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Initialization with SDEdit. UniTune is complementary with SDEdit {{cite:48af73137dd044eda4887177affd085fd0218e41}}, and we use SDEdit in many of the images we generate. Adjusting the starting iteration when rendering with SDEdit allows us to balance between fidelity (faithfulness to the input photo) and expressiveness ... | r | 02fd7ab4a7c0f4db21d28025e7068b6b |
Operators, mapping infinite-dimensional Banach spaces, arise naturally in the study of differential equations. Learning such operators from data using neural networks can be very challenging on account of the underlying infinite-dimensional setup. In this paper, we analyze a neural network architecture termed DeepOnets... | d | a98af7c66246bc69cf54e3caa6bc2aff |
Nowadays, convolutional neural networks (CNNs) have been widely used for remote sensing tasks {{cite:1dba0b976c41269d8674c250c95c7e84ef6ed5a8}} {{cite:dc14bd0b854f358c6673bc6578f854ba3a2d05ec}} {{cite:129f46adf72af4ffb3057caccc74a4740b5a46fd}} , as they surpass conventional methods in terms of accuracy of efficiency. C... | i | d7d316cc1beff2bec9d21850803f1125 |
This mass also follows from the energy-momentum pseudo-tensor discussed by Weinberg {{cite:5a3f8b26f1af0371c5cb96e419ba5362450432b8}}. In this approach the metric is written as
{{formula:e0b21380-68c2-4fc5-a224-a52fc02523a5}}
| d | 64cb1c7064e70b46684e3f61e4033ed8 |
For quantitative evaluation, we calculate Recall@K, which is the fraction of times that the target image is included in the top-K retrieved images.
We evaluate on COCO {{cite:9cd3fb39dc917a61b4212c4e331ea1c82ff6e60d}}; a commonly used datasets for language based image retrieval.
We use the same data split as proposed i... | r | 679da8b713a5a380aa8f1d70ac4e32bf |
Evaluation Methods. To the best of our knowledge, so far there is no exclusive metric for the quantitative assessment of 360° SOD. In addition, the only structure-focused SOD metric, S-measure {{cite:ad29e94c2703d08218746791c63756ca6e89d358}}, may not well adapted to the evaluation in ER images (Figure REF ). The ER im... | d | ceb2a13c148c8b9f43ebe7a9ea747fa5 |
In order to better understand the viability of implementing these quantum walks on real hardware, as well as scalability, in this subsection and the next we present experimental results gathered from one of IBM's leading quantum volume chips: Toronto {{cite:082fcf21657030415fda0c99af115f09a89f9515}}, {{cite:d63adb68fe1... | r | da34a0566412e00b348dc58e803536a2 |
In this section, we report experimental results of our proposed distribution-based domain adaptation approach (DBDA) and compare it with different baseline approaches. We show that the proposed DBDA approach improves on state-of-the-art performance when evaluated on the ISPRS aerial benchmark. The baseline methods we c... | r | 193251c6771487545b8cff90c0ee400d |
Diversity: We first look into the diversity of content selection learned by different models. For each test data, 50 selection masks are randomly sampled from the model's learned distribution. Greedy decoding is run to generate the text for each mask. We measure the entropy of the selector, proportion of unique selecti... | r | 5e466d19f2381d08f8ead37f37769ed4 |
In this paper, we provide the first sharp blockwise perturbation bounds of HOOI for tensors with guarantees for both tensor reconstruction and mode-{{formula:fa576183-791b-47cb-8826-dbc5aff517a5}} singular subspace estimation. Furthermore, we show both HOOI and one-step HOOI with good initialization is optimal in term... | d | 3ff3e0c35f07bf0c8db5ca7bf7bc31fc |
Invisibility has been one of the most challenging effects pursued by humankind for centuries. The possibility of hiding objects to the naked eye has recently leaped from science fiction to a feasible reality thanks to the advent of metamaterials {{cite:9f7e7108c95dedc16bfe767727292ff343479bc9}}. Among a wide range of a... | i | 1de203e1f2d935e937f752edf29f25ee |
The calculations are based on density functional theory method
in the generalized gradient approximation with the Perdew, Burke, and Ernzerhof functional (PBE) {{cite:f6ad0b9c635f27e4745ecd23dc1fb08817d06c28}}, as implemented in the Vienna Ab initio Simulation Package (VASP), which employs a plane-wave basis {{cite:bcd... | m | b454e9ba81385ccb3f591eba52ad71fe |
For baseline methods, we use official code packages from the authors for MVGRLhttps://github.com/kavehhassani/mvgrl {{cite:f27e2b112d9fa9a017f611673319f4260e23e041}}, SEALhttps://github.com/facebookresearch/SEAL_OGB {{cite:35329449406a77be2ca51dfa48bfb92a2b7c9e7a}}, and LGLPhttps://github.com/LeiCaiwsu/LGLP {{cite:25b5... | m | 57b070550da666297343ebbe7d78c6ae |
Nonetheless, instrument tracking methods are often deployed in difficult scenarios such as bleeding, over or underexposure, smoke, and reflections {{cite:e5c1bd213488bec8a3f4f07c185cbed7882a2214}}. The net effect of these issues increases the missed detection rates in endoscopic surveillance, hampering the adoption of ... | i | e96b009e2999aa61107b92e8737a7e8c |
In this paper, we show several ways of how FM combs can be influenced and optimized.
