc-2655-44f9-b2ba-ffbab3a1285d}} which is of dimension {{formula:f1d64685-95c7-457f-9b18-46... | r | ee96dacc07119aff254eb157fdbf6be9 |
While preparing this manuscript, we became aware of a
recent preprint by Gao and Remsing {{cite:4f28897bec223c3ce5244c6a8c95442ccecdf3ac}}, in which the
electronic structure information encoded in the Wannier centers is used to construct a ML model of the PES including long-range electrostatics. This model, called self... | i | fd03bb7aa64c0787b5f6895e11f5f989 |
The 22 {{formula:641e43c3-4793-47a1-960c-6871a53a43b2}} m flux density yielded a SFR that was {{formula:441f897a-4f49-401c-afdc-99127ed14318}} 3{{formula:a201dded-a023-42cf-b1de-e18f0c080b45}} higher than the H30{{formula:40bcda56-4759-4296-817b-57f76899d9ac}} SFR and is also significantly higher than the SFRs calcul... | d | eb411b3d045a3e829bdcd139bb7e3da5 |
To overcome such computational limitations, a new type of computation technology known as the Ising machine was developed. In 2011, the first commercial quantum annealing machine was presented{{cite:3808b0afe6382ac4a337ea72ffea9f75e045c1be}}. The hardware of existing quantum annealing machines has been developed based ... | i | ea83bb09d57a7e61cf712cf2f9c170e4 |
Non-classical states of light, e.g. entangled photons, squeezed light are ubiquitous in optical quantum sensing and quantum communication and simulation. These states are conveniently generated at room temperature through parametric down conversion in materials with second order nonlinearity{{cite:9cab278be26051ce4d972... | i | c800b68b99874f22a42ed60c0ee6c8c7 |
The base models are trained from scratch for 600 epochs to ensure the convergence, which is longer than the usually adopted benchmarks (160 {{cite:33f1cf4c75aec695dfd8918a419a2ea779219750}} or 300 {{cite:8dc64929557422f9e9175d160e4aff318aa92ac7}} epochs), because we expect to perform pruning on a fully trained base mod... | r | 97f43ba175a6da840131ae74d16f6e40 |
It is interesting to investigate {{formula:03235eab-01e3-44d2-a1d6-1b778b9d8545}} in the {{formula:28dfe270-a353-458e-9b5b-97f67f6427a9}} plane. Not only have many researchers recently paid more attention to measuring
{{formula:e9dc221d-61bd-4f42-b42b-c27d503104ee}} in that plane{{cite:be574aff5a1afc6a2e8cc6bf202f09... | r | 2688ca8a780804e3a3bc4d3b0e9dcd41 |
The interest is growing rapidly in unmanned
aerial vehicles (UAVs) for applications such as monitoring, surveying, precision agriculture, construction, remote sensing, or product delivery {{cite:acc32da7da402984acd515567bb2884a1504247d}}, {{cite:dde3a59c26da27eca7f8e692a3d90a01dc4eeaaa}}, {{cite:4a49686cc17c18adaa83f0c... | i | 9db960b8e79c351ca104dc00a0d9be23 |
A common assumption behind all these methods above is that the operator in VIs is Lipschitz continuous and monotone. However, modern nonconvex-nonconcave saddle-point optimization problems such as those appeared in deep learning go beyond monotone VIs and hence the existing results fail to apply in non-monotone setting... | i | 96279d08089425da4d25d87e4e3dbc67 |
Deep learning based top-k recommendation algorithms significantly improve the recommendation performance and become the mainstream research direction in recent years, especially the collaborative filtering based methods. These existing algorithms extract advanced semantic features and perform complex feature interactio... | i | e288752e2368f39bee2796d25b4552b0 |
In Sections REF and REF we presented two new survival methods based on case-control sampling and neural networks: a proportional Cox method and a non-proportional Cox method, which we will refer to as Cox-MLP (CC) and Cox-Time respectively.
We will compare our methods to a classical linear Cox regression referred to ... | m | 63db3022b85bfe552430222b334d7b58 |
In contrast, R-NDF s more accurately localize the task-relevant object parts and assign coordinate frames to these parts that are consistent with the demonstrations, leading to the highest success rates.
