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which is an expression similar to that for electrostatic energy, and {{formula:b6ca7f18-2239-4bb2-b9f1-497796192f46}} is now a length instead mass. This expression is still logarithmically divergent as {{formula:31d0366f-df6b-4e53-88c4-c9da62c9142d}} or {{formula:80148cb1-3396-4c5e-901a-12b307c9a07b}} . If we now set... | d | 5b6688608c3f8ee645d00172002658da |
To employ the internal motion relations, recent works {{cite:06eba37f0267426db74e5d406251bd9f25f2c6ae}}, {{cite:940bf02d315ce8e6444f10f3953341de047b0ac8}}, {{cite:e468027e07b67b7152bb75d096feec726e1e57d4}}, {{cite:19a2e37618c473d696b5b2456d0fdfa533e8756f}} built spatial graphs over body-joints in each frame; however, s... | i | e36f3c4e401f7bb1dd063ab296a0e996 |
Settings. We test our methods on five benchmarking datasets for graph classification, including two real social networks datasets IMDB, COLLAB {{cite:aadaf1a1e35b6e63ba916cea26bd8967ad916baa}}, and three other standard graph classification benchmarking datasets MNIST, CIFAR10, PROTEINS {{cite:219a294aaaf6a3cfd0c2b2bc9f... | r | db598fa4db81ee4b539ac93e00c62f76 |
Context features are aggregated from neighbour pixels and disparities with 3D convolution networks to predict a disparity probability volume. Following GC-Net, Chang {{cite:fa841770149391f55810fa43c3a6791c50fb9a8a}} proposed the pyramid stereo matching network (PSMNet) with a spatial pyramid pooling module and stacked... | m | c821bf9959aa0ad49e281d89453a2d8b |
In this section, we report results on the dataset described in section REF . In all the experiments, we use batch normalization and weight decay as regularization. The evaluation metrics we report is word error rate (WER). To assess the effectiveness of our proposed adversarial STT training method, we compare it to the... | r | 3f0e905ffcdfeb123d990600647cefd5 |
Fully-commuting (FC) grouping: This approach partitions {{formula:81a973b0-496a-46ac-945d-6b58b490beb6}} into {{formula:0e8ebe31-03a9-4035-a350-62c3c1eeb161}}
fragments containing commuting Pauli products: if {{formula:54212ad4-73fd-48c8-a4ec-55964321df49}}
then {{formula:428a0f0f-d127-40b3-92e5-88e51a9d9f25}} . Thi... | m | a109c7d21fb336bb428d900778ffb41c |
For 2D databases, we compared the proposed ADL to improved BM3D {{cite:7949fb83ead2df325f0005a016359973ee98beb8}}https://pypi.org/project/bm3d/ as the conventional model-based method, and three deep learning-based methods, Dynamic Residual Attention Network (DRAN) {{cite:0f2590818917e0bbe84b9399dcbaf3d6bc24ac4d}}, DnCN... | r | 1c950e24bab460a4874a92fb3eede78e |
By Weierstrass extreme value theorem {{cite:048acb639fcfa906d4530afa508e196738f848b6}}, {{formula:d2403a0a-e65c-4a5e-b575-d5fea965d150}} is well-defined and strictly positive, which immediately gives Eq. (REF ).
| d | aafb3f1e3964fa73bfa9c1646cb773d8 |
{{cite:0d0c664fec4d984d16616c517e1fcac3e194fcef}} and {{cite:f313a06f8192dfd6a3629c3cabeefa4f3e5df6fd}} used dilated convolutions to increase the recpetive field while maintaining the number of parameters. SegNet {{cite:3921e459c55dab0b79771451b0da36a8f86a3bc9}} utilizes a
small network structure and the skip-connected... | i | 7b437e15c4e525e418f97bd3071882b8 |
We introduced a unifying model of communication as reward design {{cite:bdc825d7cea08c886fbe613bb3bb47754fddf547}} to explain humans' use of instructions and descriptions, allowing pragmatic inference of their reward functions—a critical capability for value alignment {{cite:0daea296330cc508203abfe18256393683da168d}}, ... | d | 7506a8977b21f5539b4ef77deb11d58e |
The standard deterministic SIS model {{cite:90ec8dfb73e41614a6f5a70723e11ff536485cbb}}, {{cite:1c44154a79452d4adb1218e782833c44ee1d03dc}}, {{cite:9eff1de9a8890e9101794480d06e13907490b224}}, {{cite:adc4436868e25c51c9b8d321c5110eefcc599d16}} exhibits an epidemic threshold below which the pathogen will go extinct and abov... | d | cfa16b701c9fb7a6c901b34439156b82 |
Remark.
