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For a more direct comparison, we also train a more expressive model than the simple MTL-based model we propose. This architecture is based on bi-directional LSTMs {{cite:314cff9ae5cd404f30a00b978683d6b4a253f099}}. For this model, we input sequences of embedded words (using pre-trained word embeddings) from each query i... | d | d56d51df774c0fce8ed12271fee25149 |
In recent studies {{cite:000cbd7adf4ebe6829271d2005324b28af95d3d6}} estimate that SNe Ia with early flux excess, such as SN 2015bq {{cite:b5e77c14b6cac58a992e64eb879dc98703196fd5}}, amount to {{formula:aa5aea3d-b2e8-4494-a398-96fe0cb36366}} of all SNe Ia at redshift of {{formula:65a7e5a2-c0d1-4240-a222-0b88a91d800d}} ... | d | eecfd0e8da21a5f7d97665a00ffb7e44 |
Following details in {{cite:59cbd0d2dc152eb819f46df02e54baf49f6f21c4}}, for simulation of (REF )
we use multi-order NDFp with {{formula:d141c512-3035-4caa-9b16-4c137357d14a}}
(and respective {{formula:df1b7d63-ae48-4a29-b192-2631118b2936}} )
and these are {{formula:d14b7cf6-750e-406e-9cf5-40345bbf9abc}} -stable, with ... | m | e89f287b374f1e6ebf0fae5ab1146bc0 |
One advantage of the OutFlip is that the generated OOD samples could be used to train and improve the OOD detection performance of the models other than the reference model without applying additional OutFlip iterations. We trained the BERT-base and BERT-large models {{cite:b01b9b5793b6caeba5abe0af2de96c9855d37b9f}} wi... | r | 1adbb1afbd55f2ec6e67081d38eba1f5 |
where {{formula:f799d182-6853-4fe9-9572-7c8977303e30}} is another critical exponent associated with the M(H) at {{formula:dff9d954-fa79-4653-a389-9e35be6e7938}} . The {{formula:7125fb67-2af0-4301-84fc-dbc4efb2c5a8}} , {{formula:cdce9e72-7432-42f4-b35e-9e14d2079a23}} and {{formula:0209dce4-412f-4748-9853-17c13c8efd70}... | r | e9367217ae51e26576abbee00fa27876 |
Huang et al. {{cite:ef829f4afa4ebd4844a2c059eeb20a057cb6a1d9}} proposed a complementary approach for proving local
robustness to adversarial perturbations, i.e., proving that no adversarial
example exists in a neighborhood of a given input.
Their approach applies to feed-forward and convolutional neural networks
and is... | m | 3e15a033bcd63b3f7a78a2e73c2f1d87 |
The other goal of resurgent Quantum Mechanics is to find exact quantization
conditions (EQCs) for spectral problems. The EQC is typically obtained as the condition that a WKB solution decreasing along one ray in the complex plane
remains decreasing when it is continued along a different ray. To obtain these conditions,... | m | 721fa6d4687b8af2f649f81c748d4250 |
Artificial Intelligence for Software Engineering (AI4SE) is an emerging direction aiming to leverage AI-based approaches to assist software engineering tasks {{cite:a25e9332b7bcbde2b45b2f90a63794b8c75f7bae}}, {{cite:d83a45ecdc2be639f6738bc22bd9d3238daff933}}.
In particular, binary codeIn this paper, we use binary code ... | i | cfe3341eaff6a76ec1a08e75d06eae09 |
However, FER is a very challenging task, especially in the wild. This is mainly because of the significant intra-class variances and inter-class similarities among expression categories, which differ from the general image classification task. For example, the same people in the same illumination and pose may have dif... | i | 02592ac3ee65095e635d05389332d5bf |
It has been shown that many real complex networks share distinctive
characteristics that differ in many ways from random and regular networks
{{cite:bb548a50f2eee4b928f7dc12a0432d9efd6d1233}}, {{cite:a972d4d14a189c2f53637a96bdc646791b0248dd}}, {{cite:90ed16b7ebd70ab83f0c9c05244210757f97ca3d}}. Fundamental propertie... | r | ecc9696813c29ba3e3894f12d413d62f |
We compare three different representation learning methods: random
projection (RP), principal component analysis (PCA) and variational
autoencoder (VAE) {{cite:3bf89c7c9ff57c7e78f9d0b1f0eaa0623f5a2002}}. VAE was implemented with PyTorch
{{cite:8dab0eff3301604b1ebc91a79ee3789977424393}} and uses 1–3 hidden layers with R... | m | 2aff8717fb93d33d7a67d13b3799d9d8 |
The control of spontaneous emission in the multi-atom (or qubit) system that interact
with a quantized radiation field in restricted geometries has received a great deal of
attention in recent years (see review paper {{cite:113dafd85dbb2420802d076bd74efae0b62b30e0}} and references therein). This can
be achieved in vari... | i | 66b1e53f89e573467278c9f225eb3b03 |
Comparison with the pseudo-labeled baseline.