Consistent with {{cite:15ca5e13600de5cd8a521ac0505378723bdb4bfd}}, the performance gap between the “upright” and “ar... | r | eea33e3c650dd80e4eb70b34b7578c5b |
There are number of review papers devoted to the phenomenon of supersolidity
{{cite:d8cc10ab4516ad26fcec29e7927cd986c90f65b4}}, {{cite:12506eb05e95975a626b3ec0eb27099af26bf9f0}}, {{cite:439cd041c471806095b3b666e4f658a42f5198d2}}, {{cite:d5e7dd9bf6617e74f9e7e9d3a6e993a16614bd3a}}, {{cite:f87b228110f30fc3ded4cfe984deb7d9... | i | bde5bce09c2d81ac90c628da9fc6244e |
In recent years, the performance of automatic speech recognition (ASR) systems have seen dramatic improvements due to the application of deep learning and the use of large-scale datasets {{cite:f61608e4b831d8af86439669e92e894b5cc49770}}, {{cite:9533bb77fad8cc6585d9374b465672802ede1fbc}}, {{cite:3f5bff2840364a2a62c560a3... | i | 3e7c101c3dc11a7f7545b1f9f16a6774 |
{{cite:5a9088a3c6cb34846b66c04ea8ae6ce62145928a}} compared the performance of the MBL, AML, and GLM models, as well as a logistic regression model and a recursive partitioning tree {{cite:54b19d1cb26e68f2b86b7bc305fc63985334a95f}}, on the task of predicting whether word-final obstruents in Dutch alternate with respect ... | i | 89e665570f268ddd660f8d35137882f1 |
Now we will discuss the physical understanding
of charmonium suppression due to screening in the deconfined medium
produced in relativistic nucleus-nucleus collisions. This involves a competition
of various time-scales involved in an expanding plasma.
From the table I and II we observe that the value of
{{formula:6cf6b... | r | d970df2f371165606523327b93287eb6 |
I wanted to produce NFAs rather than NFA-{{formula:a584f823-ca56-4903-968a-dfe34479eaf8}} s. In large part this was due to my desire not cover the notion of NFA-{{formula:d0eda7a4-2468-430e-8807-db6321b4c178}} . The only place this material is used in typical automata-theory textbooks is as a vehicle for converting re... | d | 20a751b22a93f81465c7b35913a5866a |
The extinction-corrected H{{formula:b7144bfc-484d-4fbc-81af-b587887b4da9}} fluxes calculated by {{cite:97ef6f675b6905bed0953e367937c938c8b4be3c}} using Pa{{formula:5bf7068a-19dd-41c6-80a3-03e850edeb90}} and Pa{{formula:0464588b-35fe-47ec-b437-47f88378286b}} line data yield SFRs that fall within 25% of the SFRs from ... | d | 7267957b272e14f38b0afe6d45855bca |
In early 1983, Yosi Avron told me about the paper of Thouless et al. paper {{cite:8f2b6de3f1924a12e625cead495b758e9f8799d7}} which gave a novel explanation of the quantum Hall effect, a subject that had fascinated Yosi. The striking aspect of that effect is that a resistance was quantized. In the TKNN approach (we quic... | m | 26ca7334254a7f8c3b46f6c0066dec60 |
All the results reported in Section REF were obtained using their original fron-end methods for global image description. SeqSLAM used classical sum of absolute differences (SAD), and the others (Delta Descriptors, Baseline, and our approach) by default use the best NetVLAD model as we described in Section REF . In Ta... | m | fa56f8884abf01f6b459e3875d4185db |
Iridium-based materials with strong SOC host a variety of exotic quantum phases but also properties of interest for applications {{cite:4e2727f3f2b29f3da715988ae503e4d78a4ff3e8}}, {{cite:416d21b210284695158aa962db5a19a1a318c767}}, {{cite:3ceb45ddfb8b224b929bb4c38b5d26eca7e53eee}}, {{cite:680b6d78c4ad19aa84d1095eb3ade27... | i | 203934d1922515e44011fdcb0460f404 |
Theorem 4.20 ({{cite:1452cebfdc4f0c400e24548496211a897e570cb9}})
Every {{formula:5c9aa328-55d1-4fbb-9a97-28e48f2a3e84}} -polynomial {{formula:46bfa7bc-413a-43b1-b93a-3b2fbcd2b120}} has constant term 1.
| r | c24fb177729627538bb5997b31c05e12 |
Since {{formula:11bf4af1-25ce-47e8-9c9b-1dc2ee9d8b07}} performs well on its own, we question the need for WAIC since the AUROC using WAIC lies between that of {{formula:464e2d81-a199-4252-8e78-55297f58898a}} and {{formula:c68fcc40-17d2-430e-acdd-e7be5567871a}} in most of the results.