One of the main goals of FM comb engineering is the control of the number of comb modes in order to increase the optical bandwidth and ultimately facilitate a broad ruler for dual-comb spectroscopy.
The presented guideline explains the... | r | f1502d554509a9b4f9bf302991bd6cf2 |
This work (among others, such as {{cite:169aa454dbb2dbd774753bc836255dc6a3a9c075}} and {{cite:0fe97bfc23b11ffab96df40fb144b558ac54fff2}}) reveals that deep ConvNets can be extended to 2D and 3D medical image analysis tasks. We demonstrate significant improvements on CADe performance of three pathology categories (i.e.,... | d | 4a3467ef06c709bbb946ba0d6117351f |
Many feature importance scores are inspired by, or derived from, Shapley's work on fair allocation in coalition games {{cite:722bbdc117d480f8f951c9170c6f5f74ef388222}}.
In these, and other common methods, the importance score is computed in a two-step process. First, a value-function is computed, assigning a value to e... | i | 652d4b6616a3d1977675adefce3005c0 |
where {{formula:2209e99a-8817-4039-a541-64f4a3248221}} is the number of {{formula:ff7182ec-6246-4e92-a01f-e83cb4fcb7bb}} points in the frequency grid and {{formula:7301eb16-b401-4151-a7bf-bf9c2108411e}} are the dimensions of the 2-d lattice. For Fig. chiJJT297K, Fig. chiJJT62K, as well as the reference uncorrelated ... | r | b13ff19feafe8c39b0308060394ca6ce |
Comparison with Other Methods: Firstly, we compared our method with seven scribble-supervised segmentation methods with the same set of scribbles: 1) pCE only {{cite:9469991520ec593fc5248e4f7b5051241df42abc}} (lower bound), 2) using pxeudo label generated by Random Walker (RW) {{cite:acbde7a9ee3f72f7200c5780691e2ee0f0a... | r | bf82a55b7308b33a795c318c796b8814 |
Our implementation is based on the open source code of PC-DARTShttps://github.com/yuhuixu1993/PC-DARTS. We set SNAS {{cite:5fc9145041872ce870aa4ed7e5562b7b3261db43}}, GDAS {{cite:b96cf4effbd1a3214133723c5434357f390382f7}},Fair-DARTS {{cite:09af85190289d65e08690afdb9eb71dc5a699023}}, PC-DARTS {{cite:7fbcbc4cd9fed585b39f... | m | 941338fa265365259f636130bcb0ef24 |
Earlier work by {{cite:48ffa7778c331e1bfed8b5500edcf5ff9681c519}} introduced the generation of malicious biased perturbations at each pixel of the input image in the direction of the loss gradient {{formula:bc71e7b4-d0df-4d70-811f-c22d119acb1e}} , where {{formula:9d98d6a2-ec64-4a67-8968-631e337b4dcd}} is the loss func... | m | 6c183bc7e61e6c31a76696d60164ec0e |
It seems reasonable to assume that however {{formula:f5e998df-d611-48d7-8839-03f9b7cf07f3}} Fe was delivered
to the ESS, many other SLR were incorporated in the same manner.
But if this was an unusual occurrence, then most planetary systems
have lower levels not just of {{formula:2c89f81e-e18f-45f0-bf87-25a4ffcf2983}} ... | d | 1a34293bba45c4c7d3f424c5fd6ac33a |
In summary, we have proposed a scheme for quantifying entanglement between two two-level atoms based on the measurement of zero time-delay second-order coherence function of a cavity field in which one of the atoms is dispersively interacting. Using our scheme, one can measure the concurrence of arbitrary Bell-like ato... | d | 95911824cbb26e756735e18581b63e52 |
The basic parameters involved are listed below. The training sequence is Zadoff-Chu sequence {{cite:5905a211d471c7e1b056277f65041a617f47cab6}}, {{formula:6556d6a5-7de7-436a-8b3c-a373e482a0ba}} , {{formula:f1eb6202-1e0f-47b3-9518-dce70bc47f01}} , {{formula:ad586052-27b6-44d5-af78-9d8c0564c50c}} , and {{formula:31b2c4d3-... | r | 5839048c9fd851dfaa2409deef7dd69b |
We conduct experiments to explore the outcomes of excessively optimizing IR algorithm performance on an IQA metric by generating “counter-examples”.
