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In addition estimate {{formula:714c9a5b-213c-4262-b2ec-e215d7367763}} , p. 110 of {{cite:dd3af34d8f282d287901bd886b44b8cf5bee3bc8}} for system (REF )
gives
{{formula:d182cf5b-4e9e-48c8-a884-118210c544f7}}
| r | e1bfd8a3c8b3d65885d7581a0ff12857 |
LazyFox relies on nodes iteratively joining and leaving communities. Therefore, it could generate empty, disconnected, or communities fully contained in other communities. We provide an additional post-processing function to eliminate those types of undesired results. However, note that it can be computationally expens... | d | 19d1b2af42f3139df1c3951591eb533b |
Baselines: We considered the following baselines: Bag-of-Words (BoW) {{cite:d7367ce04ef6f4cc17f352a997d458d64f1eda80}}, Bag of Word Vector (BoWV) {{cite:cba6134a181e5e7161ad09d7d161b8148eeac5e1}}, https://bit.ly/2X0XfBH Sparse Composite Document Vectors (SCDV) {{cite:b3c926203a2ccc2b3e30817bc1abdcae3712d650}}, https:/... | r | c7d185d8311c6cf9f4aeae14778ce7bd |
By definition, the SDR is sensitive to outliers corresponding to near-silent segments {{cite:6e2856b9a1daae59339731e8802779484990a876}}, which explains the big difference between the segment-wise median and mean SDR, and also the higher mean SDR reported for the HTMD-Net, since it performs better in near-silence. This ... | r | de36255bd007b63d5dc552c20332687b |
We have
{{formula:16b12589-1b6b-47d9-abbc-6d7b0c1f9c4f}}
where the last inequality is a consequence of the spectral properties of the interpolant operator (see{{cite:a1b4697b183e11b6cb5a0e66f5616b52d89bddb1}}).
| m | cdb75c1680ac0fa79521785ac2fe6682 |
Previous works on topology learning with GSP models have focused on mono-layer networks that consist of a single network ‘layer’, and the tools developed are applicable to single-way data only. However, this may not yield a faithful model for many complex systems since networks and graphs do not simply live in isolatio... | i | dfaf67eff453cb278938136743ea0b17 |
The push-sum framework was introduced in {{cite:249aaec9156fd951ef2d5d8f15078ac388e9c09a}} to avoid systematic bias in the solution of multi-agent optimizations problems on directed graphs. The analysis of distributed consensus with delays was first given in {{cite:11287003998dcc72fdfe8e2cfc21a67b6e09c981}}, who introd... | i | 4c220ab7be6e291a1e2ca3f81a03e092 |
In this work, we propose a new framework for continual learning to expand the network more sparsely and utilize previous knowledge better. Faced with a new task, deciding optimal number of nodes/filters to add for each layer is posed as a combinatorial optimization problem.
Inspired by Bayesian optimization (BO) for tu... | i | 1152d48deb107225f2d12084334d82bb |
This is as in the proof of Theorem REF . The theta divisor is algebraic if and only if {{formula:4254c3a8-c4ea-4ad1-8475-61b02ddf6f7a}} , meaning that {{formula:9b7a3a12-586f-4fc0-bad4-ab7c1147c54a}} is unipotent. Now we refer to the notation of Remark REF . If the curve satisfies our conditions, then we first see tha... | d | 7e0b393dda424992565bfbbc8a138087 |
(i) Weak-Lensing Mass: Using deep, multi-band optical images from Subaru/Suprime-Cam, a mass estimate for each cluster is derived by fitting the shear signal expected from weak gravitational lensing of a projected Navarro–Frenk–White (NFW, ref. {{cite:b6799e3b83b600ddf753c60d1a93d60dc31031f5}}) mass density profile to ... | m | 4277d1c4db52c34a33e965d8cb33ad17 |
Due to recent technological advances, large multi-modal datasets are being produced; these include imaging as well as multi-platform genomics data. In complex disease systems such as cancer, integrative analyses of such data can reveal important biological insights into specific disease mechanisms and its subsequent cl... | i | b5e13e0641bb1e90ae9ef099560151b8 |
To treat adversarial attacks, some defense methods are first introduced. In general, adversarial training and input restoration are two common methods to improve adversarial robustness. Adversarial training method considers a model training on a mixture of adversarial and clean examples. Input restoration method consid... | i | 77667931282a62af1c0d7e0d4f3261b9 |
Once the softness of particles is considered, on the other hand, another time scale due to this softness appears in the system.
