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Efficient spectrum utilization is another key issue for JCAS systems that needs to be addressed. Spectrum efficiency in wireless communication systems can be significantly improved by operating in a full-duplex (FD) mode, which is being considered for the next-generation wireless systems. Specifically, the FD technolog... | i | 2ab1457d9d72b0d056e7b55a67e99bcc |
The finite volume method (FVM) {{cite:db01aa5489c4f10becbd6e38cca16a66280f18a8}}, {{cite:50e86fe91b1744e0e68b5f462a14e5762431502a}}, {{cite:138e157a793a2b35d015d782fa91209840095a88}}, {{cite:4c71efb30a752db75f81a6ac91a0451437009c46}}, {{cite:7c06837c3aa1a3ceeb5c0855c694ed311b5c98d0}}, {{cite:3d021c3acb8fb4539dda051646b... | i | bab4af7f912033ed8aa4996fd2238c72 |
In both cases, {{formula:4ddcc9b9-8b4e-4c22-9280-5d6652194de9}} is the number of independent fitting parameters and {{formula:9bb8575a-e1d8-4ebd-9fe6-9ab83db6696f}} the number of data points used in the analysis.
To test the effectiveness of a dynamical DE model (versus the {{formula:3837cc16-49e7-484b-a0e6-aca6d84e6... | d | bb615805f8331adf56f44946a9992658 |
In the end, we draw attention to the progress on black hole stability problem in recent years. Linear stability of a Schwarzschild or a subextremal Reissner–Nordström spacetime has been shown by {{cite:69145a063b10eba4415d79338a6a60609eaa314b}}, {{cite:b45c55025f6e42fc3fab5f438ac2f4583d2cc958}}, {{cite:4e6197bec3f11123... | r | 6a49f6be8b80ab9847248eb78010a5a0 |
Controlling the charge state of an individual quantum dot can be very important for quantum technologies, where the undesired switching to a different charge state precludes interfacing the electronic spin to photons {{cite:2d42bb9014c323ea9e038cd44b24a9ba8cb2405f}}.
Boosting the decay rate now brings the colloidal nan... | d | e8b29c593686fd0036e709744e42cb4b |
Table 2 shows the performance differences between different variations of the Wave-U-Net,
with different numbers of layers. No fine-tuning was performed to obtain the results shown here, which explains the difference between the 10-layer Wave-U-Net in Tables 1 and 2.
The results suggest that fine-tuning does not make a... | r | a2a4be6a0ab4546c8d96d80382226dc6 |
Note that {{formula:3e3f402d-5bad-4687-870d-726fec04d5e8}} -isoshtukas for connected reductive groups {{formula:865069b1-f137-4546-9cbb-e7e1d22a806f}} were recently studied by Hamacher and Kim {{cite:32a0e0655b7904bef8b75563344674cfd992f0e1}} from a somewhat different perspective. Namely, they defined isoshtukas as {{... | r | c4e2fac39a37fbd8873f04c48d77ae24 |
Since no radiation was detected with the numerical code used here,
our numerical results suggest the mathematical existence
of true breather solutions.
The solutions constructed here are also different from the sine-Gordon breather
in the sense that they are propagating, and have a well defined phase and crest velocity... | d | 81fc25b4a99d80069e59e2aa35a5cd50 |
We describe in this section the solutions of the shooting algorithm in the case of two controls. We first recall some results about the continuous limit {{cite:eb7b301a92966b671bd98a56ecb11c0ad2dcf921}}, {{cite:9c959f8a458ba51ae7dfaa8090653ddfd9e73ea4}}. In this limit, the optimal equations can be integrated exactly by... | m | a182d7f1e54b910f4e0e178ca959c8a5 |
The proposed method in this paper is compared with the method proposed in {{cite:6876a15e8e68ed51bcbf72a80a96e1d4690d3fe0}} as well as {{cite:6649830107ddbdd5e66474af78e1d5dc8499dcea}} in finding a Safe Corridor (SC) between the same starting and goal points in 10 randomly generated environments. The comparison metrics... | r | c50b9b2d087bd62afccb5a8a90fe2d6e |
As in {{cite:bae1320d6274d195e7ae68c24547673e68d735bf}}, the computational domain extends over {{formula:8f19cff5-37ce-4afb-a10b-12756ce92461}} and {{formula:09833ea8-f27b-4a5d-834a-21e2851c95fc}} , with the same Dirichlet boundary condition {{formula:2b68f9df-4b7c-42ea-bf63-466063e0dab3}} at the inflow {{formula:042... | m | 8b521c314c89ac25ec5bd851d36f8654 |
Qualitatively, Theorem REF is identical to the original basic container lemmas {{cite:ead3c52abbc00749d6e5cc9c58edcf62ed26811b}} and {{cite:5e7528a191be4aa8607e81e5890f3bf900d71192}}. Quantitively, however, it is a significant improvement of these results. In order to demonstrate this, we shall present four applicatio... | i | 6cb8487aba2d51da1cd01c845f137465 |
In this section, we describe mMonoT5, a multilingual variant of monoT5 {{cite:c67fffc65a054d410fd7df5e8a0f300cfebf6b26}}, which is an adaptation of the T5 model {{cite:c957f94eebe3bcbad1d4622ebf9934283a74e3c8}} for the passage ranking task. We first finetune a multilingual T5 model {{cite:db1c0c693d3042e9190ba7f642e630... | m | 3382e9666cfbf784640d6c61e1ca742f |
See {{cite:80b3c1cbd6c9cacc0c9830f769969257199d4643}}, {{cite:e4e61c5566942a655f114ab13200856171bbb7dc}}, {{cite:8fdb0c5ab72c6f4ae752cbbedb2ea0e6a419a338}} for the proof.