We apply pseudo-labeled (PL) method from {{cite:d11a30c01474e539e44a0f9ec098bce58b248851}} on our datasets (Table REF ).
We use the same model HRNet-32 as in all our experiments for a fair comparison.
Overall, the pseudo-labeled baseline is inferior to our method on all data... | r | 288dec09726b204cba4e6c640df5d69e |
The ATLAS and CMS experiments at the LHC have measured VBS processes
as a signal, embedded in partonic processes of the type {{formula:b649cc14-f38f-4cc3-b05b-88c32cf64027}} ,
where {{formula:2e0df759-08a7-4cc3-a611-f6dea5cc09fc}} is any light quark. Numerical results have been presented
in the form of limits on param... | i | be34c87f888975a3d2da586782d42f79 |
The superconducting diode effect can thus only be obtained if both time reversal symmetry and inversion symmetry are broken {{cite:5e314e4ea326a055c2c344fdaad4265105999125}}, {{cite:1696592d491452ae4d4b76d5205fae394ab1d01e}}.
Time reversal symmetry breaking can be achieved by a magnetic field. On the other hand inversi... | i | 6cb1a5f3d9b15258c05f1b2fc4bb4176 |
We also study the impact of the shadow dataset distribution on our BadEncoder. In particular, we consider three cases. In the first case, the shadow dataset is a subset of the pre-training dataset. In particular, we randomly sample {{formula:b5e091a6-a325-4b33-8faa-9834a6e07ebf}} images from the pre-training dataset C... | r | e3cfaeda442577be9122ee85fb524c94 |
where
{{formula:c3d29ba5-20e8-4a61-8916-cf409affd144}} are the energy eigenstates of the left and right theories (for example two CFTs), respectively, with corresponding times {{formula:6b99d923-9c59-4a60-b35b-869b343c02bd}} . Moreover, tracing out either copy leads to a thermal state at the inverse temperature {{form... | i | 67eebf5e00f70c4598c31d9bf9a1a522 |
Using Lemma REF and {{cite:a98fbaf974ed41bf45ea118d93413c1fff9dda68}}, {{formula:cfb2baf3-f79f-4e54-aa08-a67d50a604c1}} can be
bounded as
follows.
{{formula:a75d85d4-859f-4687-8215-07ccfab0c275}}
| m | 30cdfbeb3bd7aa399e2afd84409dabf1 |
Typically, {{formula:86c8cb58-d11b-40fb-b6f4-41bf055dc265}} is related to the layer's input {{formula:94f02e79-a66a-49d8-af02-d946270256b2}} , and {{formula:cccaaef2-e1f0-479c-bf47-5742d2d94f9a}} to its weights {{formula:b35e2932-75ba-4743-8c12-70b5cc9380d0}} . LRP {{cite:2a01aee43d735fdb9dbde80bba7238e011cc4c49}} ca... | m | 9f0e44b3e39b4be88187ba1d46b93e88 |
We employ two simple but important experimental methodologies which enable a more accurate assessment of performance in real-world scenarios.
First, we provide timing results in the ONNX Runtime inference engine {{cite:fef8239b3d0ebae986fc96d05b1d84b906d58be2}}.
Timing results are crucial because of the strict real-tim... | m | e5494b6ed5af6fa88a747a9a11788be4 |
Therefore, evaluating recommender systems in offline fashion without running A/B tests is of significant importance. These offline approaches avoid serving experimental recommenders to users, and instead evaluates them using old online data, where users were served recommendations from a different system. Metrics such ... | i | d4078fe26d923ccffc84ca664af423a3 |
1. View Selection: The first part of our framework aims to identify the standard 2D PC views used for the analysis of cardiac function and haemodynamics from a CMR acquisition. The manually classified data were divided as follows: 80% was used for training, 10% was used for validation, and 10% for testing. The training... | m | 66cd4bc182b861b73840632161527195 |
Some of those ideas can be extended to nonlinear problems {{cite:3fce9163181a8c8837bce15e8942ba9a1881f28b}}, {{cite:15296c8feec2785fbe38be1ec8d257c79e561ef9}}, {{cite:c27e2e2a6ccfcd655018e8027ab00caa60cf5c50}}, {{cite:77ad171791d05675fb8360dc323763ebefbc0266}}, {{cite:422fc88304738ace0b946e0f61657c1fb07e308a}}. These a... | i | 63ec922a8f8915fa14504764a0b021e6 |
where {{formula:ceaf93f3-5812-4f19-b0d6-abda29990fc0}} is the Boltzmann constant, {{formula:9415c920-75ce-428e-a8df-c8eb12ba5eb7}} is the effective exchange interaction between the Gd{{formula:478d8718-74ec-420b-8eb9-128e57cd08b1}} local moments and the carriers for the momentum transfer {{formula:25058fe0-a9f4-44b6... | r | d1f96b23819b9b49b6428b64847f432c |
To reduce the inconsistency between the masked pretraining and the video prediction task and to speed up inference, we take inspiration from non-autoregressive, iterative decoding methods in generative algorithms from other domains {{cite:c957e099f0829a35edb04c528a9cc5ec92b1082a}}, {{cite:4ebab5d2ad47b7c22aeb9c17483ccd... | i | f03feff56ce880a35bd5a02d35a93a36 |
In this paper, the problem of TL is tackled to with novel perspective. In the proposed method, samples are cast into a new domain via the process of Gibbs sampling while DA methods usually align features extracted across domains using a deterministic mopping, usually an ANN{{cite:4df044559028576b4135cf2dbf6a100ca0360df... | m | f73359b2c717d963e2e0e9e8f814ea0c |
In this section, we quantitatively investigate how our M6-Fashion model compares to existing models for unconditional generative image synthesis.