The kinetic theory of soft-core gases has been constructed in many systems such as that having the inverse power law potential or Lennard–Jones potential {{cite:bfb0736173c926d858f0af2a2603ab6... | i | 42c2beb68fbcb8059b9e62771edeb7e1 |
Datasets. We use CIFAR-10/100 {{cite:a2a35ee5a1f984a7a9bd680124c9767b04811f6e}}, ILSVRC-2012 ImageNet {{cite:e2c7311943c87130519b277bc41bf0cb4dc58a9a}}, and ImageNet-LT {{cite:b59ddb8c27006d04970d3ff86d1c806af6cf082c}} to compare different sampling strategies. CIFAR-10/100 with 10/100 categories has {{formula:dcf3818e-... | r | ebc48bd487cb80774e85abb9f397a280 |
We showed that the relationship between the depth of decision path and explanation accuracy, as shown in {{cite:f7338ca468c6a6224299079f5a3c1ba11fba00a8}}, are only visible in cases where the frequency-based ground truth and Euclidean similarity are used.
| d | a0f65b0cf6b6747166d3d34fc6d72083 |
More generally, geological data are commonly available as examples (called statistical samples) of a conceptual model. These might be facies maps from geological cross-sections, from field outcrops, or from geological process model simulations, and each of these might require a specific set of physical processes (a con... | i | 3bfa96f451a674b6ab5c3ff4d80bec80 |
This assumption is a very mild condition for the loss function {{formula:8b6965f1-b5e4-4696-aa52-c6b763a1f816}} , thus we, in fact, do not previously assume the form of the noise (for example, optimizing the square loss is to optimize the maximum likelihood objective of the Gaussian noise, and {{formula:0f59b5d8-be08-4... | r | 3dd6223c58367fd3eebcd4b57b12e3a9 |
This notion was largely investigated and characterized in second-order variational analysis with various applications to constrained optimization. Besides the seminal paper by Poliquin and Rockafellar {{cite:2f878dbf857f52f6e9a58cbcc748fb3c4c60d403}}, we refer the reader to Chieu et al. {{cite:8d54640ee1d79b2d26330e974... | m | a258b795890732d571be9c8a11be71c4 |
As discussed above, {{formula:43b8b6a1-563c-4bc1-accc-f124dd6afc5c}}
depends on the parameters {{formula:a5a4c467-50c0-423b-a15f-5ccc425fc508}} and {{formula:ff98a1e8-ceda-4a1f-9bd0-a81f2812d455}} which are responsible for the
generation of the observed value of {{formula:f1a65e76-43b5-4f0a-8d19-d697299472c7}} , thu... | r | a9a8d9dcd615c676fb8e8c019643c66d |
However, we observed effects of error cancellation where mitigating the quantum measurement readout error alone may not always yield better results, despite the improved state fidelity after post-selection. In some cases these mitigation measures may even deviate the obtained results further. This highlights the import... | d | f7645f2ad4bf212274b225d308911894 |
This work introduced Conformity, a novel strategy to measure the homophilic mixing of network nodes w.r.t. their categorical attributes.
The proposed measure aims to address some limitations of the well-known assortativity coefficient, in its classic definition given by Newman's work {{cite:ae973516a1c6d214f7dcd93a9f10... | d | bec4bddf76af94d03043cb81c3610cec |
For the Image Captioning models in our first approach, we use two implementations. The first one is an implementation of Show, Attend, and Tell by Xu et al. {{cite:c2c872e7c53c5d7acc6bd43075af34408a6183a3}} and second one using Bottom-Up Top-Down approach by Anderson et al. {{cite:eb49e4789576145033354e0afb7b8fa8d25386... | m | 03d8b0a2da0ac437a0abc2ba7be6559d |
This was also achieved by {{cite:bef99e00115d61066ce659e1ae210d9e22c7ca6f}}, a
work belonging to the line of research that leverages pretrained
language models. There, a sequence-to-sequence model with a single
encoder captured the style-independent semantic representations using
auxiliary matching losses, and two deco... | m | 6b847483698db5c33ed75286201dbd66 |
We use pre-trained UniCoRN model to initialize code embeddings. Then following {{cite:5910a99ef61cd597c8b17ef712e1f5e1250c6833}}, {{cite:a979e5b6be7e30f03cb09c42842b8d48d8e15d3e}}, we predict method names as downstream tasks. The method name is treated as a sequence of subtokens (e.g. getItemId {{formula:977c2c20-a723-... | m | e982d9ea58bec7e721775361459056cb |
Large-{{formula:48c3e169-3bca-4ad0-b9a4-e45195dd6bc6}} volume independence of {{formula:e97bf498-2d1f-43a6-a760-65bf995b0f9e}} Yang-Mills
and similar theories is a topic
discussed since
long {{cite:2bef0759365865bd5419bfada327a1cf338f12dd}}, {{cite:2cf17970c453142bfebf849248e548d7d1c1c4f3}}, {{cite:60fa3d9f8be3412bcd... | i | 563d0071f5d64ca116bdcff5c5a79a31 |
Tab. 4 reports the detailed performance from different models on all four protocols.