| r | f8c33e574a3d0239ebae4d67183ba2ca |
Two released BERT {{cite:8dbd49a205c45322332a8b6f31932dfe9f288b7d}}, BERT Large Whole Word Masking and BERT Base, are first used as pre-trained encoder and baselines for Span Fine-tuning. Compared with BERT Large, BERT Large Whole Word Masking reach a better performance, since it uses whole-word masking in pre-training... | r | 0b7d8a7643f1ff4fc894cd9962352780 |
We prove that for super-resolution measurements it is possible to achieve a quantum advantage demonstration even when the measurement is approximate (with a system-size-independent sampling error), based on plausible complexity-theoretic assumptions similar to ones used in the "quantum computational supremacy"
literatu... | d | 4e3c0584bbf30084d1f0e8143df7fd74 |
Table REF shows the quantitative comparisons on the Set5, Set14, B100, and Urban100 with factor {{formula:98616da7-6898-4e4c-b628-c77f9f1864e5}} 2, {{formula:970864ba-89be-4ec1-b519-7ab08dd4f12c}} 3 and {{formula:0c00922b-01f3-4612-bd9c-8576443f8f8f}} 4. As illustrated in Table REF , our approach surpasses IDN with a ... | m | e087b5f083f2f91e98c793b67fe0f01c |
Note: Instead of the covering number of {{formula:bfec0403-4c8b-46ed-99de-11a7a2089cd2}} , the above result needs the following metric entropy result of the interval {{formula:d64fc777-b163-4ce6-ba14-a0b58895c944}} (see Prop.4.2.12 in {{cite:c3ab3d6c0931fd076525b6b3a7abbeb78407c2c5}})
{{formula:a1fa43e1-bf4e-4dba-811c... | d | 7d5a77cc33cf065c74062d44cd4a1b3c |
TER is not restricted to grid-like environments. For instance, consider a LunarLander task in OpenAI gym {{cite:8836b1c72ef73aa1ce8b108d54ef1e30e4f8aab9}}. The goal is to control the aircraft to land on the planet. Though it is not a grid-like environment, we still can build a graph to maintain predecessors of each sta... | d | 6a78c679451537d51278fb1df3dbe626 |
On the other hand, the secrecy rate optimization problem has been intensively investigated in recent years. Because phase shifts of the IRS can configure the wireless channels, it can greatly improve the secrecy rate {{cite:9e10da8b36624517b166a175f212c3ad3f9d8019}}, {{cite:8e73fa5e37f19f216ad406d8e1860e37d586ad9e}}. S... | i | e40b3698648ec6b21111d5ffa5b24e27 |
Unsupervised Domain Adaptation (UDA) aims to reduce the domain shift between labeled and unlabeled target domains. Early works {{cite:ddb337a850e639c3b75ed871dfcf81bde0360571}}, {{cite:0bb51ad53d9759174f713eba8259f3560a432807}} learn domain-invariant features to link the target domain to the source domain. Along with t... | i | 3e54bb54d60b54955f87200c62f4f050 |
where {{formula:959c6ee6-a34c-4317-bf9f-b8f3e316c8dc}} and {{formula:09e3041e-493c-4875-a8ea-a2cdd1261f20}} for {{formula:54866432-2cc0-4114-a1e3-b3c0812c39b5}}
is the wave vector of perturbations with the wave number {{formula:ff1d84aa-8b4e-4f50-b20f-c3674b039567}} , and the number of Galerkin terms {{formula:875c... | m | 94e5c6ce5203868298ae27c26a5c2f09 |
Carbon-footprint aware NAS. The EC-NAS-Bench dataset reports several metrics per architecture, as shown in Table REF . Combinations of these metrics and the use of MOO could allow for the exploration of architecture spaces that have interesting properties. For instance, NAS can be performed to directly optimise the car... | d | f6c4ed3ddfd473e82057728b77411d0c |
In this learning paradigm, we do not need a large amount of images labeled by attribute labels. Instead we pre-train our CNN model using a self-supervised learning paradigm. Here, we use the MoCo v2 self-supervised learning {{cite:4111a9ef2659c3c4973149f1e9cb00b0da354fac}}, as it is a strong and efficient self-supervis... | d | 369c8daf7be2c787b92ec81fa82a0346 |
On this note, let us once more consider the quantity which
Eq. REF measures, namely the distance of {{formula:cc8334a0-f7ec-40c4-ba25-bfe0bf789ad3}} from
{{formula:4f535c11-517b-4b9d-8e0f-4bc45dd6b085}} relative to the distance of {{formula:8bd49482-133e-4788-9267-2f42d7b86497}} from {{formula:d3c1a97f-e433-433f-9e... | d | aebaef1c93f792786a8c162b843b6e39 |
Table REF presents results on SDD in the short term setting {{formula:65e0d4c5-199d-47ca-9257-f3a8c5c5a3f1}} seconds, {{formula:b0885aeb-d43a-46b0-b379-712e9ca8b4e8}} seconds. We follow the standard split from {{cite:37ed19bd4c097cd0383bd3db949269fc63f7f338}} and report results with {{formula:585e7ed0-55d9-4b8d-919b... | r | dd0f685d7c8df8b5f46e91841e082311 |
The general framework and results obtained in this paper
motivate applications to a variety of exciting problems in different areas of interest.