Particularly, in Table REF , we assess the performance of our model in terms of FID and compare to a variety of representative models for different generative paradigms (VAEs... | m | 3b83b8fe7730402f00aa96e8ad2f177c |
It is also important to point out one caveat of our simplification. Naively, the double-scaled matrix model dual to JT gravity shares the same non-perturbative instability as the Airy model we considered; for instance, see {{cite:2bf07c50bdecc6b0fe5cc67b3dbd0d4b9c28bc27}}, {{cite:f12e8aab1e14f44ee498a38089e698fbb4788d2... | r | a877b01fa68b59f109e12b0cd63b6623 |
Finally, we estimate the average fidelity of {{formula:6f6bc2dc-763b-424a-bb98-2e6dd5ed8853}} rounds of a large-scale quantum circuit.The Haar-averaged fidelity can be related to the entanglement fidelity {{formula:87d6cbc5-e6f9-408c-941e-068d68aefc57}} , which has been proposed as a means of characterizing the noise ... | d | 14d001b0104dd29557fd8c0e5a9f39db |
with an uncertainty of {{formula:d495ff24-ddcc-48b8-a431-3bdf54cda2f1}} ppm {{cite:a975d3c9c0aa1bd8bdaa5d24fe906620ee6d48bd}}.
The current theoretical estimate of {{formula:58a94330-a99a-4b10-a5a6-97f92393ad72}} within the SM has also reached a comparable precision of {{formula:1af716b4-8ccf-4fc7-9eb3-25546dd499ee}} ... | i | 260a3c5f2d14661b2430de030cff27ca |
Likelihood Lower Bound - Since the data expectation in Eq. (REF ) is no longer in closed form for the DBM, data-dependent statistics must be approximated with a sampling technique over the conditional distribution, {{formula:528553a2-a147-43f9-a4a3-cc1d73eeab7e}} , where {{formula:1549b3c1-3a70-4f1c-b24d-07f5475b6350}}... | m | 06554d97e912bfdca8d755fd9a497758 |
In all of our experiments, we tune the baseline, DDPG, and report its best performance. For exploration, we use parameter-space noise {{cite:fd818edde9db42f75d927751c9c39e39a8540182}}. Every experiment and each setting uses the same adaptive parameter-space noise standard deviation target, so exploration is controlled ... | r | ab3a3b888cd5eb6bd272b4dda00b7487 |
Interpretability of deep neural networks has recently become a focus point in the deep learning community. The furthest progress has been made for
classification networks operating on single images {{cite:a527f1fdfd2566a09e1e834ac014013d655261ae}}, {{cite:18a71b7d9463a7fc26b360cd3ad3003253dcb51b}}, {{cite:ba7f4f4de3df7... | m | 08242915a69df1929ea925eb1b968c96 |
We describe two closely related applications of a new rich class of strong homotopy algebras: (i) integrable models {{cite:bddef0d2d3c00b7d628902153bb042455a5ec3b0}} and (ii) three-dimensional bosonization duality {{cite:a4b1c32d52d04419c272169f4ec4ddeca061b37a}}, {{cite:d0a6291eaa043af272f8ac605d513052bb1464e7}}. A co... | i | d375fdc5c3fa3de9797cf82aa9c1ab2d |
From the perspective of numerical practice, the execution of this work consists of three main steps: (1) MHD turbulence simulations to generate data cubes including the information of both magnetic fields and velocities; (2) implementation of wavelet transformation to decompose MHD modes into Alfvén, slow and fast mode... | d | 3a09980efd9c45e1d7e3005858385ed6 |
In this paper we report the results of the first
joint observation of a new detector in the global network: KAGRA.
The KAGRA detector {{cite:04a1c3240a3d86e97e71ebae003f6a29be046f70}} took scientific data from April 7 through April 20, 2020,
at the end of the third observing run (O3) of the LIGO–Virgo–GEO network.
The ... | i | bf942e8281fb389035de73539fcf903e |
We compare our method with the recent baseline: Next {{cite:a1fc82a5e6b575238b257d816edbca8dc9b3afed}} is an end-to-end model utilizing rich visual features about the human behavioral information and interaction with their surroundings to predict human actions, which is similar with our work in this paper. This method ... | r | 11f9f406278091123bdbb1414d0d9dc4 |
Let us compare our results with the {{formula:7c4b9523-3479-4bef-9dac-a457b6eb85b1}}
measurements available in the literature. Our
limit is a factor of ten worse than the Planck 2018 constraint, {{formula:54e035fb-9bfd-44e4-bdb7-449de65475ab}} (68% CL) {{cite:76ae855d3f729657e6c77a5c37f504d2a43125e0}}.