Moreover, different from the imp... | r | b86fa864c3d4515e1bfb7647f682478d |
Our findings show that some models are more effectively robust than others. Namely, zero-shot CLIP models have much higher accuracy on images with lowest spuriosity than their performance on the images with highest spuriosity would predict. After finetuning a linear head using ImageNet on top of the fixed CLIP image en... | r | 4261a5e693eda13e6b289b0f95901571 |
CoOp + GM applies gradient matching method {{cite:f0d4ff94ca44e869112e4410ff9bc7f2cf0622eb}} to CoOp, i.e., we not only project the {{formula:100925f5-036b-424c-83df-8e767e5e1d0a}} to the perpendicular direction of {{formula:febf4fe7-665d-4ba1-b8e0-9238cd23fe7e}} as the updated gradient, but also project the {{formul... | r | 28dfa56d78832cf68bac10baf447519f |
There is an immense literature on this subject's intrinsic difficulties and attempts of regularisation of apparent divergencies in it (e.g., see {{cite:b5d97cf85cb8f389e493223f45855fade5c75384}}, {{cite:b159e2419128c6ba4f90d9cad46175b1afd6d129}}, {{cite:395365b788376ecc24357e51877af32280426697}}, {{cite:fc92109c07f24... | i | 594d1d72a2209c00b4633b4f2b3d1880 |
As the combined configuration prevents the average reward from dropping,
it is particularly relevant for real-life applications when a Grover search (with its intrinsic overshooting drawback) is implemented.
An average reward that saturates at a high niveau without a subsequent drop can also be achieved by employing ot... | r | 86de992d349638fd8b746f584e0ce72a |
The consequences of {{formula:1b68ac99-dfc4-4657-869e-fad41f54e3ed}} and {{formula:ea8a1f1e-9216-47a2-ac04-4588e3914816}} at {{formula:8b52e85f-bc8f-4444-b30f-6b2df9ba5cd4}} for the contact interlayer tunneling term –independent of the spatial gradients of the atomic displacement i.e. to zeroth order in {{formula:4a... | d | bfbd5a25d811fa5393e83e24fa60ef04 |
Supplementary Material
Cost Sensitive Learning in the Presence of Symmetric Label Noise
Proofs
Proof of Theorem
Let the linear classifier be of the form {{formula:9062d7b8-6674-4b9f-95eb-0693f6fece14}} where {{formula:f9f6ed42-237d-48a9-9388-949f3747c6d0}} and {{formula:4d885ece-3a6b-4fce-b5de-a81b9f32dcfb}} tr... | d | b5460292bb225538269995dcade8d47c |
Studies related to the pion form factor within AdS/QCD program were motioned in Sec. . For sake of completeness we will present them again in Refs. {{cite:9d729b60f7ca748bc60be527737839b0e62eeee7}}, {{cite:e72f98abe92237c2779b8cc483226c322cae7129}}, {{cite:7fdda353562bc0c0f38d47bfbb3b912a7be47f8b}}, {{cite:83044688718c... | r | a2d6e0df2944234d632d9d7434170ca1 |
Binary code scale and diversity In our experiments, we adopt AnghaBench as the only dataset of source code and use the compiler method to get LLVM assembly code from this dataset for COMBO pre-training.
While it has been an extensive dataset with one million C programs compared with other datasets in AI-based source co... | d | b2c11c9aa5cb50dd5ae397efe560fc0a |
Please refer to Kurtz {{cite:19dfd5857a4f0465534553b071bea825afcc03f4}} for this lemma. We give a proof for the readers' convenience.
| r | 04fdac5454e490bee8c311dd21c534c8 |
Overall Architecture Fig REF (a) shows the overall architecture of our proposed method. We first train an in-domain intent classifier using IND data in training stage. Then in the test stage, we extract the intent feature of a test query and employ the detection algorithms MSP {{cite:f3de616bbbbff27df9ed2ffb94443f14cfe... | m | 141e4ab1f7dc72f156457d21b537f529 |
Appendix 2 shows that this expression, which corresponds to a Bayesian {{formula:53aee991-c9f2-4364-8f19-7426930c2d21}} -test (cf. {{cite:8b2dd90ac5a8c97a824b853807ea717917efc737}}, Eq. 6, and {{cite:467dcdc8dd76a05a8ae0ec0cce29493ba8ee0635}}), approaches Jeffreys's default approximate Bayes factor (e.g., {{cite:af806e... | m | c11d21c9a66ad93b08ff36095cca88e5 |
astropy {{cite:e9843ce13037dd97e97de9e359bf8b37e135936e}},
Dolphot {{cite:7bf396ed269d0486db96dfbd1ab22a4242f4ad5b}},
Tiny Tim {{cite:c146d3fc16550505ba82627f30ee9e8b895001a3}}
| d | 16e3378195f1f1fa73e7a2444f519fc1 |
We also evaluate the performance of Seq2seq / transformer-BERT to better prove the merits of using fastText as a semantic-neighbor word prediction model. The performance of them is shown in Table REF in terms of accuracy metrics, Ent, Dist, and Sen(the average of Ent/Dist/Sen-n and n=1, 2, 3). EA and BERT variant show... | r | a3b0187e946974abb650eaaa3324670c |
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