Our standard experimental setting follows that used in {{cite:edd8b70ed3e0489577c1311537be0f7e108d7ced}}, {{cite:d3baa85b9679d9bf8dec30b53487739f55ca5ab4}}. In this setting, for questions tagged with multiple concepts (in the ASSISTments2009 dataset), a single learner response is repeated multiple times, one fo... | r | fa21529cbf39f63a2d7411504a8b46eb |
For the perception SR task, a preliminary attempt was made by Ledig et al. {{cite:95b09f7454622922f64eb2c7240cf5be08af3ea5}} who proposed the SRGAN method to produce perceptually more pleasant results. To further enhance the performance of the SRGAN, Wang et al. {{cite:95b09f7454622922f64eb2c7240cf5be08af3ea5}} propose... | m | 00d100359db47bd3a9a85fdf51401ae8 |
While the individual and multi-qubit control results shown here define benchmarks for quantum dot qubit systems, we envision several strategies can be followed to further improve the fidelity.
Precise control over the exchange interactions between adjacent qubits is extremely important for high fidelity quantum operati... | d | 36b222a638efcec5993252e7c05337eb |
In Subtask-A of the shared task of Multilingual Offensive Language Identification (OffensEval2020), we focus on detecting offensive language on social media platforms, more specifically, on Twitter. The organizers provided data from five different languages, which we worked on three languages of them, namely, Arabic {{... | i | d4a38e5a79d66fadafd21fff25342192 |
Over the past few decades, there have been several methods published to solve the LiDAR odometry problem. Based on the type correspondence used during the point cloud registration step, the works mainly follow three branches: (1) point correspondence based methods ({{cite:71a7c71361094bccb344bb556248b6d57af9c0e5}} {{ci... | m | 65f5bc963294125c638b4da10827c625 |
Relation to Prior-based Methods.
Existing methods {{cite:45c4284cf3a3da7bdaeda421c13ba0e7317ad69a}}, {{cite:8f8e6933e230258f5a3d8dbbe917ce810241e499}}, {{cite:8c36f8eb97cbe7d2c300289c833ffc1058152f68}}, {{cite:68ba865caa092923e467087d6ebf122174f28a5a}} focus on the application of dataset priors.
They typically inject l... | d | c35d476272d5d69604eaafd366db19b0 |
In our implementation, each segment of the scene is reconstructed based on the standard incremental pipeline of COLMAP {{cite:d03548c5a91614b204d8da1d50c5f057683438be}} with the default configuration. Since the feature extraction and matching are common steps for SfM, the time consumption of these two steps is not incl... | d | 9eab4c2f75adf8cf15fcfad6de19021b |
Definition 1.5 ({{cite:08e01f9ace08b4d49b34c3c1791d59cbb60eed59}}, Definition 4.1)
A nonnegative tensor {{formula:5cb04ad5-2346-4c40-9555-044783482dc6}} is called essentially positive, if it satisfies one of the three conditions in Proposition REF .
| i | be306c277c5933f91336d4db4693d97b |
Clearly, the goal in teaching a calculus for propositional or first-order logic is not just about the simple manipulation of strings. Instead students
need to learn to fluently understand the properties being expressed by logical formulas, to visualise the classes of structures that are being
represented by them, etc. ... | d | 86cc725d25288b0ca708e5767b4cf3ca |
As a physical quantum resource, QC for multipartite system is interchangeable with other resources such as entanglement or discord {{cite:83199f9b2083eb19400095f65c41ce1e41207ac9}}. It would be interesting to extend our analysis to multi-UDW detectors system and compare the QC dynamics with other quantum correlations. ... | d | 2e414d22092c859dd3f3f57f0ffd41d1 |
Here we focus on STORM (STochastic Optimization with Random Models) studied in {{cite:a39ad3f6bf8993dc9a2b469ba35337543f613a2e}}, {{cite:5d90b3c23b436a32dc310e63d06e9ca73ae9026e}}, {{cite:e6e18a04ac5b1f9637025a009d2d1a86cdaf4fe7}}, since eventually our method falls in such framework. The computation and
acceptance of t... | m | d28ec98a444f4560c1b5ab1ac1a955aa |
Based on the above discussion, VAPC focuses on addressing unsupervised vehicle Re-ID through a viewpoint-aware progressive clustering framework.
We alleviate the impact of vehicle similarity dilemmas on clustering by transforming global comparisons into progressive clustering based on viewpoint.