As we stressed... | r | 12b2dd984e6d1e782265ffa902651ce5 |
Another challenge is the thermodynamic uncertainty relation (TUR) for discrete-time systems {{cite:fac05c3547e6d1a703e2337d5d2887a01706a10c}}, {{cite:79b997faa6241fb9b37f77fb30708b4f1e7e3060}}. TUR is an inequality in which entropy production is bounded from below by a function that depends on a quantity called current... | d | ccfc1296015fcb43a2e172c07a3f1085 |
The following lemma will also prove to be very useful in our study of solitons. This extension of the Bochner formula can be found in {{cite:32c71c82c2871340b7681a5e2b781afa185d4e0a}} and was originally shown in {{cite:64796d418488976873e4389ab7741b1e2d0e2b71}}.
| r | 8a10aea1171c44a9d873ac9ce6f47d1b |
Further, and to ensure that our approach does not cause the well-known obfuscated gradient problem {{cite:c624b4e4f69f8bef702c5858e9b64f0f5b9a16ed}}, we resort to stronger parameter-free attacks using the newly introduced AutoAttack (AA) framework {{cite:88f17a4a55e7ee62afbb13f3e24c49a36ae0039a}}. AA comprises an ensem... | r | ab06375ec233ed94606a4a8a9fc0c378 |
The simplest model that can incorporate baroclinic processes together with diabatic heating and surface friction is Phillips' two-level quasi-geostrophic model on the {{formula:b784891a-2b7d-4f8a-bb0c-02315265f177}} -plane {{cite:fe636f4619b4a600d8692c6ee1490119350d0714}}. The present paper uses this model to derive a ... | i | c24d76446996ba1724b7846baf2d0957 |
For the last passage time from {{formula:e5d72469-8c88-49d2-a350-b515675b472b}} and {{formula:a743e38f-daab-4293-ba83-fa4bf25c96b0}} , we have the following one point estimates by the connection with random matrices.
For any {{formula:39164ce1-3b5e-4d8c-8775-3655f9963531}} , {{formula:60be0a6e-fc88-4252-bb1c-b3d15cd46... | r | 15b4b97c100680d88f805ab1a2dc6c19 |
Fixed-length skills {{cite:2ce20980bf6fa5054ea0f5abac773445fe7cddfa}}, {{cite:f4c1fc8251d52a84de00ef3db9ae2bf9ac6011d7}}: A simple and common HRL approach uses the high-level policy to choose a discrete low-level skill to execute every {{formula:4fbb6328-f0b1-4ab1-b691-7fb375124a62}} timesteps, where {{formula:6276eaa... | m | 5afa66c07041d9a56d8eff0066c91d84 |
We considered dynamic mode decomposition (DMD) {{cite:fe4ba1ac14bf5af8ac9bbf56f318fec905fa88d5}}, {{cite:18c594dba00ca2ba75ca1051b4fa7f545cc1ed19}}, {{cite:486a951afce34c229175c46fb2a56e5882b559b0}}, {{cite:f59540b3ff27bfdda76f75cf275d9d29d15eff27}}, {{cite:7e839752b54557f0b6c50394906b14d3c76647dd}}, a popular approach... | d | 9d167c5efd123453a220cfd39cb4d151 |
For a source-target pair, choose a related higher-resource language to the low-resource target such that there is sufficient source-related parallel data to perform joint mapping. {{cite:118a2cdd1c25d5baed3f5ff13d7d4e701f77c43b}}
Use offline mapping {{cite:f55710a77cc6c1ff2a712d10a226de245f92e4d2}} to align related a... | m | 79a49912916d4127648a0c54995bc94f |
Tree Parzen Estimator (TPE). We apply the TPE algorithm {{cite:7815b548a0efb2fdd0c0e40a819da9fe6243cc98}} by formulating the problem as a hyper-parameter search where the hyper-parameters are the sparsity values for the attention heads and FFN of the 4 encoder layers.
Reinforcement Learning (RL). Our implementation f... | m | bd6fbff99b02bf7367258f3e4cbb16b1 |
Neural Implicit Functions are gaining popularity as alternative 2D image and 3D shape representations. Using a simple MLP encoder, these networks approximate a function mapping between spatial coordinates and a quantity of interest such as colour, occupancy or SDF values. They have proven to be very effective at fittin... | i | 6954b8832425a86415e9977bf31cea01 |
To describe the stochastic ensemble of networks, we must design a
measure for the probability space and define the fluctuation size
with its given statistical properties, (i.e., its observable quantity).