In mathematical GR, potential applications include
the analysis of waves on black hole spacetimes,
analytic compactifications,
and possible generalizations of the Chen-Teo ins... | d | d5cf45b7927959a0617d65b0552dddd5 |
To understand the real nature of the BLCCs, {{cite:dc28d589d0f8578f9698472fda92cbbb27f8d65c}}, {{cite:870be4579c83bc57731d8b6d543c8502ba0c85b5}}
performed an HST imaging survey of 20 BLCCs in M31's disk. As a
test case, {{cite:dc28d589d0f8578f9698472fda92cbbb27f8d65c}} presented details of the data-reduction
pipeline t... | d | 6fee06a2db5d3945f2ac5e5f61b2898f |
We give results of regular (natural) decision trees, TREANT {{cite:9d6d5130a75cdf6686a94915070b557d3b91dad1}}, Chen et al. {{cite:d461dc462a21b32909f0c8e2caf39f8f31d81fbd}} and groot on fourteen datasets in which we compare predictive performance and run time.
For natural decision trees we use scikit-learn's {{cite:18a... | r | 47cefb5593312a5c97c982a1d7782101 |
Shor's quantum algorithm for order finding in modulo {{formula:3b91d4ac-fda7-4138-b2e6-e08a1928a832}} requires {{formula:c0abbdc2-10bc-464c-9b68-37a43b3be853}} quantum operations with {{formula:f2964b7e-9ee5-41f8-aa8e-bf5f37f23735}} uses of modular exponentiations. The main subroutines of Shor's order finding algori... | d | 50d1d8a52bef1572b6299dbc42d28415 |
Later on, similar methods were introduced to reduce the bias of IG towards features with large number
of values. The extreme case is using an feature with an ID code. It is clear to see that knowing the
ID code we can precisely know the class of any instance in our training set. The problem is that we can
say nothing a... | m | 18f409e11f0751fba5818e79c7e8611c |
Pose Encoding on a 2D Manifold. The first challenge lies in encoding the input pose information so that it can be leveraged effectively by the image synthesis pipeline. Prior methods employ global pose parameter conditioning {{cite:1c813a225f17c9f99888da084cbb9db80782350b}}, {{cite:db21e607f2c30398f79866e73316544223961... | i | 56d18cf228e7fd66db9ed2c99db19fd6 |
The solution of the {{formula:673a784d-b5fe-4d72-871c-46bc18cf11d0}} -subproblem involves keeping the {{formula:1edd3975-1c02-48c8-bbf6-31260d06f241}} largest magnitude elements and zeros out the rest. Inspired by {{cite:806b8eb1856227bc0578a4877a9330ef57ee70e4}}, we can obtain
{{formula:ad1acd5c-be63-4c67-be1c-a1994a... | m | 7cf07cd6b83a3d271df2d69994faa74e |
Following {{cite:7d16163749bbb8c6b8303df3813185b21e915b03}} we use positional encoding {{formula:38d96116-96a5-4da7-979f-4cd9ac066178}} for both scene coordinates {{formula:c5685fac-1411-418d-86fc-f0176fc11085}} and viewing directions {{formula:8e62c281-bb64-4faf-b35f-f7d5b0a3904b}} to capture high frequency details... | m | 1b76caaab540a2ded3dda14eddb8b1b8 |
There are many techniques that can be utilized to solve the system of linear equations produced by each iteration of Newton's or Broyden's method for finding roots. Two widely used methods for solving a system of linear equations are described in the following sections, and we refer the interested reader to {{cite:cc83... | m | 39d63be94a18dd71f75b07eabd652ef4 |
Then, we let {{formula:932a5d08-e29f-4e4a-b3d5-d6840980d0ea}} denote the number of residual errors that fall into the {{formula:78458d00-6250-4650-9a44-431a631a01b6}} interval, so that obviously {{formula:8ad5f6fd-82cf-4a25-b5a4-fe735d9813c4}} . Following the routine of Otsu's method {{cite:b2a9093e9ed4963f0e56b4327b... | m | c4a5e0e7282dd18afc870efa71453cfb |
Many modern RL methods use deep neural networks to parameterize the policy; this is referred to as Deep Reinforcement Learning. Typically, deep RL methods cannot be used to train policies using real robots because they require huge amounts of training samples to learn useful policies, and collecting this data on a real... | m | 12efee04f77e62e4a4d7dc23b09fbfc1 |
In this section, four datasets are adopted in our experiments, including ShanghaiTech A, ShanghaiTech B{{cite:e6c47bb63138cfb1dc5cf8f713cb8e5aaf31dd6d}}, UCF-QRNF{{cite:0dde039fb318b3d2217f5a3eef5604b5564cd218}}, and UCF_CC_50{{cite:df325d2faef36dfc395ad9bdd173158c7f3f1ba7}}. Firstly, the evaluation metric and datasets... | r | 9171b178bc6e06e8fa12d17dafa09126 |
In the smart grids context, usually there are reasonable forecasts of the statistics of the uncertainties and a knowledge of the systems dynamics. Therefore, using a structured function approximation such as Model Predictive Control (MPC) scheme can be beneficial. Indeed, MPC uses the predicted information and model to... | i | 9626d431e98da30e278ad248bb07b375 |
TGIF-FrameQA is an open-ended QA tasks based on
TGIF {{cite:93802204f7cf4f71d95a94f1ff736116d7d9f361}} dataset.