Conceptually speaking, even one counter-example is sufficient to disprove an IQA method as an IR metric, because algorithms may take advantage of this vulnerability to ach... | m | 1c73c736ee0d8bbde6054fa510375281 |
We would like to mention here that in PCMAO, the suppression of the transformation rate from the high-T AFM to low-T FM phase across the thermal hysteresis [see Fig. 6(a)] by P is nontrivial. Because, as P increases the transition temperatures, the size of the critical nucleus of the FM phase at a particular temperatur... | d | 49d4afcee483b1184c367f5580de4e18 |
where {{formula:e0d027bb-d7f5-418b-b411-8b705c4016dd}} is
the reduced mass of the complex, {{formula:4de7d682-58f3-4f6c-b98e-06da51161aa3}} the CO{{formula:3c399ad0-8b60-4451-b617-a05d408faf99}} monomer
rotational angular momentum operator, {{formula:72fb696b-a293-48fd-877a-1521a6961024}} the total angular
momentum... | m | 4b34e87dadeb762c2dd81ad62986ccbb |
The solution to this problem is the Mittag-Leffler function {{formula:870428d9-3049-4165-bbc8-01252c521cb6}} (see Diethelm {{cite:6ebb8bb887c57ca9db9cd048609971559ed3670f}}), which is clearly continuous on {{formula:2fecdedc-b63a-411c-b2e3-edafe5556c36}} but its derivative, {{formula:d70f8e63-a51d-4ebf-b623-bb0b72c88... | i | 076c4ed7170a952aa8a53b3e69e97b9a |
Changepoint detection is a central problem in fields such as finance
or genomics, where {{formula:5ff64c07-9779-4121-9b3c-62d104a90562}} data are gathered in a sequence over time or
space. Many models define the optimal changepoints using maximum
likelihood, resulting in a discrete optimization problem. Multiple
chang... | i | 146796be615de217c707ca5ccc28ddf8 |
In addition to its performances boost, VBSW brings two advantages. First, it validates an original view of the learning problem, that involves the local variations of {{formula:f76258f4-00b0-4a33-b65c-602749fbb435}} . Indeed, we started from the Taylor approximation, which is specific to derivable functions, and manage... | d | 863988e9e1cecf97c2370b39ad92c061 |
We also compare the performance of CNNs trained by ZORB against a standard
implementation provided by Tensorflow {{cite:2e5385ef454db8b0429f67968be28c24a55543eb}} on a subsample of the CIFAR-10
dataset {{cite:8b26ec18bb5560104fdb01dd7575710487a1bc79}}. Similar to previous work exploring Neural
Tangent Kernels {{cite:92... | r | c000681b29aa549cba0ee14a2df80efb |
where {{formula:89d6d3e6-a2aa-44dc-8d4d-2e08e6b40f6b}} is the desired reference and {{formula:dcee4b4e-65da-4d02-b72b-c47b1752536f}} is a time-varying parameter that dictates the rate at which {{formula:f2d94ea8-174e-41a4-b1d0-9d311ae28e6d}} converges to {{formula:63822e63-b353-4e23-87a0-453c97139dc7}} . The paramet... | m | f81c406c72e3d30d6ea9403fa0c2387e |
However, explicitly accounting for the network defects by use of a worst case delay within robust control schemes is, most of the time, overly conservative: It can often be observed, that considerable delays do only occur infrequently and accumulated in certain phases, while for longer duration the delay is negligible ... | i | c58f53525b519bb1079e09a80ac6d5f7 |
However, the links between nodes of a graph convey specific information which is not properly captured by existing architectures. The weights between nodes may signify the cost or advantages or popularity of a transition from one node to another. For example - weights between two nodes in a graph, with each product bei... | i | 021e458bed7ccb61719002a584219346 |
In this paper, we have studied {{formula:936bb5ee-2546-4124-9553-d65b00b5b1d0}} gauged supergravity in four dimensions with {{formula:8a0566fe-f453-415f-bd32-0491e36b0aec}} gauge group. The gauged supergravity can be obtained from a truncation of the maximal {{formula:1c7c155c-919e-4850-a7ee-1be55f47a767}} theory wi... | d | 966c50f0ddd030f8ef0b4c17ac55cb67 |
DQN {{cite:f9fa9aa42ad82fbc213cdeb681d3043e48ece754}}: Have tendency to overestimate Q-values.
Double DQN{{cite:da84c33c15889ce6cc3bfabea682094e5a778929}}: Uses the target network to calculate the Q-value to solve the overestimation problem.
Averaged DQN{{cite:5b65b2cd686c7ccaf5d796e72ccb3bf001a24d36}}:Provides more... | m | 0dcf2516a766c7d6fade7382f9a3b6ca |
This section summarizes some basic concepts on combinatorial classes and their generating functions that will be used in our work.