Following the well-known methodology of Jaynes {{cite:090943f061f94fc8fc22391300d6d212cb36410a}},
we can write a max... | m | b8c3aead52ebffc5e506dd922543de4a |
Any statistical distance function that measures the difference between two probability distributions can be used for the discrepancy module {{formula:9ec5276d-744e-401a-8369-c78ac349a0ad}} . The algorithms such as Maximum Mean Discrepancy (MMD){{cite:1ca0ad9e42809afe58377a7904cce3724e11c98e}} and Wasserstein metric {{c... | m | a51a6e4229a82a7d045a4f52406abbfd |
Results for exactly the same experiments, but for the ASVspoof 2021 DeepFake (DF) database, are shown in Table REF .
While neither SA, nor DA improve upon the baseline EER of 21.06%, consistency improvements are obtained for the wav2vec 2.0 front-end for which the EER drops from 7.69% to 2.85% using both SA and DA. Thi... | r | e29d53ee2e2645f2101f939fa9012ee3 |
Note that we do not compare against simple baselines such as BiLSTM-CRF {{cite:d19bfe8f1ae76e0a0a471bc0c83d7ca45d9378b9}}, LSTM-BoE, and CRF-BoE {{cite:971bf96fb6aa5fee781d30b6f7337ac15b82556f}} because they have been outperformed by the previous works we compare against.
| m | c5003806af7397d46d8c037fc236e3f7 |
where {{formula:a8a6f6c6-0a79-46e5-a152-29b3d5f76f88}} is the observed shortest variability timescale, and {{formula:6c04a917-13d6-4be1-9d78-18f4595bc30d}} is the redshift.
By equalling the minimum zero-crossing time of the ZDCF (in autocorrelation mode) to the shortest variability timescale approximately {{cite:123c... | d | 14bc894537c9f19b4dbc95de5b2ace40 |
Our proposed perturbation bootstrap leverages a first-order multiplier bootstrap distribution using a percentile approach. Studentization often can improve the quality of bootstrap approximation {{cite:7c59587c136810a58fc80df8947543c35c8573be}}, although, in the case of the {{formula:60ac4b36-12ff-4f32-bae7-31d6b469ff1... | d | 37e4fee4ac39de1c3669ff9e9e269d98 |
Some efforts have been made recently to reduce the cost of model training, e.g., ClipCap {{cite:8e76a690823091e7268fdaaf3c8563247edc00ef}} and I-Tuning {{cite:bc730b83277087952f7344ae1a0d734b85c2c628}}. These models use an off-the-shelf pre-trained vision encoder
and language decoder.
The parameters of these pre-traine... | i | 94290e2c1a5625571e22b06ed43bb895 |
Modifying {{formula:93dd59a8-2d05-4b3a-bafe-fd87361a244c}} helps improve the efficacy of DL models; active learning (AL) methods introduce querying a user to label unlabeled data to help the learning model develop a map. The modification has shown impressive improvements on CNN models {{cite:4dbee20b6e7b716e5a3e22282e... | m | 6e0204799d42ab808b632226eadfff3b |
Electric polarization was calculated within the Berry phase theory. {{cite:2762061731c912f82ce9548f41b48ca081f81481}}, {{cite:4a9e55179b2010f7e159067fefe4879c46ef705a}} For the results shown Fig. REF , we have considered the T{{formula:bea0db24-f43c-4c2c-a6bc-704c2865d7ae}} phase modulated along the {{formula:51e76554... | m | 78278f6b247dcfbaf47c24bda9bf48b0 |
An alternative way to generate {{formula:ce87743d-394a-4dc8-be5c-9cd0fe96ff7b}} is to generate first the partition and next attach
to each set in the partition a value generated independently from the center measure {{formula:bfe5b50e-8dff-4357-a71d-00c24d2a2460}}
(see e.g. {{cite:4e28a774ae7348fad426cd46f976b5967a42... | r | 36310522ef4b354fb9ef460e01acd8f8 |
The results can be seen in the first two rows of Table REF . In all the cases, our approach is more efficient as
can be seen from the shorter average trajectory length and duration, which is more evident as the measurement noise increases. We also provide a comparison to center point approximation {{cite:fa2ffd223d7af4... | r | eb575c40bced084dda469f9b6fb9a5b9 |
Baselines.
We compare our method against the following state-of-the-art talking head generation baselines: 1) Wav2Lip {{cite:c4faf3a9ac1e54a1ea90d1efb221e764a75736d9}} is a lip-syncing model for videos in the wild. Wav2Lip generates the lower half of the face given the upper half of the face and a target audio. 2) Mak... | r | 3e83820168d33929538646e6f1f196db |
This section is devoted to the proofs of Theorems REF and REF .
Let us begin with some useful lemmas. The first one is the classical Euler-Maclaurin formula, see Theorem 7.13 in {{cite:e64751f7b23e5a058c3e64ec3970725634117989}}.
| r | 86956c497b8f1b74e7f23856573b851e |
2D Instance Segmentation. We compare BoxeR-2D with Mask R-CNN {{cite:c62f306c228580e0081b446269ae45458b50dd4d}}. In order to have a more fair comparison, we attempt to make the Mask R-CNN baseline stronger. We add generalized IoU {{cite:9c9bf60dafeedf6d20604239449572047de800b8}} to the box loss of Mask R-CNN and train... | m | 78c466162317689ed82c73edf2322dad |
Training and Inference Details.