To improve the clusteri... | i | a57397245bef32a12073d640aa53fa70 |
There exist a number of proposals for contribution measurement, i.e., algorithms that determine the quality of the service provided by the clients, for Federated Learning {{cite:51cc95b59f478607891dab7bfab4da5ec83f124e}}, {{cite:d09bb93887f642666579ac99a2d4094014294f85}}, {{cite:8d6c679fb8a1e356ee3b0d21480e0f29d488a4a6... | i | 18e0df0937e346b6e5ffe4d6775921ca |
i) Minimum Average Displacement Error Given K Predictions (minADEK): similar to the metric described in {{cite:585c1bf5b55db5bda860eb9c14c6092df10ec2a0}}, {{cite:1633141db169234c0258c2fb1f2a47cc7777acba}}, {{cite:7a1e85fa131e105784b615582aec71679015bd74}}, {{cite:d9474197916441736c467cd9bce3963599af46fe}}, for each tr... | r | 57b42026eaf0d7d8c1b1790ebe008360 |
Deep reinforcement learning has been demonstrated to be highly effective in a diverse array of sequential decision-making tasks {{cite:b9cc5562402bb71641ab35175c5ce5537faf04a3}}, {{cite:83e244c74b37385f2769a243c7df8a6ca48093a0}}. However, deep reinforcement learning remains challenging to implement in the real world, i... | i | 38337118c546c1fdf01de13a012b8bf3 |
BayLIME is a Bayesian modification of LIME that provides a principled mechanism to combine useful knowledge (e.g., from other diverse XAI methods, embedded human knowledge in the training of the AI/ML model under explanation or simply previous explanations of similar instances), which is a clear trend in AI {{cite:dffd... | d | b18bb683af74525db7e710b82faeaf18 |
(iii) In the considered model of non-renewable resources, the stock
region is depleted upon each encounter with each diffusing species.
This assumption can be relaxed in different ways. For instance, one
can consider a continuous-time supply of resources, for which the
problem is equivalent to finding the first-crossin... | d | 295c4365d779c373535c13ce18989467 |
The first problem is important both in theory and application. An optimization algorithm with superlinear and quadratic convergence is appealing in most cases. The second problem is of great significance in analyzing the convergence properties sub-sampled Newton methods. Besides, a unifying framework can provide some p... | i | 0c63051deb2ec57a89905e7064e4b943 |
For training, relation statements are collected into groups of positive examples (statements that align with the same relation type according to our denoising techniques) and negative examples (statements that align with a different relation type) for each selected entity pair. Following Soares:19, negative examples in... | m | f41a2b1fc78169de0a24be6e73eac879 |
In order to compare the power of different valid independence tests we must create alternative hypotheses, stratified by both strength and form of dependence, that reflect the types of alternatives we are most interested in. Studying power is thus necessarily more subjective than studying validity, but once we have for... | r | 13b519beb0a8a8689a192c92e9890407 |
Triplet Loss From the results in tab:comp-fine-tune, our methods consistently improve the results over the original model no matter which variant is chosen.
As in fig:learning-curve, our methods boost the generalization after the training Stage I converges, where the triplet loss reaches saddle point and there is still... | d | 04a89baa2bb93712f5fae90803fefc5d |
The optomechanical system consists of a high-finesse single-mode optical
cavity of frequency {{formula:1321f1b1-017a-41e0-8391-9ab449da583f}} with a fixed mirror and a movable mirror,
which is coupled to a mechanical oscillator {{cite:e144581745178d9d44b71e688e217eef0f09245e}}. We assume that {{formula:0593ebca-eb41-4... | m | ee7975824c8123ab67017f5fa6349187 |
The Kuramoto model with inertia (KM{{formula:a37b7513-0d7a-4ea4-b89b-aa454dfdcebf}} ) has been the subject of tremendous research efforts within the last decade {{cite:ebce64c6e371f3617aff581ef878e55b50d35259}}. Certainly, one of the main reasons is that it has become common practice to use networks of nonlinear oscill... | i | 034cdd8b3231c73bca48645aa0dcaf9a |
Finally, we explore whether unsupervised adapted networks can provide better pre-trained feature for night images since the weight itself should contain adaptation ability. Specifically, we adopt DANNet {{cite:b1719c237e99cae866c28ba87ffa9a8fdbf3d720}} and AdaptSeg {{cite:7e3c9ce38cdf8faf789a43476d429242096f465d}} to f... | r | 911cc692e142a8075c9e0a6f9991ab4a |
The next result extends Lemma to a family of unbounded delays, which allows to deal with totally asynchronous iterations {{cite:f4a55211166fd397c4eb8bab3cc107308dbf6426}}, and shows that the sequence {{formula:c0ec0c8b-92b3-474a-8a87-a15bbac78f43}} can still be guaranteed to converge.
Let {{formula:5f70c103-408f-4d... | r | d7541104a1510737fbffb663036ae354 |
One can observe that the Rank-1 and mAP achieved by ISM are superior to those methods. In particular, the proposed model achieves 48.4%/21.9% for Duke {{cite:db30b4835b8b916bc19223dc2b15e6f55f005648}}{{formula:e7ce84cb-1034-48d8-8727-e3c387119756}} Hazy-Market, 37.7%/20.8% for Market {{cite:e544b0328ab605c44060aae47b43... | m | 07741f0f1b13a4a292fc51f6c4167cfe |
Our goal is to train a stereo matching network on synthetic data that can generalize to realistic scenes without the need for fine-tuning. To achieve this, we propose an information-theoretic approach to automatically restrict the shortcut-related information from being encoded from the input into the feature represent... | i | 527d8a6a86d5e6208cbdb2b1ff486d1b |
We choose several most recent state-of-the-art methods (and label their time of publication) as comparison. Specifically, we compare with methods that represent different existing temporal modeling strategies in VQA, including VSFA{{cite:f0373f6b1f0b743638f6f147f35dd9608274a786}}, which applied a ResNet-50 2D-CNN backb... | m | 95b20ac07522d92e90d633d3c9eb8ba1 |
In this section, we recall some basic concepts from convex analysis and theory of monotone operators which will be used in this paper.