Our presentation follows closely {{cite:1a08d9a1d8a2fb5db43b3f9bad68c6bf10728d2c}} (although with much less details),
and the reader interested to know more on the topic is refered to {{cit... | m | 7a7365d5f658a4877d1576ea18d47084 |
In the above example, we have domain shift occurring between training and test time, which degrades the model's performance {{cite:c20148b9e416a68ba1686b92fd40ca72b5b6782c}}, {{cite:4652734031b5044a95370cf30dc0f3a5a48c2e31}}. By explaining the cause of the domain shift, we are able to easily fix the model's predictions... | i | 8d6751db67cfaff5c711ef9832a90248 |
In prior work, Woodbury-based inverse has been considered for the case of a one-hidden layer neural network in Optimal Brain Surgeon (OBS, {{cite:8f114db8cf892fe4710317f3cabe4f21b1af1a8d}}), where the analytical expression of the Hessian can be written as an outer product of gradients. An extension of this approach to ... | m | 81ccef6ceae82eeba606db969976ac1e |
There are several directions which are worth exploring in future works. More complex starting architectures and larger or augmented training sets could be explored, to obtain even closer approximations to CNNs or other more effective architectures. The dependence of the architectural properties induced by pruning on th... | d | 5a652d9bdf04cc47a109a51e3f308d80 |
In analogy to TIs in electronic systems, there has been a great interest in topological magnon insulators (TMIs) lately. Magnons are spin-wave excitations of the ground state of systems of localized spins, and when magnon bands have non-trivial topology (in the form of finite Berry curvature) the same Hall-like transpo... | i | 3587a26c1ca47899c8c87913f6eac120 |
We observe that our eDiff-I-Config-A model outperforms GLIDE {{cite:ecd1088b8339c865f471f8b386eb2183dd92141a}}, DALL{{formula:7e8a5e79-2713-42e5-88d9-bba91ce7cecd}} E 2 {{cite:89c6c59a4aa4f0492d4061db2f5f0ca938726303}}, Make-a-Scene {{cite:fa29ce884f826c65a80af2fa9b6c3b076921d88c}}, and Stable Diffusion {{cite:507dd57b... | r | ad3c4e2ebdacce2ae7d215374cd98038 |
To solve the SBO problem for calibrating the simulation model of the ED in hand, the approach of the Sample Average Approximation (SAA) {{cite:17cb0f9f702bc59f5cc56ce90805a8241121f891}}, {{cite:9ba4e03ee0c6d9e7448c2be5c3ddce66f50a8d60}} is adopted. As a consequence, the resulting optimization problem is deterministic a... | r | 67f4c84e12b47fdd5c17746735c43465 |
In the experimental side, the leading order corrections to the cross section of top quark pair production induced by the top quark CMDM and CEDM have been studied in {{cite:d23ed2cdc81916215aec048db8123a98c1a70446}}, {{cite:efd30e9537d97f094695e95d1c312bb08c128c0a}}, {{cite:a5c0ae3f4fc812994632f89971c3ecb36f3b93f9}}, {... | i | 0418d0f4662f962c4266fa071894f799 |
When comparing common criteria before and after the addition of our clustering method, we observe that a greater variety of filters are retained. As an example, Figure REF represents features extracted from the filters of the first layer of a simple ConvNet, AlexNet {{cite:b5c5af9d9ebec412e6e2aec8e92aed17ebc011a5}}, b... | m | f106d63a0cbe147ca9cbecedabe4c8b4 |
Traditional post-training methods rely on heuristic rankings of the weights or filters to be pruned, often based on parameter magnitudes {{cite:a1ea0c4f4bde715707dbefafd08c87605a0720fd}}, {{cite:f267356d120060be516e91ce93305c86086fa24d}}. Despite their simplicity, these methods usually require retraining the weights to... | i | 63996855ff67eee39e387fbf9db7787e |
Recently, due to its ability to handle the non-convex problem, reinforcement learning (RL) has been used in wireless communication systems design {{cite:835d73c73343e8e748d73103e7a05f0a3ddfc525}}, {{cite:fbe670cb0ee26318f964945cd3b7f05ee6b7d883}}, {{cite:db74a89a955438891edb8110fd09d4c4923e08e0}}, {{cite:a4a28c020355e4... | i | a3cd93f46cdc0df4395a5f4acab8318d |
We are motivated to study the application of boosting {{cite:97bb9b6a25765eeab42676091c0e2056b987b325}}, {{cite:7f16a5ea7b64449ab07040f791c468b06d21dba0}} to neural networks (CNNs) because of the great success boosting has had in conjunction with decision trees and other classifiers. Before the recent explosion of rese... | i | 7df5794aaef0efe548220aef2193a214 |
In summary, we have developed a new framework for continual learning based on approximate Bayesian inference combined with trust-region optimization.