Overall, our method surpasses the previous best method on source and target domain evaluation in all categories, with the only exception of the target domain performance in the illumination protocol ({{formula:197a2f9d-14a9-417b-8339-e... | r | a9cd193fac0c38fb3e73139a2d369956 |
For MS-DCNN {{cite:347c003ccabdfb789d12ee77b2fd269695819045}}, we removed dropout and added InstanceNorm {{cite:a891203b8140d5b3101232a2da5f3d6c67e62524}}, which greatly improved training stability and performance. We also extended the method of Zhang et al. to handle 3D OCT volumes instead of en-face projections, it i... | m | 0ea1dc86ac04dedb29171072c294d0c2 |
to name two possibilities. Often we want to understand how the two are related — what is the impact of specific modeling choices upon performance. A common approach is to fix some parameters while varying others to find out their impact on performance. Whenever one implements the classifier and has full control over th... | i | ee1acd135b221f16fc7e46b79fa3cd31 |
In contrast, we show that gates are not essential at all to construct a well-performing recurrent unit. To this end, we develop a recurrent cell not containing a single gate. The proposed cell surpasses not only GRU and LSTM but also the so far best Mogrifier LSTM on many commonly used benchmark tasks. Our cell is base... | i | 32658cb99c59f9702d71448ab2b63e01 |
The idea that new particles need not be heavier than the electroweak scale, but rather can be light and feebly interacting draws increasing attention of both theoretical and experimental communities , , {{cite:253f98d92f2b98b5c82fcb2c9a71b7668752ffb4}}.
In particular, the idea that heavy neutral leptons are responsible... | d | 83a5fb81e9d5904ef81ab0c83a9fbffc |
The {{formula:0b752ebe-7dfb-49c5-a0f9-a0771c54d66b}} harmonic number is the sum of the reciprocals of the first {{formula:8c5bd846-a951-481f-91ee-f2ae86a7b12b}} natural numbers, expressed in the following form {{cite:d259e72b5b32416ad7a436f9220881dfca8c354a}}:
{{formula:952c1890-3baa-4418-acfa-7dbcb8b5b07e}}
| i | 90b05237a6837da1c9797a8b935bd9dc |
If {{formula:7c979181-ab1d-43d0-9ae4-cc2b9caf3e6f}} , we interpret {{formula:f24aa64b-8c85-46ed-b480-175b4a54738b}} as the confining potential {{cite:82312f2bc1b6cd54f80e331cc00d996505df4df3}}. In some cases, Eq. REF describes an equilibrium statistical mechanical system, in particular if {{formula:e0e3775e-31c1-42a2... | r | 4c61d07cb191855ee188c660e7df0365 |
Quasicrystal (QC), an ordered but not periodic structure, is famous for exotic tiling patterns with fractality{{cite:f79846f3c22aa969290060d72a0c703397aacbc3}}, {{cite:e996ced85ec1f5a930e374991c1c0a2cb5566872}}, {{cite:38cd3fd2126c5a3c7fb22f7fdefc87bd0b909234}}, {{cite:25fd347f4efdde93b41fca439415f4019489ebf1}}, {{cite... | i | e48075a704e67659b8976a7cee0c68b5 |
We should also mention that {{formula:c7901ab8-41db-47d6-9afb-842a2e3173ee}} holds for the function
f(w ; Es, ):=(w) (-n m 2 MSE(w ; Es)),
with the mean square error function
MSE(w ; Es):=1n m i=1m j=1n(Yi j-I(ki, j ; w, Es))2.