These concepts and properties can be found, e.g., in three monographs {{cite:8b4f51ef2aac1f1f107821da57441d0d83dfbcd8}}, {{cite:74fb320c532bd110b1fc550a60246623630188bb}}, {{cite:0ef9fa... | r | 7875e29934614efcecb1a0fed2f1757a |
The outputs of 240 Skyrme interaction parameter sets, in eleven domains where experimentally or empirically derived constraints exist, have been examined by Dutra et al. {{cite:d761aff47d648b1b52f19b518f7b9e5a6f93643e}}. These domains consist of a detailed systematic analyses of the symmetric nuclear matter (SNM) (4 ar... | r | 81d74bdddd7b7d1a6391b442331bd7ef |
Using the free energy surface one can employ a probabilistic (PROB) method to calculate the p{{formula:5d1a68cb-7a32-4123-85d5-7764c0389c61}} of water and p{{formula:7625b7a0-c791-44c1-9ed8-c6e775324316}} of an acid, as suggested by Davies et al.,{{cite:8dda7ca78cc49a2eb34f6186a9374173c80f36d8}}
based on the work of ... | m | 6270b26bffaadba3ca16a6fdbbb64cef |
Dynamic convolution was firstly proposed by Jia et al. to improve the performance of ImageNet classification by introducing attention mechanisms {{cite:ae43e1d84b2c75dad31544152403161772fac10b}} without increasing the depth and width of the network. However, in Simu-Net, dynamic convolution was used to transform pulse ... | d | 81038abd10b8fd1cf8bacf76b5c34ec5 |
We compare the performance of popular real time warping methods and the proposed method. Bilinear warping probably has the best trade-off between performance and speed in the literature, as pointed out by Zitová-Flusser {{cite:07a685b1c1b6be0dcec7e1a6d01cf42c31022ffd}}. Hence it is recommended for video and animation a... | m | 6070d2ce7b4a2b85b0cf9853a2a9c516 |
subparagraph51.5ex plus1ex minus.2ex-1emEmulating synchrony.
Alternative abstractions avoid dependency on the specifics of a failure model
by simulating synchrony {{cite:efec7132abe32b640d1a1c1a191e6460ad3e164d}}, {{cite:e19a7a63f01d96f1d3508cfcf2e8ca9f86af1056}}, {{cite:e23f6f8f4126b5ba7051c220cb3b5fce9125ae7c}}, {{ci... | d | 982227a8dcf46fde9b8e5815abe6f590 |
However, the derivation of the retarded potentials using the Euclidean geometry implies some conceptual and mathematical inconsistencies.
An extended physical charge cannot be reduced to a Euclidean point. Conversely, a Euclidean point cannot be expanded to a finite dimension, unless invoking more advanced geometries.
... | i | 134e0751482788a0f356709bde1c8fde |
As mentioned earlier, an agent can potentially have equally valid future trajectories, e.g. turning or going straight at an intersection, and it is important to design a predictive model capable of capturing such diversity. However, this goal is not achievable by using unimodal approaches {{cite:7b02453c36aba0290bb5882... | m | 0dc94911902e63b81bbbc315b74fec4b |
We use the approach Multiconfigurational Time-Dependent Hartree Method for Indistinguishable Particles (MCTDH-X) to simulate the steady state of the system and extract the observables of interest {{cite:c6fb07c1fe7167034410b0c01bc7074ecc6c0a9d}}, {{cite:4f12a45cd7f7967b307d11f142c7055680691d2a}}, {{cite:7b55c5efd4b184d... | m | 32e582f3ed3e30a510708f23058aedd8 |
The rest of this note is as follows.