TGIF {{cite:93802204f7cf4f71d95a94f1ff736116d7d9f361}} contains 165K QA pairs on 72K animated GIFs.
FrameQA requires a model to highlight the fact that questions in this task can be answered according to a vi... | r | 89431b84483f2955b5f629d6f48c6995 |
The mean field limit, roughly speaking, is an infinite-{{formula:80f4f379-88d0-4300-82c7-1e741507e876}} approximation
of the model. An important question is: how large should the number
of neurons {{formula:0d6e7e18-699e-4eb9-9727-f743503b22be}} be? It is known that under certain assumptions, one
only requires {{form... | i | 4913a7bb438b72ec08f754eea0ff35cc |
Hyperspectral image (HSI), a three-dimensional (3D) spatial-spectral cube, which is rich in spectral information, has been widely used in applications such as face recognition {{cite:c37462689e404228a76a9605ed971520066d5ccd}}, remote sensing {{cite:2095d166542792ade76bb3b3033bcb2ba28d02d1}}, food surveillance {{cite:3b... | i | 2d32973aab814a89b4d5e0b3231753dd |
To assess the performance of PG methods applied to variational compilation, we have run three different numerical experiments. We generated several random shallow quantum circuits with depth logarithmic in the number of qubits and known connectivity graph, which acted as the target unitary {{formula:dabdc35a-5d77-48f1-... | r | d63ba0264f52a0db8526b63cff63b92d |
Our model combines the advantages of both the seq2seq and the sequence tagging models, while alleviating their problems.
On the one hand, while seq2seq models usually suffer from over-correction or omission of correct tokens in the source sentence {{cite:bda20799c9a375d05f6af658410623095d109248}}, the proposed sequence... | d | 9f3151553a5850efdd67094454a56a2b |
Despite the complexity and difficulty of amplitude computations, which grow exponentially as the scattering particles and/or precision (loop order) increase, incredible progress has been achieved in recent decades, especially in planar {{formula:8ade2d63-e132-4f56-a3c5-732972feb880}} supersymmetric Yang-Mills theory (... | i | 4e950ba06b5dd2027d16d641d658c2b8 |
Despite showing video as the main application in most of our experiments, our method is invariant to the order of frames in the sequences. This is a result of using a zero-shot strategy, in which the underlying models are trained per frame. Examining the results, the generated captions display a logical order. This is ... | d | dcfea902873d0eba485374ef045c830d |
Unsupervised learning of an acoustic model (AM) has a long history from the Bayesian model to the deep neural network (DNN) system in the field of automatic speech recognition (ASR) {{cite:50e8eb150a88b12b52c4694288e7b3c6b1ea49e0}}, {{cite:3cf79b5568bea1cd9e0ae5ead65c28471d28c28c}}, {{cite:244ce733fe299fb1f21fe2bb88960... | i | 5ab3d6fe8326e88c4cc142d45f1990c8 |
Several methods can be utilised to measure different {{formula:18f5e2f6-0edf-443e-b717-804267fc83c0}} violation observables in these decays {{cite:0e71cc0af749e195c75c1fa35cd5491b89f0bf8d}}, {{cite:5c438ccb34c14a68f9bc311c81a80230bfe38821}}, {{cite:d66f28eeffb17af220de53bd53c77b29f3f47590}}, {{cite:1869b620f81209d55c5... | i | 1323e8de02dbbd076c98078b54819f8d |
In centralized asynchronous communication methods, the network overhead is concentrated in a few parameter servers. Therefore, decentralized gossip-based methods have been developed to spread the concentrated network overhead to every worker node. DP-SGD {{cite:59adad2e0b7b9cbcf4a4f5305d78a1f894ba6b55}} is a significan... | m | f82f21bb6614dca3217cd6bc03cb1455 |
Step 5 - Distilling knowledge from the original model: Once the compact model is obtained after resource allocation, we follow the common practice in network pruning {{cite:77ff43683cf52f280bbaf9567a5b14f97c7bd52f}}, {{cite:6e71af80bed2ac9a533925d9518abe477e077236}} and start to train this model from scratch with both ... | m | e1b639d570599a050dcad8bfcf33e8ed |
Recent papers have proposed fusing E2E models with LMs trained with text data (usually referred
to this as fusion), including shallow fusion {{cite:fce7b25abbb5ba0ccba6f11cd77070dd6e7785e5}}, {{cite:9b6732c69e368025df35df5adf98b58f2233a70e}},
deep fusion {{cite:fce7b25abbb5ba0ccba6f11cd77070dd6e7785e5}}, cold fusion {{... | i | e5ba9245b0b7d769d1a20aedc180f7da |
The good performance of the algorithm is the result of several choices. First, the use of voxel-sets as input for our experiments instead of other shape representations such as meshes; indeed, due to the statistical properties of our large datasets, the voxel representations leads to highly stable embeddings, they are... | d | be488662bbf777da2cb80aac50c882d8 |
To our knowledge, only a few of the previous studies considered the effect of the AGN contribution on the morphological classification of their host galaxies. {{cite:582ce4299793ef7cb37a575a81b95672fd24204c}} studied the effect of AGN on the rest-frame colours using a sample of X-ray detected obscured and unobscured AG... | r | 1d10a88df30a09bfa2606fa3fd599d5c |
Comparisons with the state-of-the-art.