The Bayesian analysis treats the statistical ensemble of {{formula:114822a8-0fb4-4ab4-8... | m | 0ca7f08abc98f33b643497781ffc1770 |
To answer RQ2, we calculated the proportion of annotators from each group (i.e., gender or age group) that mislabeled (failed to detect) or misunderstood (failed to comprehend) each text. The resulting distributions were non-normal, so we chose non-parametric tests, which do not assume an underlying distribution. As we... | m | 02ef9cd61bbbc7687b60ebba45e19bfc |
This assumption is commonly used in the literature {{cite:8a006d097508ee8846774d3f9d33907ec84c155e}}, {{cite:6b158c5a9b42bd66aafb8be096a2ab25f04f24e3}}. The Fisher-non-degenerate setting implicitly guarantees that the agent is able to explore the state-action
space under the considered policy class. Similar conditions ... | d | 1993a16477c3f99a0dfdb53556f64ea4 |
In this section, we provide the numerical results of several applications of the proposed GSAT framework. In our experiments, we used the following datasets: MNIST {{cite:49a39c68571512371ac58f5bd69f736ac4648b13}} and CIFAR-10 {{cite:dc7988849720d9137871fed1d1e079f9cbf75baa}} for image recognition, and HapMap GWAS data... | r | 578aa56eeb36d1b68a2142ea86828de7 |
The alignment is calculated at each training step on the fly.
To mitigate the negative effects of unreliable alignments in early steps, the CTC-based model is pre-trained with Eq. (REF ).
The soft labels from BERT can be pre-computed for all the training set.
For memory efficiency, top-{{formula:9b3c9482-4632-4815-8f8a... | m | e2b4b50bada71d1b48b932725c59ec00 |
Naturally, the subspace matrix size is much smaller in the non-orthogonal basis when {{formula:cee8c6f7-7761-490a-b4d6-39999d527c4b}} , and is also more general than Hamiltonian-based generalized eigenvalue problems derived in previous works. The choice of Hamiltonian function {{formula:f1688ed9-0f4d-46f1-ae0b-9577db2... | m | fc13905125daeee597304bcefef17c06 |
SphereFace {{cite:e3add297387c2db48e4787d9dbd16cae4451a226}} provides a new view on the weights of the last fully connected layer, representing the centers of each class in angular space.
Enlightened by this, we draw a theory—minimizing the angle can achieve discriminative features and the fast way to decrease the angl... | m | 98b1fa1aea2533f36b9c28838341e42f |
As part of future work we intend to empirically evaluate the extent to which our approach can be applied to the task of extracting the structure of PGMs from observational data. We intend to evaluate our approach along two measures. First, how close the learned model matches the empirical distribution induced by the ob... | d | 089035d3821cce0998f4fe77b8514b0d |
Our insight to evaluate robust decisions using statistical decision theory applies to the whole literature on data-driven robust optimization. There exists a growing number of applications of data-driven robust decision-making in a variety of settings, including portfolio decisions {{cite:d02cfef395923ab7b4a9b32066005b... | i | 5821948ef759d930bb678b901e0dc7c3 |
There are various types of digital and physical attacks, including face morphing {{cite:6c788b4dfc60ac01a3d6671c816c29c0a10a1bac}}, {{cite:92a8f4234366cc47019be30865986302f126bdd1}}, {{cite:030a2d70d881ef8740e5ebd1ea68654884fb37c3}}, face adversarial attacks {{cite:91d4c69f9617fc7e34e97cbd7ca327aad2ab2f9f}}, {{cite:86c... | i | b30f29b334de5ec4bf2438ea425a77da |
The characteristic hydrogen Balmer spectral signature of EBs is an intensity enhancement in the line wings with unaffected line core.
For the majority of the observed QSEBs, we found such spectral signatures in the H{{formula:23691221-856f-497f-a97c-7573eeed0e70}} line.