Our learned affinity estimation is implemented in PyTorch {{cite:5a44d82430550a9243ca1f84083ecb67c8bd44b0}}. We use OpenPCDet {{cite:29e9e9b7c985b158a3354eabb228a8478535081a}} for the Voxel Backbone described in Section REF . The network is trained on a NVIDIA Tesla V100 (32GB) GPU. The ... | r | f15b8ce1861b171821334897d2981905 |
Controlling the Type I error rate of a test is, of course, only part of the problem, and designing tests with high power is more complicated still. In Theorem 3 the proposed test is shown to be consistent, in that its power converges to one as the sample size increases and the resolution becomes finer, for each fixed d... | r | ad343f1206e67af7b7393544dad91e1a |
Implementation details.
The network is implemented in PyTorch.
The weights have been determined empirically as {{formula:04523c0b-e91c-41db-b831-d9e120d1762e}} , {{formula:c3ab2bc2-8d6f-4f99-be72-942ae9060d81}} , {{formula:8ecd8796-533f-481a-9f9c-4bdffea81f36}} , and {{formula:d5623e55-ee5d-49e6-958b-6910cddbac51}} .
W... | r | 93d0589e81e34fe4ec57dc0c513d3fb8 |
Remark 3.5 We can see from the proof of Theorem REF that, when {{formula:15787fbd-91a1-42ea-b2ff-6699914709b9}} is an integer, the above convergence results still hold for moments without the modulus. Absolute moments of the limiting distributions can sometimes be given more explicitely, for example, it follows from ... | r | c73bcbf6a83b2ccc034ca77d45477429 |
We comment that the reversibility of {{formula:e91a1689-bfd1-4f44-b7c1-8ecb2896bef4}} processes can also be treated via the conformal welding of Liouville quantum gravity surfaces (see e.g. {{cite:1aa0a0ed9749d95739bb29a75cb4ee530b3d6ca0}}, {{cite:d1ecd25decb45135712df99545fa8b83760d83b5}}, {{cite:117ad31d52a7720bb532... | i | 3812d231d7d8cc3b4d256f86d0a384cb |
However, in many DL-based communication studies, DL technologies in computer science are directly applied to wireless communication, ignoring many essential characteristics of communication. Actually, the features of wireless communication channels and the theories of wireless communication are very helpful in the desi... | i | 2fd0e69cf10df3b90e926b9c7ecc6364 |
PointNet++{{cite:eb7fe4922d5eac7179ad2047e2a982c9e75e3d61}} - Qi et. al introduces a hierarchical feature learning alongwith the PointNet architecture and thus enabling a better understanding of the local neighborhood in point clouds.
| m | ce7200155f68c9ce8edde4b6705b3196 |
Parameter-isolation based methods allocate different parameters to different tasks to prevent subsequent tasks from interfering with the previously learned parameters.
This family requires task-id in both training and testing so it is mainly for TIL and DIL settings.
This family also usually suffers from the capacity p... | m | 660b5f24c4a38e91aad6ed34062e2493 |
which e.g. generates the state with {{formula:65561def-4ff2-4f22-b0c7-27a6b2671e42}} small and no large excitations {{formula:a0648c9c-c87a-40fc-94d9-eeafb23e6ef6}} and the state with {{formula:24cfb2ba-81a3-41ff-9f94-ba922c9fb801}} large and no small excitations {{formula:d937967a-573a-4e94-873a-4709bc65c698}} , a... | m | 52f3dc10b4344433397f5180bb9b940e |
We assume we are given an RGB-D sequence with camera poses and intrinsics.
Depths and poses could be acquired, for example, using a dense structure-from-motion pipeline {{cite:fc6457ef5221396737824280822a0a00a58e553f}}, {{cite:4b5d58cf29c22678743fe2550936dbcb3e8fae81}}.
For most of our experiments we capture posed RGB-... | m | d9fe8ef05ca15378e60cbfe3d7fc652e |
Dataset development: We developed an Architectural Design Patterns dataset, named as ArchPatterns dataset, comprising 2,035 architectural design images from fourteen different architectural patterns viz., broker, layered, event-bus, pipe-and-filter, repository, microkernel, microservices, model-view controller, peer-... | m | ee91a956869763abc938d07f017844a5 |
Separate Learning (SL) is the standard in BIQA, which trains the model using a single prediction head on one of the six training sets.