To verify the effectiveness of the proposed ReconFormer,
we compare the proposed method with 7 representative methods, including conventional compressed sensing (CS) based method {{cite:a8fb10b53e3363f8b1ca54d6441f54132bd8cc05}}, popular CNN-based methods – UNet {{cite:5c1bf049ea... | r | dda7cbc62ce06e7565a141b68599b926 |
In this paper, we incorporate these changes in the adult speech spectrum through LPC Segmental Warping Perturbation (LPC-SWP) and Formant's Energy Perturbation (FEP). We use this modified LPC spectrum to obtain the augmented children like filter-bank features for model training. Steps involved in the proposed method ar... | m | 011a0bfd800756d893fd2c858328a066 |
We compared the performance of P-TD {{formula:2093e75a-a60b-4484-b23d-588af6a5660a}} using 8 different voting rules: Borda, Copeland, Kemeny-Young (with weighted and unweighted majority graph), Plurality, Veto, Random dictator, and Best dictator.For formal definition of the voting rules, see {{cite:7467d397b1d96227421... | r | 0b7b94c20a36b24be9e75aac9fd4b498 |
Pretraining ever larger language models is a research direction that is currently receiving a lot of attention and resources from the NLP research community {{cite:8dd6a95589d65c8b859fd8ffa785087a4f91bca5}}, {{cite:02471ae66eae5ee9fe5e5b07e7fb6b94a886d249}}.Be careful, everyone is on small, specialized models these day... | d | 5a7443b1f771932baae60eac404becde |
Soft thresholding has been widely used as an efficient denoising approach in signal processing {{cite:6afdb8ecad7714ad517275fdc9e21fff19b7bb36}}, {{cite:2f06f3e22a80edc1bcd4fc076356bd301da54b38}} and recently has drawn attention from deep learning communities {{cite:a40bc35fad6026d5bed4aebbd6cd84ec83da4fe5}}, {{cite:d3... | i | 93cc72c7c24cd58b13fc3618c5653c9e |
The discussion between the width and depth of deep neural networks is an essential topic of research. Researchers have focused on the correct approaches to increase the depth of DNN, which in turn increases the accuracy.
{{cite:48cfa138439987bf003ca72b2423286d1abac53d}} demonstrate that the width and depth are connecte... | d | 8393dd61ad9deb3de502cb6431f937a3 |
Policy gradient methods {{cite:dd6c7d72394d13603a6075c0351cfb0dd3e69654}} are a popular choice for a variety of reinforcement learning tasks. Suppose the policy {{formula:def1eeee-fa50-412d-9b2a-f46ab311198e}} of an agent is parametrized by {{formula:7f30d30d-0242-4d09-9195-788946d8c0fa}} . Policy gradient methods aim... | m | de7edb702de0696c7c0c29a37afa3f96 |
Lemma REF gives that the adjacency tensor (and signless Laplacian tensor) of a connected hypergraph {{formula:dfd0bc04-0d0f-4043-96b3-251633160b68}} (and {{formula:36e76006-6f12-4709-9f0d-f57fd4a7d2e7}} ) is nonnegative weakly irreducible, so {{formula:164f48ae-35ce-4129-a3cf-51e230396c30}} (and {{formula:24a57936-2... | r | 511f8943caf3e869d3f28c251f8e8fed |
A line of research in image-based robot manipulation learning relevant to our work involves the use of fully convolutional networks (FCNs). These works take advantage of dense, per-pixel calculations of convolutions and their robustness toward translation shifts of the input. Given an image of the workspace (often in t... | m | 0b34ef86466846ae36fedf5ca4a22fd6 |
We investigate the test performances
on various problems, comparing different choice of {{formula:0c2ae7a7-edd8-4034-aff3-478afad465d6}} . In the following problems, we apply the Gaussian kernel with median distance as bandwidth {{cite:e8a7de01dad7ab01074de9fcb30003b5679bb747}}.