We showed that this framework encompasses recent projection based methods and found that it performs better than naive weight regularization in a recurrent neural network... | d | 4073a00bbf0f5672d5d623f12a0dddcc |
Here {{formula:211d6853-1dce-4eb6-bccf-c1519e75b570}} stands for the exponential integral (see (REF ) and (REF ) below for the general definition of {{formula:e712966e-4884-49da-a19d-a5f9d07105dc}} ) and Ei for the “complementary" exponential integral defined as the Cauchy principal value of the integral (see §6.2(i) ... | i | f62b20aa4c3f5bbaa03e7a7e87bdb316 |
Here, we report on successfully recovering the hidden objects by exploiting the correlation between the fingerprints. However, we believe, based on the spectral ME or the 3D ME, that the technique could be used to recover multi spectral or 3D objects. This is a significant advantage as it does not require the scatterin... | d | 277807b9fdcf9a45688d9c30dd8d6a65 |
To test the robustness of our approach against different data types, we apply our method to two other datasets: Fashion-MNIST{{cite:5481258d24030b36e386c16d9b3a9bd7a3e90029}}, and Human Activities and Postural Transitions Data Set (HAPT){{cite:51bb7efea0d18862931561799d4689afeaa0062c}}.
| r | 3c8baff3f54e47673bf582d7e741d698 |
Steps 1: Survey definition: We established four goals (G) to gather the students' opinions:
G1: Identification of who should attend the SLR subject;
G2: Identification of the contents that should be taught in the SLR subject;
G3: Identification of the skills developed in students who attended the SLR subject; and
G4: ... | m | c381404eccddf017ae561fad7221a8a3 |
is also to be noted that for large enough disorder strength and for weak
magnetic field, quantum Hall plateaus gradually vanish from the higher
energy side. We confirm it numerically. However, it was not the main
motivation of our present work, and the disappearance of the integer
quantum Hall effect due to disorder ha... | r | b9a1538170b82a8ecab8199ac55eba4d |
To further improve the tracking quality, we test different network backbones. We find that the accuracy drops severely when the network backbone grows deeper. This problem has also been discovered in SiamDW {{cite:22b581fdf9cadc92840e5a3ec1acb93e67704f8c}}. One reason is that these deeper and wider network architecture... | i | 14c6b2cff274727fa03305d915d972d0 |
(REF )
This is reduced to the finite-dimensional case.
The inequality {{formula:86708f5a-ef0a-42d4-b353-74a91f2762cf}} can be seen from
{{formula:f8905a5e-5e96-407f-b8e7-ab51242e0d8c}} for {{formula:3487bff4-3f3d-4787-abeb-9cb584a39cad}}
(by the interpretation of {{formula:f413f54b-76e5-42e9-844e-7048d992e02e}} as ... | r | b3b780d498f71d25b64117b3c8ea9550 |
In this paper, we investigate the local convergence of Riemannian gradient descent algorithm in a generalized framework, assuming that a warm initialization of {{formula:b5686818-29f0-48ae-9520-891115d3c27d}} is readily available. It worth to point out that obtaining a warm initialization under weak signal-to-noise ra... | d | 152afd115175e66bbde96e8be414d983 |
Both {{cite:563023082bd5704b7f2cbac32742aa396dca4a99}} and the posterior mean estimator of {{cite:47629da752696929f2b8cfc82d9743ee0b872e4d}} have the form
| r | d6eba9770275d7b08dd0aeb3746a7a77 |
The study of equations for the recursion coefficients for OPRL or OPUC has been a subject of interest. The question of how the form of the weight and its properties, for example to satisfy a Pearson type equation,
translates to the recursion coefficients has been treated in several places, a good review is {{cite:e4c0e... | i | c3d730db3086a74fc69d7216a0aadfb3 |
From performance difference lemma {{cite:ebe7c894696f5327080069fe52eda5bfb64e3b1b}}, we have
{{formula:777c99a3-07b1-4578-b8dc-4d87403c37e7}}
| d | 440c85c5f32ad88ee78deccce5b8d65a |
In this section, we develop two different networks for interpolation and extrapolation. For the interpolation task, inspired by the variational autoencoder {{cite:c694c248ca69e4fc7463c0f8c40ad3fda4e01bd9}}, we assume a linear pattern among samples in the latent space when the CO{{formula:f64b9987-ef86-44c6-bcae-ea0c4cc... | m | 5b3e4cf91c1074821b8fdd46a5b9bf4f |
Discretized convolutions, on the other hand, sacrifice small quantization error ({{formula:8fcf5cbe-f65a-4af2-ba38-8ebcbaccc1f9}} 1cm) for scalability that can process millions of points with faster inference using the spatially sparse representation and GPU hash tables {{cite:34368304366b8cdcff9d3acf6ce7e79ffb67986b}}... | i | 1f80ebdced648cac20a5351bc9fa30c1 |
Kumar, in {{cite:95ae5329ad5ad58582e54ba65fc2683fdaf4c41d}}, proposed a detailed classification and tagset for marking aggression and bias, which included the distinction between overt covert aggression as well as the target-based classification such as misogynistic, communal, geographical, sexual, etc.