In Section 2, we give a brief explanation of Knuth's algorithm
{{cite:75d4ca441ac6ca4b4620a819230f29923e6a42e7}}. Then, in Section 3,
we describe our computational experiments for determining {{formula:e38b62bd-642a-4903-ab26-2823b2d80d9a}} and {{formula:e4279826-07ed-4890-ab59-2ce... | i | b7d24f6b5b465e75f5834527a0f1f7f0 |
While fusion encoders can be time-consuming, we combine the strengths of both strategies by performing re-ranking as in {{cite:d7c11dca98ec6502552627823cea6b883653acd1}}, {{cite:52a76ad16e7d7a76c91701c2757e1fba78ce8ffb}}. Specifically, we can first retrieve the top-{{formula:af761ecf-c621-4c81-ae33-494703e32423}} most... | r | cb44893590bd81327c23d64906e566d9 |
Third, data augmentation techniques are more effective when the training sets are small. For example, all data augmentation methods achieve significant improvements when the training set contains only 50 instances. In contrast, when the complete training sets are used, only three augmentation methods achieve significan... | r | d0542aaf782f0a13fc70a717a2d498aa |
In previous research summarized in {{cite:fbd44f6131922a62d0db7eb4814140b60363f76c}}, most of methods to solve Learning to Rank problems are pointwise, pairwise or listwise approaches. The pointwise approaches use a single document as its input in learning and define the loss functions by the relevance of each document... | m | 8859a7ca041ed9ac7aff3a3f0d5b9f54 |
Besides the video-level evaluation, we also compare our model with state-of-the-art methods in terms of frame-level AUC on CD2 {{cite:15a7b367faf1909c4d9f6e2c2b31df1cf44c5fa0}}, where our model is trained under the cross-dataset setting (see Sec. 4.2 for more details). As shown in Table REF , our model outperforms the ... | r | 0703616e54c351e5d936af6202d8c989 |
Another possible explanation for the difficulty in directly learning a small number of shared Hebbian learning rules is the lottery ticket hypothesis {{cite:c6c877ed4b2ce2c7b3cef3183637d89693429f79}}. The lottery ticket hypothesis is based on the observation that neural networks can often be pruned aggressively without... | d | 5883558d80f8a6b4c9ade5680364dde1 |
[leftmargin=6mm]
We develop a fully state-dependent performative prediction framework which extends the analysis in {{cite:4cd8b60a83680802b3b499c241cf5b32eec8e8c4}}, {{cite:99e80b9fbd45a670b3c265984af57ac9b01d7c43}}. The proposed extension relies on a state-dependent stochastic approximation (SA) algorithm with noise ... | i | 1b247980a16400bed0fc328d0f5345ae |
In this work, we have ascribed all the electron-phonon coupling to
a single longitudinal optical mode with frequency {{formula:95a1b843-c806-426d-8b79-60d87584ddb5}}
which is mostly coupled to charge carriers. This mode has quite a
high frequency ({{formula:f6dab3c1-cb9d-41de-aeb3-f48ccf3c1e9d}} meV), therefore we
ha... | d | 6a2bd3655cdb97f6c4d32d25b14baef3 |
There is a large body of work that suggest that local environment around the chromophore may affect its fluorescence lifetime. Variation in pH {{cite:65639ad3697eacd1ab2b41f7d204ceff1fec27a1}}, viscosity {{cite:dadda7a6364d8c9a683b06f7f71bd1c86e50c416}}, temperature {{cite:6efa58c0409194ca3bd7af8bf7779eeb961c4e39}} and... | d | b05657666ef586dbbcb3b5ea6dda5a6b |
In practice, these considerations mean that most, if not all, the model dielectric functions can be viewed as particular cases of the memory function approach. Another advantage of this method is that it is highly flexible and customizable. We have shown here only the basic features, limiting ourselves to a simple memo... | d | 3fc0fe811a354eeaa1638cf03c6f3634 |
Rather than learning a policy defined over the full {{formula:0136d551-e6e9-4834-a38d-e60a48cadede}} dimension space, we have restricted the search space to the typical set {{cite:178cf114aecd37fc60fed6de2fd9b0375d5b9ebf}} of a d-dimensional standard normal distribution (the base distribution used to train the Progres... | m | 6221144078e086b26638d4aa91c0ff8f |
If we check exhaustive textbooks in biostatistics, such as
{{cite:e3dbb29e22db664358e02ead19f6545d7dacdc81}}, {{cite:82ffd86ed9695a2d7a0f2bf2cc36893482744a99}}, {{cite:716be15015007884253d731778f60a3e898de9e0}}, or more wide ranging ones, such as
{{cite:4c7ff907564c885e60047870a50569276adc78b7}}, {{cite:2f8c3d1ea245e13... | d | ae98f2039150b63c5463331691bb4120 |
In {{cite:b4b9b8344a23f2d0511f2af74e5415d61d9cb314}}, Xie and Peng (team “Pengy”) used a well-tuned patch-based 3D nnU-Net {{cite:244b43bbeca8f5f55600648d3f4484905486c0e8}} with standard pre-processing and training scheme, where the learning rate is adjusted dynamically using polyLR {{cite:4748d7828dd4928f5e427c4fb3a7f... | m | ec597a26929839f85e2831aba95071b6 |
It is relevant to recognize that ass:lyap,
ass:lyapmeanfield and ass:lyapminorization boil down
to standard stability and recurrence conditions; see
e.g. {{cite:bc6b539ee6ac770d1238ae5714cb4e0201378ad3}}, {{cite:d1ea8984eb2d233a93e41eaf226ca8f8145c84b1}}. In the
Euclidean case when we assume the uniqueness of a solutio... | r | da016c21280f3e775542fa415f8519c8 |
In this section, we provide more benchmark results.
We evaluate IQA methods using Spearman rank order correlation coefficients (SRCC) {{cite:1d7fda0cd0da76591a0211ccd5f538803150211c}} and Kendall rank order correlation coefficients (KRCC) {{cite:c4894f0a61d9b1775292e634b019b9e3a9833ceb}}.