| r | cc2ad8bf40a6cde038a61027cf20c277 |
Published by A. Einstein in 1915, the general theory of relativity (GR) {{cite:9ef1cf836065823cd7658a27de468db142f02977}}, {{cite:51178a78bbf5e50ab7d97214ae85d107bc09ae99}} has become one of the pillars of the modern physics and is very well tested both theoretically and experimentally. It can explain or predict differ... | i | bf38c389d688bbfecb9e3cef9cc2faff |
User Study. Following the previous works {{cite:ac81bda89c3b1bd6b3b3f63fb598753ec6964a3d}}, {{cite:704a1375fe6335cc67ed3bed381fc359d2596316}}, {{cite:14c104630655011773d25f6bad9a20348f48d9f9}}, we conduct user study on Cityscapes dataset.
Participants have been informed their identities will not be recorded.
Each volun... | r | 23aa82b30d9a74f1c7d766c1b94d06d0 |
Since an epipolar plane image (EPI) contains patterns of oriented lines and the slope of these lines is related to the depth values, many methods achieve depth estimation by analyzing the slope of each line on EPIs. Wanner et al. {{cite:1b18b0912705a9539cb8ddb255b4048d3737188e}} proposed a structure tensor to estimate ... | m | 1db11ef087e6d53afd69aecec60e8244 |
The generalized Lie symmetry {{cite:d1046b3da70be959bc7c2f1b75802f00f8f129bc}} of the Euler-Lagrange
equations of motion is generated by second-order prolongation
vectors in the tangent space {{formula:4b451507-43a5-4f6b-a655-b7b5d553420b}} of the
second-order jet space {{formula:a8d8c079-4bd1-4f03-a8b0-2cf6d2305eb4}}... | i | 2e443385585c949f01384e703dc44b01 |
In this section, we use the Phragmén–Lindelöf theorem to prove a theorem on a class of meromorphic functions appearing in complex dynamics, namely class {{formula:c7654b64-adcf-4774-9dee-4910bd8d40ff}}; see {{cite:27d89c910fe7c9e9a54dca58e5f50bfa32c8a909}}, {{cite:38e097edca94c8f8b296bc3187bc57f1f4984866}}, {{cite:659b... | r | 3108e4a25a66986f796cb303cdc97c5d |
As for multi-cellular organisms, the brain presumably fine tunes the synaptic strengths of billions of neurons to generate an optimal behavior. For this to happen, feedback signals should not only carry precise credit information to individual neurons but while doing so, they must not interfere with the activation/feed... | d | 184f4cb8b3d6ec7c0569925042cfb659 |
Problem Formulation:
The input is a continuous recording of an EEG signal from a subject and is segmented into non-overlapping 30 seconds EEG signal called an epoch. Each epoch is categorized into five sleep stages: Wake, REM, N1, N2, N3 according to American Academy of Sleep Medicine {{cite:80461988b3906837f99022896a6... | m | 8bac4dc45398a374d773425f66237806 |
The classical result of Wold {{cite:530291bb5f7c749e258fdc2b8ed3bdf86d883802}} asserts that given isometry on a Hilbert space is either, a unitary, a shift, or uniquely decomposes as a direct summand of them. Beurling {{cite:3ef8a1143e270a4b00f8c67f767a5480e67c1f8e}} proved that every {{formula:8db83e2e-2500-42d1-baa4-... | i | 099936192fa1202742f36888674cb76a |
In our first ablation experiment, we have eliminated all the enhancements in order to show the performance level of a baseline CNN on the CH data set. Since there are several SISR works {{cite:9807d7907338e68953e1871b5512b2538975dfff}}, {{cite:0d597fe015c24c97ecd6223b92d1f1f757b0dce8}}, {{cite:ca0647993ede4133fae7c3800... | r | 1a4f24e5a28ad8daa75d9b76d656c5b1 |
In terms of the mass and time of sinks that form, previous simulations find different results. {{cite:905835434c9314aea8504a193b48185cf1b217d9}} find that the additional magnetic pressure leads to massive stars forming, whereas {{cite:2061ff5dc55f38f00cc0407d6c182a481a7c47e9}} and {{cite:0b536179dbeedbf33852e4e9c98ca09... | d | 35e82efa1f41223d0f7157b367ad1d4b |
The statistical significance of the dark energy dipole is
about at the {{formula:d4cdc0ba-6104-4054-89e8-7c38d937bee8}} level. The direction of Union2 dipole is
({{formula:c5979e39-b388-4874-bc43-12d7af366232}} , {{formula:8e064d3e-0dcd-4ed6-98ff-e5d2f7451164}} ){{cite:258947f3468fc2d16f1e391dbc2bbdfb4d8695b9}}, and t... | m | 9400ed5bf3dbb46e03b343b8e1cb58cf |
Competitors.