While the tagse... | m | a3ece6f898837d71a249f245cd49b249 |
First, the SHAP for neural networks (KernelSHAP) is based on an assumption of model linearity.
To mitigate the problem, {{cite:ff419651c86ddb436b3ed9cfc642ce241d67080f}} propose a polynomial-time approximation algorithm of Shapley values, Deep Approximate Shapley Propagation (DASP), to learn a better Shapley value appr... | m | 7f658d97ed85191e33131fa6edf62208 |
Transfer Learning: We follow the procedure in {{cite:8c2c6571b9c932db0916e4bd52307185cd2f8141}}, {{cite:3fb3f9872746e5f69029be9ada060bad85929822}} for transfer evaluation (refer table REF ). Hyperparameters for each dataset are tuned independently based on the validation set accuracy and final accuracy is reported on ... | r | 782d7d46cf2dd31d115ce8f1fd1cfcb7 |
where {{formula:54b72e12-d0b9-431f-b42c-71dd9400338b}} . By the induction hypothesis and {{cite:d4d52dad822e611da94eaaef3584bf79861097fc}}, for {{formula:7d86922c-6a90-4beb-9949-c7be63e5dc2f}} , we have
{{formula:24a06036-00b3-43fc-b343-75e0561cfb2b}}
| r | 1cd39af645901cdcbd9bf1bfdc7264a2 |
In order to make connection to actual phase transition characteristics, we used two approaches
to predict the GW spectrum. The first one is the so-called sound shell model {{cite:0490148667db58652ec893adb2d10f79793300f1}}, {{cite:48d082244b09068a793ed71bf8f81599defd3f01}} and the second relies
on fits to results from s... | d | d51ccf94977a19db3c388ead414ee18e |
In this paper, we have explored in greater detail some of the ideas
presented in {{cite:3a9069dff1db41c80251b47aee181cd5db8fe885}} for obtaining “{{formula:ebc628db-13c8-4e6d-b114-ddaf9745127a}} supergravity”
(i.e. the low-energy limit of the bosonic string) as a convolutional
double copy of Yang-Mill squared. A cruci... | d | e3c69c8a046f0b2e868320a52c546561 |
Building on our results obtained, we expect to further study dynamical correlation functions for the charge-charge separation theory of FFLO states.
According to the linear response theory {{cite:496c0808c7a9daf326bb12ef9e4fa6c869ebee96}}, one can measure the spectra of pairing and depairing in the system by imposing a... | d | 2c69d851e123793446214f04e63bc1e7 |
the main idea of the PINN{{cite:b31e1b2701306456896fab837f7115788fc2028e}} method is to use a neural network {{formula:6583f400-6a9a-44db-b989-5fed8fa96d85}} as an ansatz to approximate the solution {{formula:2d5dcd45-a163-4b52-aac8-3d773b65b814}} , where {{formula:4f98f70a-ef4e-47c8-89e5-d19bcf4afb29}} represents th... | m | aab584ea6cc5f6b83851b33e5338d5af |
*This section may be skipped by a quick reader. It is only relevant for Sec. REF , where it will be established that frame-dressed observables formulated with covariant methods (generalizing the construction proposed in e.g. {{cite:2d55a3c6ea1a0e41c409f18b97b28a7115703098}}) can be re-expressed as relational observable... | m | b3ac4232e48dc2aed50b27fada6634ca |
The galaxy distribution observed by spectroscopic surveys is modulated by
the Doppler effect due to the line-of-sight peculiar velocities of galaxies, and exhibits characteristic anisotropies,
called the redshift-space distortion (RSD) {{cite:4205a9a833be2382342f8f70de5bdf494c59b554}}, {{cite:3fcbd77e7a0e297089825ccafd... | i | 13aa4d748b554c052fde5bdf8f5af122 |
Superconducting nanostripes (SN) are a fundamental component in superconducting electronics, crucial for various applications in the field of quantum technology. For example, superconducting nanostripe single-photon detectors (SNSPD) are used for quantum communication and applications in astronomy and spectroscopy {{ci... | i | b33bb0441b9141595f3f071020a56481 |
Algebraic automata theory, that is, the use of algebraic tools and notions such as
monoids, morphisms and varieties, has been very successful in classifying recognizable
word languages, from both the theoretical and the algorithmic points of view, offering
structural descriptions of, say, logically defined classes of l... | i | 29e9767ed9f0bb3d649cfff6c2415255 |
Table REF also shows that popular computer vision models such as ResNet50, VGG-16, Densenet201 ({{cite:2eec1003c6127bf1ac2d5976bd45df53a647d50e}},{{cite:17232d379f9712ba4389ca842648879760640c42}},{{cite:fe716fabe897c51adfaf74569a9dca234a7e9897}}) achieved relatively inferior performances. It might result from that the... | m | 935861da6dfc8b17bd7de4c8aa9fdd55 |