Joint Learning (JL) is a recently proposed dataset combination trick {{cite:482fa4da771452948e0380bb031022a480af28a3}} to address the cross-distortion-scenario challenge in BIQA. As a... | m | 115a9e56a8949d456236f8454d0bf616 |
Remark 1 The argument for non-collision in {{cite:7ab874a4ce99a7a958a1fd3e82f83ed5f663ae80}} is different. For {{formula:a0880ec6-8b1e-4913-9670-aa2a7a93d450}} , it is shown that the first time for {{formula:636effa7-309f-4e16-98d3-270a3a98851f}} with {{formula:0a1d6257-7eaf-4133-a20c-211285c2b2e1}} replaced by the {... | r | 67f16b162c5e513562bf03d6dcc859b6 |
Partial Connections. FPN {{cite:8918c394013b0534c59d47d093a726e148f8c44c}} first proposes lateral connection to merge features from adjacent scales in a top-down path. PANet {{cite:7121479f3ac03e945c4f0ba85ebffc0255947af3}} brings an additional bottom-up path on the basis of FPN to supplement the missing finer spatial ... | m | e01cd30fb55cc3fce918c5f1f90d32f2 |
Next, we recall the following approximation result, which is an analogous bound presented in {{cite:29ea32d57c4bf2045deb2f8acf80b37e0d7f57b4}}.
Let Assumption REF be satisfied, and let {{formula:53ad976d-910a-4224-9876-c10e98e39dfb}} such that, for some {{formula:93062d47-684e-426b-bf48-079b59a68287}} , {{formula:0... | r | 3c60086333bad4df902521524e0294e0 |
(i)
If {{formula:a1247013-85c8-407e-b00c-c0ae52803435}} then moduli spaces {{formula:2cd21dd9-9bbb-4bbe-a711-b922d6dd2714}} are smooth {{formula:5964029a-5edf-45f7-8e5a-9794a271b716}} -schemes. If {{formula:a718595c-319b-4770-b2aa-b1eb7b2e8541}} then {{formula:7c948c23-557d-45b8-b26c-38ec3f5a1185}} is a compact com... | d | 1339e7538c8efa74ed5fd964247bb527 |
In contrast to the formulation of the effective reproduction number {{formula:045fd97e-8a0c-4b48-8f13-3cf68ca9b712}} as a time-dependent parameter in the reparameterized SIRD model and its estimation using e.g. the SIRD-AKS method, the reproduction number can also be calculated directly from incidence data {{cite:8fca... | m | 33a8941d84e122cc3ca9572432731bc6 |
At this point, we have experimentally shown that our proposed detector outperforms OS-CFAR, OR-CFAR, TS-LSCFAR and CHA-CFAR in our specific indoor sensing scenario. The reason for this is twofold. First, conventional CFAR methods and their state-of-the-art derivatives (see sections REF and REF ) perform detection by M... | d | 2e81d765798734dca7ec2d042b6ee908 |
Point clouds, which are widely utilized to represent 3D contents, have played a vital role in immersive applications such as virtual reality {{cite:48d8f9a9c4366e147a4454b9ffeb58221c1a55d8}}, mesh representation{{cite:0f8e1586d9c3e7bb4704ce1423bc41cee212ad7c}}, 3D reconstruction {{cite:047e520c9701b119da9c871cea5017ade... | i | 2efb4509050c798ebf3a82ef65e8bc76 |
More concretely, we attempt to analyse a version of BERT, DistilBERT (that was shown to attain 97% of “vanilla" BERT performance {{cite:5f0f184ce386042e6faaddc9d45bac4a437fddd0}}), fine-tuned on the TREC 2019 Deep Learning track datasethttps://microsoft.github.io/TREC-2019-Deep-Learning/. We extend previous work {{cite... | i | 7da9d489c91e4683c795412934039a36 |
Variational inference provides an accurate approximation of the full posterior distribution. Still, it is generally not recommended to infer properties of an underlying distribution beyond the expected value due to the difficulty in verifying the assumptions regarding the factorization of the posterior distribution. Th... | d | a3aa463f7d359414e7e461b53104e36f |
Given a corrupted input image, our aim is to predict the missing region in such a way that it looks similar to the clean images to human eyes. In this paper, we propose a frequency-based non-blind image inpainting framework that consists of two stages: i) frequency domain deconvolution network and ii) refinement networ... | m | e09146279d99a98e0becf094c861c0cc |
In this work, we demonstrate the feasibility of coarse, low-power depth estimation of real-world stimuli using event-based, mixed-signal neuromorphic hardware.