We compared the proposed STL method
with three different modelling strategies:
(a)
Hand-crafted feature based methods
(GRDL {{cite:b4ecbf61b8a3d8d73a234a4df5a47b1088ccc072}},
UnKISS {{cite:3e642e376f1c6fd808c0a2700bce7370d5c37567}}),
(b)
One-shot learning methods
(SMP {{cite:6b62d6288c6dbe22c0c1319ac16245e... | m | 492e6b5bd226ccfe6e605cac74bb374a |
Successful models which account for the observed radius excess in hot Jupiters by additional heating must be
capable of supplying {{formula:7b318628-0e28-4135-9e1c-4f5da56b4959}} to the convective portion of the planetary interior. The proposed GEC model is capable of such heating, and produces planetary radii broadly... | d | b70746e1f90e65cdf34f8e236266e3b3 |
In this section, we show that the assessed SAT and TSP neural solvers are not robust w.r.t. small perturbations of the input despite the sound perturbation models introduced in sec:sat and . We first discuss SAT in sec:empiricalresultssat and then TSP in sec:empiricalresultstsp. We use the published hyperparameters by ... | r | d8eaefafdc2fef896f23bc9ea9e806bb |
The No Translation baselines are worse still. Label projection uses hard supervision that affects model performance dramatically. This is likely because the binary labels we use as weak supervision in parallel data do not necessarily mean anything, and therefore cause the model to learn parameters that cannot generaliz... | r | b403b6c370d6139bd689584d52eaa781 |
Local smoothing {{cite:8c00fa65bbaa5a582aa5d5b20c78fe768d9100fe}}. Is a method that uses a pixel neighborhood to smooth out each pixel. For a given {{formula:2b85775e-3e6d-4a96-9e41-1bb65502dc97}} sliding window, local smoothing changes each pixel, center of the sliding window, with the mean, the median, or Gaussian s... | m | 0727a14bbbbc2e1067d133e9e5798499 |
For this task, we compare SBM-Transformer with {{formula:0799edd2-9445-46a3-a5cc-30bd415acf2c}} clusters against various efficient Transformers: Linear Transformer {{cite:a793c034771da511c4ada05254fda164d2a6f1ed}}, Linformer {{cite:01e67c4450647b89816d1ccad0c01ab7d4ed146f}}, Reformer {{cite:57f384a86f3d723ba4b4f818e25... | m | bf8e4c1376ca33e5afdff09106dc7c70 |
In Bayesian settings, we start by defining a prior probability distribution over the unknown parameters, i.e., weights and biases of a DNN. Bayes' theorem allows us to infer the posterior distribution of these parameters after observing the training data {{cite:9ab8dc70e506da6c7c3ae0a59e37dbb59d794665}}, {{cite:9da468a... | i | 77a6a2f1d1f65c62866e4c6249b6e8d9 |
Our framework, Continuous-Time Continuous-Options (CTCO), comprises a policy {{formula:1dc81326-ea7a-4080-a58a-7b2aab113dba}} , and a set of options encoded as a parametric model {{formula:073628f2-632d-490c-82ae-2ee5faeeaee0}} . The policy {{formula:4be15470-c085-46eb-b652-f76076ec7fb0}} selects options with variable... | m | e8859b8a4c8b0daaf22186abe43dc540 |
where {{formula:56449c73-30cc-4f61-80cd-788a41533ecf}} and {{formula:637db4e9-0674-4321-9a6b-bc14842de943}} are the sample variance and the mean of log-transformed data, respectively.
Also {{formula:bd4c5596-d748-4d91-8459-5545e901e976}} .
It can be shown that (REF ) and (REF ) are both asymptotically
unbiased and co... | m | 468cc45b72d51ca7b4d0ac40f529336f |
To assess the performance of the four considered pre-trained CNN models, namely VGG16 {{cite:93ca6d19bd1d1407fc6d3ed18b9dd79845d49abb}}, InceptionV3 {{cite:db1c471fe6502aca6c6e3fd2756fb11ebab0567b}}, ResNet50 {{cite:5b8b017a078cd672fb29838065c8627bfa43f68c}} and Xception {{cite:a56d1104309838b0daebb04a9a1b20f887e30575}... | r | 8325b8db65937a2e6c563ed94e6cd7a0 |
For sparse DAGs, {{formula:922ecfd3-99c6-4107-8a95-44b38b29c152}} is a sparse matrix, and for {{formula:af2275a7-60f6-49ce-98f8-bd3338b21734}} , {{formula:aaf6f139-293a-4c45-a1f2-0b019a4c333c}} is low-rank. Hence, when the confounders are pervasive, it is possible to estimate each component through a low-rank plus sp... | m | fbc978af05613448981afbde05c91a72 |
In Fig. REF , the system outage performance is illustrated for various target data rates, when the large-scale channel gain of the direct link, {{formula:5c7fd9c8-07f5-4967-8920-4eb530ffeb21}} , is equal or less to the individual RIS-enabled channel gains, correspondingly. This implies longer (at least equal) distances... | r | ce1ec97a1b653734d3a348b77e9e70f8 |
In this paper we consider the totally asymmetric simple zero range process (TASZRP) {{cite:04144f5ffeb2501cb3a0bf869464b847101b1248}}, {{cite:124f55f9c7f228e963177764dced193bf8bc3d13}}, which describes a system of indistinguishable particles placed on a one-dimensional lattice, moving randomly in one direction from rig... | i | 84ca2de35ece484110376a95af4f6953 |
To the best of our knowledge, we are the first to propose an explainable end-to-end deep learning model to predict BCR over time after prostatectomy from TMA spots. We introduce a novel network based on self-attention {{cite:16cedaea019469fbe6eac0e019af666f0c8dc343}}, attention-based multiple instance learning (MIL, {{... | i | b62399a5720e3d68091e4ecd46b44a50 |
We foresee these detection concepts to lead to widespread tools for quantum optics and quantum information protocols when moderate photon-number resolution is desirable. For instance, these could be employed for state engineering based on measurement post-selection {{cite:b10b13f61cc237c50146351bfce2ef7a7310ec9c}}, {{c... | d | 5dbed5d76c81874a7f6aea41f83256a3 |
Alternately, algorithm-level methods {{cite:3f4c09f1d2c7d64ddae71d69f54f160942cdb4f2}}, {{cite:3d46fd6aa94226239297f728fca181352a7078cf}}, commonly implemented with a weight or cost schema, modify the underlying learner or its output to reduce bias towards the majority group. Algorithm-level methods modify the structur... | m | c4ec303e4e9be5cad1c7b393572f7156 |
Due to their empirical success, there has been a recent surge of interest in using recurrent neural networks
and their extensions for solving NLP tasks such as machine translation and question answering {{cite:2a4bfafce019f331d662433ceee66d39dae72a47}}, {{cite:7681e9e9ff4b54fcf8a90d0244b4e54da0791c56}}.