These two indexes evaluate the... | r | 7aceb3842b73d81bf040b56dceece0e2 |
In this work, we use three different deep learning methods as fast SWE solvers. These methods, shortly, are referred to as PCA-DNN (principal components analysis-deep neural network), SE (supervised encoder), and SVE (supervised variational encoder). The schematic of these methods are shown in DNNssketch. The PCA-DNN m... | m | d8d29f97bcc7cac3bfc48e17aae49b56 |
Linear attention may be a promising direction to solve this issue, which decomposes the self-attention to reduce the computational complexity to linear. Numerous methods have been proposed for attention decomposition, such as approximating the softmax {{cite:c0a9f29b346bd31a840247b345162f274126a9ea}}, {{cite:52c11a6ff6... | i | c49196bcfc37b99e5352e0c682644aa9 |
In future work, we would explore more machine learning techniques for multi-modal sequence data integration and metacell identification. For example, scATAC-seq and scRNA-seq are naturally causally related. It is interesting to utilize this unique relation to learn more robust, causally sufficient, and efficient repres... | d | 4aad8c2838baad337917cd278d7ade39 |
[leftmargin=*,nosep]
Seq2Seq {{cite:9d775bee1ded90a5988702cce0d8258af7f95895}} uses attention based encoder-decoder architecture to generate hashtags from the processed post.
Seq2Seq+Copy {{cite:ea3e48fdcd09a228af692e3c7bb95cfa1b64eefd}} uses Seq2Seq architecture with copy mechanism.
LSTM-TOP {{cite:38fe3e0e48e7924ba... | m | c32e66a9bb48bff63330d16fe13d64d0 |
We take a different approach, based on offline off-policy learning {{cite:852e079979813aa715cfeba91485d14d194d5ea1}}, {{cite:53f746d7ec6e0f8e0e0f7943e2783afbf69d8205}}, {{cite:3e84773095ddbe15f45a5de104560ee46eaa4952}}. Specifically, we aim at learning an optimal policy from offline data collected another policy, refer... | i | 5c8352ca86b8c43b34fea28ba9108c25 |
In addition, the power of gravitational radiation decays faster than that of the magnetic dipole radiation (see equations (REF ) and (REF )).
When the spindown is dominated by gravitational radiation initially,
there will be a moment {{formula:8636be3b-745e-46f8-a3e9-ecd68af2928e}} that the spindown dominated by gravi... | m | 08cf9cb1b545fbbe7cd9407a4ec0e190 |
To evaluate the proposed DRr-Net and LadRa-Net models, we conduct an empirical evaluation based on two well-known tasks (i.e., SNLI and PI). For each task, we select three benchmark datasets for evaluation.
Specifically, for NLI task, we select SNLI {{cite:63971a883f61e4a6c73917d46d893d1fa9986330}}, SICK {{cite:1f10283... | m | 657fc0c896973d64b31c0b1ccabab45e |
One widely used distance metric in patient similarity study is Mahalanobis distance{{cite:29570a98e346ab02bef30b912346e1cd8024fe1f}}, {{cite:ebb589578b112798400dc058f63d73bbe1f1cab5}} or variations of it{{cite:29d5a04b9d9488086bce555cac890542c23e49b3}}. The equation of Mahalanobis distance is as follow:
{{formula:5988a... | m | 7f1e6ddc53ae27ff9263c3cd800a9802 |
All detection results are measured using the official WOD evaluation detection metrics which are BEV and 3D average precision (AP), heading error weighted BEV and 3D average precision (APH) for L1 (easy) and L2 (hard) difficulty levels {{cite:c2e71d6dab7b35908a079ccf3e7a86c60d92b5b0}}. The IoU threshold is set as 0.7 f... | r | 063f66156e489a890d4e3216b387a641 |
The proposed network, inspired by {{cite:e030f733861895c090f9611c866ea481734e0a7a}}, is designed to model the private and shared representations of the different domains explicitly. The private representations are specific to each domain and the shared representations are common between domains. To model this property,... | m | 9d20c1639e0dd6694b86a49100abe645 |
To obtain the prior map in a similar manner to PFENet {{cite:57ff5d5fa6d32c72a892958e7e640626927a141b}}, high level query and support features are reshaped from R{{formula:3697cd40-e9b0-4881-99f0-5c47d01bc8ea}} to R{{formula:2ecf9117-4894-49f4-885f-3f95734a2049}} at first. After that, row wise norms for high level qu... | m | 4895a9334857a535f392e52c54cec383 |
The paper is organized as follows.