Motivated by the recent progress of self-supervised learning for unsupervised graph representation learning, in this paper, we aim to answer the question by exploring the potential of graph contrastive learning (GCL) for detecting OOD graphs. However, it remains a non-trivial research task, mainly due to the following ... | i | 8b6273e52d8913d772050e63d6032b2c |
In order to elaborate on the existence statement of item (i), we recall the following deep theorem of Sacks and Uhlenbeck {{cite:0b2bd513e2369964250ab5db70bd6823157760a4}}: Given a finite energy map from a Riemann surface into a compact Riemannian manifold, either there exists a harmonic map homotopic to the given map ... | i | 3d296127d35b1eb4ae3bca9b58236906 |
DCD was compared to the vanilla dynamic convolution {{cite:3dbdfe5e7cd679664a4b07ee00767fecce387b2b}}, {{cite:2e8ba7c4f738b448e6e43b093b1fecdc47154c01}} for MobileNetV2 and ResNet, using the settings recommended above, with the results of
{{figure:7cfb419a-e3c4-47c4-9a6e-bf43140f9bb7}} | r | b0ed82301f8f8d3db22b3b13177aa783 |
We performed a Bayesian analysis of this model using the latest version of the Markov Chain Monte Carlo (MCMC) sampler MontePython3.3 {{cite:7dc58cebbe13792325933a8d3a5227e07e1c48eb}}, {{cite:0b3218e21c4c2f0d28de0c235c4d67938ad29c91}}. To analyze the MCMC chains and plot the parameter posteriors, we use the GetDist 1.1... | m | 4d70e79d7b8de0f3a6ed7d81d364eac4 |
In Table REF , different observables fitted in the present
work, their experimental values {{cite:3599cf7e36b2fc06c5eedfd0207ab982d59424c2}}, {{cite:3987f78af4b4147e4dd825a2072efe8208a1dd13}}, adopted errors {{formula:8bb462da-82b9-4b44-aa1f-9a1e581010a3}} on them
{{cite:7aa6f6a89b03a5a408073cd1d9e3da6ebc16b490}} alon... | r | 5e0232148eeadbcac752179016fe91a1 |
An important open problem that should be investigated in future is
delineating standard and robust accuracy for highly overparametrized models. In other words,
is higher robustness of larger models due to their higher standard accuracy or these models have inherent properties that makes them more robust. Our preliminar... | d | dfbb53b3183911107072dc2c9b9abc80 |
We have proposed a probabilistic model that uses a novel composite transportation distance to cluster data with potentially complexed hierarchical multilevel structures. The proposed model is able to handle both discrete and continuous observations. Experiments on simulated and real-world data have shown that our appro... | d | cceb21ee63f359edf32374956a2861d5 |
Another computational topic covered at ThaiPASS was producing a Forward-Euler solver - a numerical method for solving ordinary differential equations (for a full discussion see, for example, {{cite:02af4261638c981dc6d355db01fcd197690b81a2}}, {{cite:87eea780f93cf2c0e7afa98888234c2152e6fa4c}}). Because of its complexity,... | m | 753fb77dfe77682cbe9f1eb1168653ae |
It is somewhat surprising that multiplicative cyclic learning rates are able to approximate Pareto frontiers when constant period cyclic learning rates do not (Figure REF ). Why does this work, particularly for increasing periods? One possibility might have to do with different phases of learning {{cite:c6a4a18e2b12b42... | d | 167918e745f614955cbbbb6a73f49c55 |
Our full method shows improved sample efficiency during the early stages of training as well as better evaluation scores over the baseline. Our model's performance in self-play after being trained also demonstrates slightly reduced variance against the baseline. In ablation experiments, we found that agents that implem... | r | 236423633f65b09eda798c4afb5da0cc |
One example algorithm that takes advantage of these speedups due to batching is a variational quantum eigensolver (VQE) algorithm. VQE is used to model the behavior of energetic characteristics of molecules and is used in quantum chemistry problems for extracting the upper bound ground state energy of a Hamiltonian {{c... | r | 8dc03384a109c2c2474df0279ce6cc50 |
Harris exhibited a simple transposition in the Galois groups {{formula:99a8a536-e5f2-49e9-b446-7ce08ce383ec}} by producing systems {{formula:06531855-2792-46db-9a03-88423aa3515c}} with the property that the fiber {{formula:1e683275-7c14-4603-9062-98bdbde197cd}} has degree {{formula:93fe7061-6acd-4516-893e-d736043d6c... | m | 17c8e9e3b518d8b70aabc5d5e9f528bb |
The result relies on the established Exponential Time Hypothesis, which we recall below.