These methods s... | d | 75a716ddfe19123a37d8be6458e4be2f |
It is well known that the curvature perturbation ({{formula:3b3b2961-9869-40d6-9079-840792e637cb}} ) is sourced by the first isocurvature perturbation ({{formula:24d65255-ef8f-4c79-aa3f-897339dd4d2e}} ) {{cite:0fe81f7887ecfb6946410ce05d373915fe663aa3}}, {{cite:d197f35c828e4df2b5c2bd99fc8efc75b3216e0b}}, while the rest ... | d | 1658da2f9522c2755f598782c176a503 |
(see {{cite:e7e6a36c0c3379542c698016a2fbeaa55031c3d7}}).
Thus {{formula:351862bd-75a8-4b54-8683-9a929097f094}} of Weierstrass equations are globally minimal.
| r | 4eaf48c850b7e6d6d7a92aaeb0112c03 |
The sentence similarity algorithm used for this methodology achieved good Pearson correlation coefficient of 0.8753 for word similarity concerning the bechmark standard{{cite:efdf3c1f7a694bef4b0ecf17773c4be1f61549f6}} and 0.8794 for sentence similarity with respect to mean human similarity {{cite:85428f41ded2617ea002ff... | d | 81ce0bfb5084827a6a6f95167ecc6a76 |
Let us briefly comment on some braneworld black hole solutions and their possible relation to the solution that we have found above.We would like to thank the referee for pointing out these interesting results and references. While the CHR solution {{cite:518951ec788beedd36fadbb5403b0ae32dfa2f3c}} describes a black str... | d | 8ed2e9617e1794f18d494aeb3cbd58d0 |
Gender bias and its reduction has been observed on GloVe embeddings {{cite:695b0e6f9c54eab91473dcb6ffdff1c4f8d4e7f4}}, {{cite:dd678dd3eae43d71f8bc9f5c8884feaf3d2d5458}} using different metrics {{cite:2e5fce991bc53ac5999951bdf69796eb7ee2084d}}, {{cite:dd678dd3eae43d71f8bc9f5c8884feaf3d2d5458}}, {{cite:864617e74f9a36bf6a... | m | e4933d2ca36b1e5b53bb19481b69c043 |
In this study, we find that DMD is quite sensitive to non-normality (see figs. REF , REF , REF , REF , and supplemental figs. REF , REF ), and to the existence of higher order nonlinear terms (see figs. REF , REF and supplemental figs. REF , REF ).
As to the question of which data-set metrics govern this sensitivity, ... | d | 8f09557e6bf08938be5333d1fa57520f |
The generalization of various mathematical notions such as functions or even operators has importance that goes beyond mathematical curiosity. The Gamma function is a generalization of factorial for a real argument that is unique under certain constraints {{cite:6cdc60f20c07ef23ee32145f9935056ff6fb346f}}. It can even b... | i | fbf35972273072e8c16fba1e3d342dc2 |
A fine-tuning with quantization constraint method {{cite:61f01f595982f57bba9b576f324f916bf68747b6}} is employed in our design. It effectively diminishes the negative impact of brute-force quantization while introducing more non-linearity. Different from the ordinary quantization method {{cite:f7d991cf5a0504ea317f761149... | m | ffb98c6ab7304b002511366803764ea3 |
Aside from providing fundamentally new probabilistic perspectives on
GGMs,
our work also bridges the gap between frequentist penalized likelihood based strategies
and Bayesian shrinkage prior based ideas for GGMs.
The simple and highly scalable computational algorithms resulting from our latent factor representation
sh... | d | 13771a149bb700b6ea29f35310b46e4e |
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