Given their massively parallel, asynchronous, real-time computing features, and their explicit representation of time and space {{cite:14cacddec09996cb9f57e6790... | d | db0bdb97a15ccb9fb777f1bfd564603a |
We compare our approach to several state-of-the-art methods: the out-of-distribution detector method (OD) {{cite:854af9ffb99e1a41786cfca0a4b416f798f25cb5}}, a generative approach to zero-shot action recognition (GGM) {{cite:5342cec3ef2ec1505dee125a2bc614fec31a6a21}}, the evaluation of output embeddings (SJE) {{cite:b4e... | r | ffe4d5c620ad9e2f7feccd205df7f645 |
In this work, to address these issues and limitations, we propose an approach based on self-supervised speech representations using wav2vec 2.0 {{cite:4b478c7d6196db22cd2806c41338dc090ffb321f}}, {{cite:578c4ac08792579be01b78885162582d3ab49692}}. Recent studies have demonstrated that self-supervised learning (SSL) is an... | i | bec34bf679d2284df28133e414e14310 |
The CCA between two subnetworks for individual {{formula:725fcb95-3e2a-4f86-a811-bbe97998979a}} is a stochastic
Poisson process. The time interval distribution between two successive
actions of {{formula:0e472b69-0233-448a-97a3-3b312965ea70}} in subnetwork {{formula:5078fc8c-c51f-43c5-bd95-3ddbc643792e}} is {{formul... | m | 4e4613eea3f945d4c67423ca7bef9d9a |
In this work we demonstrated empirically that the model randomization tests proposed in {{cite:e9d371bf147878770393d42e9cca9d2e9bb00caf}} are distribution-dependent. We have proposed performing the sanity checks w.r.t custom tasks in an attempt to control for factors that may confound the results. Our simple experiment... | d | 63333c9820a60aa0e8fe485d9a0aa25b |
We first would like to highlight the fact that in this work, we do not propose any novel network model. We rather show that the state-of-the-art models (e.g., EfficientNet {{cite:a617b48ec542754a22bc2de7da5609916e71a222}}) can already handle the challenging state estimation problem with the help of transfer learning. O... | d | c0f6c50edb0442b24b1893b800f3b0da |
In this paper, we also show that if a {{formula:5771c0f2-415e-4b34-bb0d-6f08a144ba84}} -dimensional BCFT can couple to a {{formula:ff5e56ff-6e5c-4461-8d41-f6ac7ef5b789}} -dimensional gravity with a boundary,
the energy-momentum tensor should satisfy nontrivial constraints other than the ones for the boundary conformal ... | i | 7b54fb0363d0f69872531c0e298c3bc2 |
blackLastly, we mention that in some communities (optimization for example), the algorithms that avoid or use gradients are termed zero-th order and first order methods. Similarly methods that use hessian information are of second order. The method we propose in this article can be viewed in between zero-th and first,... | i | 5ce5634b472697d3c1b47428c06bcefc |
Under ideal convergence, these two discriminators help to ensure the realism of generated images and the reliable random combination of content and style representation to create a hybrid imageWe use hybrid image to represent the generated image whose style code and content code are not from the same source.. More tra... | m | 9e01bbdc3877f09d351880b11189485c |
Observation of superfluid to Mott insulator (MI) transition in a system of ultracold atoms in optical lattice {{cite:b70dc0a19bc36d890ef2c00aeebdd3e04207b9b2}} as well as the advancement of quantum engineering techniques has opened up the possibilities to realize various exotic phases of interacting quantum systems, wh... | i | 25ec11b48e1e0aad493b57ca49c334b6 |
The ring here is the 0 ring, whose set of elements is {{formula:24681ea7-e157-491f-9838-37a70697b1b3}} .
The definition of the operations {{formula:95dba085-f343-4592-aecf-7e952167bb47}} is immediate.
A proof of this fact can be found in any undergraduate textbook on
linear algebra for mathematics students, as long ... | r | 27f47c2b0197965feaa17413d4552711 |
Existing person Re-ID approaches are mainly divided into two categories: image-based methods {{cite:57c434a944d4bf7d0f86155950e2cc83fc82fe37}}, {{cite:dadeb0bf1340e20b5221271135ff24c6b16033b3}}, {{cite:e9a2356895bec1e658863603fac76af091f8c96e}}, {{cite:47174ef90a55988eeee66d9f75c66fd634661da3}} and video-based methods ... | i | 049a7dbf2ac76f5faa713434cb961401 |
In this study, we obtained the light curves of an eclipsing binary system, DK CVn, in two observing seasons, and we analysed the BVR light curves obtained in 2008 to find the physical properties of DK CVn. In addition, using Kepler's third law under some assumptions, the possible absolute parameters were found. The obs... | r | e4d7997d9a6d71ac07c449c726211a48 |
Access to reliable information has always been and will continue to be critical to people's lives and rights. Diverse ways to retrieve information include text, voice, and image queries to search engines, which systematise human knowledge and provide universal access to hundreds of billions of worldwide web pages (or s... | i | 8bee8b3b5930e0c0408f411c94135b99 |
A cornerstone of the field of quantum phase transitions in magnetic materials represents the development of a continuum quantum field theory using a Landau-Ginzburg-Wilson functional, taking into account quantum fluctuations of the order parameter. As established by the seminal work of Hertz {{cite:93ff41d7f571a257dbd9... | m | 217ab53e48d8c83cefb420bf990b6bdf |
In its introduction Ref. {{cite:a691189d6d1e44832cdf6272b51a6df3e4b08aaa}} asserts that a {{formula:55add2f0-155a-4b52-a076-be82cf83b634}} spectrum “carries information of the dynamics of soft and hard interactions.” As noted “The aim of [Ref. {{cite:a691189d6d1e44832cdf6272b51a6df3e4b08aaa}}] is to investigate the im... | d | 893e2df1ede5aa4abd33f2366a772d80 |
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