In Section , we introduce the variational mathematical framework
of our models. Starting with the local equations as guide for the modelling exercise, we focus on the non-local diffusive terms, explicited in the form of {{formula:83cf2886-9386-47ad-be83-b08e07e2dec7}} -Laplacian, for ... | i | 84587761ce86151ce0acc838edc57805 |
The FDM scenario, where this kinematically forbidden process dominates freeze-out, was first discussed in {{cite:b35597fa363f5efbde5c697be1e90715c07fa7fb}} and is further explored at the weak scale in {{cite:c270bf326d7bbeb0cbb450a4ca5539336371984d}}. It is discussed in the context of sub-GeV vector portal/kinetic mixi... | i | 562a70764fa415934bc98ffe72353c34 |
We used the narrowest and the broadest profiles, the one observed on
September 23, 2008 (QS), and the analytic profile deduced by {{cite:d9f3e262d2efda880152b95270b9614fc326cd77}},
to derive the 2D maps of HI outflow velocity. Taking into account the
velocity difference maps shown in Fig. REF , we found that RMS
values... | d | 0d7e908a4a5145a2bf5d2047a28c3e8c |
We first discuss the origin of {{formula:b9427dab-3252-448c-a803-72dc8b7603d4}} in this compound. As current knowledge, it has been well established that an AHE emerges from the two mechanisms: the intrinsic one caused by a Berry curvature effect {{cite:2f0f189f3230518f692812f7619e3bae745d0464}}, {{cite:2993c0156f2f09... | r | 67a43e48f32904f97a6eece840674b20 |
In this section, we introduce our hands-on experience of implementing STARec in the display advertising system with top-K recommendation and learning-to-rank tasks in Company X.
As industrial recommender or ranker systems need to process massive traffic requests per second, it's hard to make a long-term sequential user... | d | 869bf63d2d1bdfccfc6fe12db0072835 |
For the {{formula:926a5956-1d14-4386-a7ae-1d9e94beb4d6}} case the process is quite different. As {{formula:906df71c-0330-4154-a271-85f757965d5d}} is a spacelike hypersurface one can not obtain the symmetry group the way the {{formula:85602af3-fd1b-4e06-a77b-dd11b2308893}} group has been obtained. It should be added ... | d | 556f32e7cbc3ed4198b94575072a6b53 |
where the regularization parameter {{formula:617c099f-7965-4b44-a43b-94cb2fee0560}} , calibrates the trade-off between data fitting and sparsity of the solutions. Under technical assumptions on the data generation process, it has strong theoretical guarantees on its performance {{cite:d1ad0a0807919ad6136d577bb5c3cfdda3... | i | 8cc5d42968c59519d4c105bd2d5c18bf |
Quantitatively (tab:compare:events),
EDS outperforms all other monocular baseline methods, even without using inertial measurements
(which are known to improve robustness and increase accuracy in VO {{cite:aecc56ab97803a0bbf86717a60fd3d41a9524103}}).
Our approach also outperforms the state-of-the-art event-only stereo ... | r | fe42d7d3082f867e7e18b0ec9d4a8517 |
A marginal structural model (MSM)
is a semiparametric model assuming {{formula:82f983d5-a573-442b-a785-23c43b875227}}
{{cite:a5a69c1f820a7b24fa71e0902684abe5edd9eb3a}}, {{cite:3f4183fb559ac2a4e50849dbfb2effc6b5fb88cd}}, {{cite:049ec8bdb227fea28f7857a4a7ae5d060bc6dea5}}. The MSM provides an interpretable model for the ... | i | 406a6a6e32bf2271f19682cf57f2c454 |
Let us recall a special case of the quadrilateral comparison from {{cite:73e2444c9dcae3fd5d62bd365d12b4919fca9c31}} (see also the previous {{cite:54a27543ccdb54baf2a27ee3023a173b09bb8d63}}).
| r | 2a89f8c907cdc780a4b343f699b24f61 |
Density functional theory (DFT) calculations are performed using the Vienna ab initio simulation package (vasp) with the projector-augmented-wave potential method {{cite:1837028550266f3b37ca009ee964744d237b921f}}, {{cite:b41e9b73a3da80289c7e7ae33241bd455712e12d}}. We use the generalized gradient approximation (GGA) wit... | m | f3f78b88b483e5e51dc5f790b904b4f7 |
New advances in quantum technology provide new perspectives for exploiting of rotational degrees of freedom of quantum systems. In this regard, many experimental works focused on the study of rotational degrees of freedom and control of molecules {{cite:0e96a1d5fa3ca56a1e0c0c1c58af57b34931cc15}}, {{cite:10d3a4fa0f8f57e... | i | 21117ecbafb144302ef0b82b7aedb37f |
A major challenge in realizing a real-world autonomous system capable of continuously learning and adapting over time is to prevent catastrophic forgetting. The learning model needs to maintain a balance between plasticity (the ability to adapt to new knowledge) and stability (the ability to retain prior knowledge){{ci... | i | a29f350fd4c65b890482ceadef171382 |
As described in detail in {{cite:8de624d4438f97d8c38d9e9b9f78a02bda2a54dd}}, the toy torus
{{formula:11d0fd45-b1b6-4d5b-85ad-3fc3468f578a}} is distorted
into the “target torus” that approximates the orbit by the generating
function
{{formula:edf05c8f-6d9b-43fb-9ac0-aa4470682215}}
| m | a76f303c8d8075c2d9a899bf4495390b |
We noted that,
compared to many visual benchmark data sets,
disease-specific factors in medical images
may be buried by other more significant factors of variations
in terms of contribution to pixel reconstruction
or image distribution
(e.g., heart-chest ratio vs. torso shape).