[Exponential Time Hypothesis (ETH), {{cite:edf46188c7ce45b9cfa5430127c20c5268465624}}]
There exists a constant {{formula:736425c5-a9c4-4dac-a2e6-1c04ef863c89}} such that 3-SAT with {{formula:fe40dfa6-bce9-49b7-9b4e-827641ddcbdb}} ... | r | ca25d0fcec809c482eac523a0375966d |
Using a slightly more sophisticated method to correct for the quark-mass effects, that originate from non pion-pole contributions, the authors associate an additional error of 20% to this result. Additional ensembles are required to perform a more careful chiral extrapolation and first results have been presented durin... | r | 2130e5b11a58bfd6d2cff2550648e51a |
From an extrinsic point of view, in the setting of a minimal surface {{formula:ae571b2b-f15c-4130-af9f-24a5b34f3186}} immersed in the Euclidean space {{formula:220115e9-1f84-4274-b06f-7ac1309b9db9}} , it is well known, see {{cite:eac5f4d42ed0a9a303f1184497c7a94089028970}}, {{cite:0ee55af7b194f1968e9c2a2d89a333d1b79f58... | i | 29364c9d3285bfcdb05fcb45386d7976 |
To better understand how our models achieve consistent improvement, we adopt the gradient based attribution method, Integrated Gradients (IG) {{cite:eb216b18572e2aee258427ed5f8549f2faf2a6dc}}, to visualize how each character contributes to the final prediction.
To make the visualization more readable, we first perform ... | m | 7065e7d03a0edc2b929b40d0d412aedc |
For a general overview of the basic facts about box-counting dimension, we refer the reader to {{cite:b71f314137facee619e97e1728ff80caf79ea20b}}.
| r | 2b2a03c0226bf6336261d50e81585bc0 |
In the paper {{cite:42109aacd29e7248779d42f1437534f6c01a51de}}, {{cite:7a9822014060ef83e43bdd67e056d605bfd656a4}}, it was demonstrated that by combining the information of the Schur or Macdonald index (both of which can be obtained via associated VOA) and the selection rule for the OPE that can be obtained via supercon... | i | ff2d63a0ab652747df4b666fd1f5cee4 |
Besides temporal distortions, the temporal quality attention mechanism also affects the overall quality evaluation of the video. Similar to spatial quality attention that is related to contents of different regions {{cite:4c31ed073b41a17625bf620a0f7383426f25f5bf}}, {{cite:df72c9131fafe1a25f69703b3895afb706d37c88}}, the... | i | ae9618142529e8c5792dda4301e33bf9 |
It is not difficult to obtain the solution of the linearly constrained least-squares
problem (REF ) via using the Lagrangian multiplier method
(p. 479, {{cite:81fe3e36ccfb097d1346de5622ff7f5272d21f4f}}) as follows:
{{formula:1a2f8ea4-525a-4f28-91a1-79273ed8c251}}
| m | 1b0403dab3f0f67647951f69f47a5716 |
As described in {{cite:fb110ac4fa08b39dc66511e0492f8cc9124f8783}}, SSIM is a good approximation to assess image quality from perspective of human visual perception, but this method only considers single-scale image information. Compared with SSIM, MS-SSIM is an image synthesis approach for image quality assessment that... | r | 2b2c0ad66a898c1bce4af1c1332d484d |
and at results calculated with the same potential (and using LAMMPS {{cite:5fe5223af3fa78d3c30c7a2e810bb65eeaab4d6b}}), The blue X data point represents the computational {{formula:10f454be-f77e-4051-b07e-a260e272539b}} result given in Ref. {{cite:6c50260c1e11e2d31d75b20e02e847469380c67b}}
| r | b94f80d39fefc6f24be8fa465885b9f5 |
We made some test runs to optimise the parameters prior intervals and sampling.
The final run is based on 120 independent channels, reaching the convergence criterion
{{formula:f20adc7f-d8ed-42f5-a02d-2d8918b59d9b}} . The {{formula:b9513747-8831-4471-a1e8-bba8655a0be7}} criterion is defined as
the ratio between the va... | m | e25a37b4fc21348c62a8a0117eb8d7a4 |
In this work, we have focused on generic autoencoder networks, as they are popular tools for dimensionality reduction and learning compressed representations of sensory signals in many contexts. In deep reinforcement learning, for example, they are frequently used to learn a compact abstract representation of high-dime... | d | 451d69ef4975748a1720399e5c82d032 |
with {{formula:4607a90c-4989-4052-8a81-a4160822318f}} denoting any tangential vector, may give rise to two sets of incompatible equations of motion, which correspond to the Dirac-Frankel and McLachlan variational principles, respectively {{cite:d01f18cdcdcb71c095731a397f464cce516d40cf}}. If and only if the variational... | m | 2ae7fe59f1ca8e4136cd2525a3dd2106 |
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