For instance,
we attempted to remove the ... | d | 727e743676e912e4f78ccf731143e312 |
The solution to the Einstein field equations for a static, spherically symmetric object in vacuum is well-known in the literature as the Schwarzschild metric {{cite:bc0c5288a81ad5d1987763fa38c073d2be5c3d83}}. This solution describes new effects that could not be explained within the classical Newtonian theory of gravit... | i | ae204dcec437f131f75ef0e316f4bdd4 |
We also require positive modularity in the core node sub-clusters for each cluster.
This is a relatively mild requirement that avoids
cases where the core node sub-cluster may be k-valid and connected but might not reflect a preference for itself over the outside.
Consider the case where a 10-clique, a complete graph o... | m | 8aead712caafb73963cb8bf75174986c |
State-of-the-art performance on face parsing is mostly achieved by deep learning methods.
Liu {{cite:b1859d37fdf1d93f50ee012d57fea09e390d4a51}} incorporated CNNs into CRFs and proposed a multi-objective learning method to model pixel-wise likelihoods and label dependencies jointly.
An interlinked CNN was present in {{... | m | 25957d06edddd3552e425018640fb818 |
In contrast to {{cite:2e9381d5bd594bf3fb3b0eed2625cb6a98c5bcf8}}, whose work we build upon here, we use the value-based agent DQN/DQfD instead of the policy-gradient-based agent A3C.
This shows that learning reward functions is feasible across two very different RL algorithms with comparable success. sec:comparea3c com... | d | e5fb65c5c60848bf16b1211019cc5332 |
The areas of stopping theory and dynamic programming are vast. For an overview of classical stopping theory, we refer the reader to Ferguson {{cite:256e43dea5f3a02dd429f7c7536b30157766fe04}}.
Various robust approaches towards stopping problems have been introduced as well.
Closest to our work is the robust stopping fra... | d | 6b5a5597e7a5f7aaffbba4e1c0c49a8c |
A system of reduced order with respect to (REF ) is {{cite:32d45c53e93d81f67a1047a0716a553f1028fa6a}}, {{cite:23a0f874cad134a52b14e2047de7b445d3e67736}}, {{cite:bf7609c6517c3d571686d39543b91a4a5f51c7e5}} the system governed by the equations
{{formula:8b6c95fd-1ed8-4987-b41b-ea8c94879f33}}
| m | e5f81ef539ce4634effa2edfb8859e2c |
This section describes the TAR method and its application for the automatic translation of the Stanford Question Answering Dataset (SQuAD) v.1.1 {{cite:36b8a37ed9dde3b7dc5d01819a947538004632e5}} into Spanish. The SQuAD v1.1 is a large-scale machine reading comprehension dataset containing more than 100,000 questions cr... | m | 3a8364d6abc84c3ce67d3336524ed43e |
We experimented with alternative graph construction methods, such as applying a score threshold for edges (more akin to {{cite:01f2c039bfcfef8d1b9fc15f41549f9cf0cbe20d}}), or using BERT {{cite:6cd0bf9ff022d37c0f2f1afccd23fc42b206e6b8}} to encode diagnosis texts prior to similarity computation. Empirically we have foun... | m | 2c049a0689eed714b501d38ac04ea4ad |
The Unified Transform Method (UTM) or Method of Fokas provides a powerful approach to solve evolution IBVPs, including all those with linear, constant-coefficient PDEs and some integrable nonlinear PDEs. The UTM was introduced by A. S. Fokas in 1997 for the purpose of generalizing the method of inverse scattering to IB... | m | 02fff29e6776c7c648c725d6076dd6f4 |
We evaluate three state-of-the-art X-ray CAD methods based on image classification, CheXNet {{cite:e1a0e6aa65ff17cd038120b5262ae9ec232e4e38}}, WSPLWeakly-Supervised Pathology Localization {{cite:ac8ba8f5abd87b9e047df2c5bf2a30480d3ebf0f}} and UFDetUniversal Fracture Detection {{cite:dbc1361940e4fcd11cd5ef98c6d2d8e999a02... | m | 3e0dbd136ab74601050b2654a608913f |
In Fig. 6, we compare the convergence rate, w.r.t. the unit of time in seconds, between the proposed DPD-AirComp scheme and the error-free baseline. For DPD-AirComp scheme, time duration of one communication round between the BS and all users, is given as {{formula:31ef1708-dd72-4569-9c66-fb93b8c54cc3}} , where {{formu... | d | a0c3c7a03bdea4e70802c7ce884ca471 |
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