However, 14% of QSEBs manifest compact brightenin... | d | b5d04eb2faa029d24da0c1a3ad2edfd3 |
The combination of the trigonal structure and the interlayer AFM order makes MnSb{{formula:9dbcd12b-03fa-4bdd-a5f8-b48a127283c5}} Te{{formula:c3b6eb28-f0a9-4c3c-8e0b-be5bec86b29b}} {{formula:8deb090d-81cd-45d3-88a0-9ad9ae6b1c59}} -symmetric, {{formula:adf9614f-fb5a-46c3-8584-ce75da12f7f6}} being the combination of t... | r | 229e26a604c87f418430ae40b917c33a |
To answer the RQ, study results provided evidence that compared with using interactions, using entity references improved note characterization from fair to moderate on categories and from slight to fair on overview versus detail; the two sets of predictors produced comparable fair agreements on using prior knowledge, ... | d | a6c91a3ba56e6428119df45922abf0f5 |
It follows from formula (5.2) in the book of W. Feller {{cite:186b06c8e1cc9aba5951509c09f4a577cbcb63f6}} that the number of ways of writing a positive integer {{formula:bc577a5f-25cf-49ba-8059-ce89c07696db}} as a sum of {{formula:cdfc66d2-7cbb-480d-a2ac-253b21678462}} non-negative integers is equal to {{formula:0eb22... | r | bb13cf408c2a2f80c61265da77298354 |
The following theorem summarizes typical convergence results of randomized Kaczmarz method for consistent and inconsistent linear systems{{cite:eaa79075a7a4e106bae6efbf4becde38baaa7ce1}}, {{cite:0d634431f02e9c87bc3d862588d1bf2d27e62c9d}}, {{cite:35138b658f8abe8603dd650bdddf56be6009e888}}, {{cite:67bb7d8e3dc3df5e803e33c... | m | 15a913474903241e40d6daab05fff992 |
We quantitatively evaluate the quality of disentanglement in Table REF .
We describe two novel metrics to evaluate this.
To evaluate the consistency of appearance with changing geometry, we measure the standard deviation of the average color in a semantically well-defined region, which could be obtained via an off-the-... | r | cc71508f6fe513ddd4eae4bc8ed1a58c |
The model used in Ref. [takada2022transport] neglects, however, the collision geometry, particularly, the collision angle dependence of the coefficient of restitution. Moreover, it is assumed that all particles carry the same charge. The situation becomes significantly more complicated in the case of positively and neg... | i | d1488d6f6b9131bea3e2896519b46024 |
We compare the performance of our tabstruct-net against seven benchmark methods — deepdesrt {{cite:9cc7812741df58ad334f5311ef65f6db884bbfcc}}, tablenet {{cite:6ad9a9bdce3155d8cdc7b3976c811edc3b0e476f}}, graphtsr {{cite:0f6b1c8d73e7e4fc0c1a0b7768b0a9528488c80a}}, splerge {{cite:1210c7b6606545144ea1a98c1842929631ee7348}}... | m | d841b1f9a315c600232198f22fb30571 |
Applying the above estimate for the sample size is not trivial
since in general the upper bound {{formula:1731a33e-5c5b-4c58-996a-e14a22e9914d}} on the variance is not available; however it can be replaced with variance estimates
{{cite:e8152ad4071bb7a4d577b9ef30b91e9e17db60ca}} obtained during the computation of the ... | m | cc969db9c6ee19ad7b606fd34e737b55 |
In order to evaluate how the neuron model responds to external stimulus when
interacting in a layered system one needs to know how a single {{formula:41259577-0973-41c1-ba16-9bd821248979}} lattice of
neurons work. First, with {{formula:e61d1419-0dae-4b29-8695-95fa93964a44}} fixed, we explore the effect of the
control... | m | 9b34f0cb18ea45889d31cd70f023593c |
To better understand the effect of populating memory, we analyze 3 commonly used methods that utilize the memory buffer in different ways: 1) AGEM {{cite:e9ad176f676a80daf3b6e2fe5fc247ca5523426d}} which uses the memory as a regularization by comparing the gradients of elements in the buffer with the current task, 2) GD... | m | 591df37648d2feff29971583f9bcbdce |
Generic non-multilocality of quantum networks with multiple independent observers. The prediction of the quantum theory is incompatible with the local realism model {{cite:bc7ce2e56510ed0c8c61f8cbefe8f5e7088f2c8d}}. This feature is generic for entangled two spin-{{formula:13fce6af-f126-4c95-80cd-88217825123a}} particl... | r | 730c4dae31d4ea9a2bfcf7e9da02faa9 |
Early Degeneration for Contrastive Learning
Contrastive learning methods such as MoCo {{cite:88748665c8a366bdc589e27b2ab2fcdf291318da}} and SimCLR {{cite:522508d4f62d66669d22ae4d63b32b88b1a3e4ba}} are rapidly approaching the performance of supervised learning for computer vision. However, their incredible performances... | d | f0877bd4cc22ee815a80fe4eb629969d |
Firstly, to model the running of the QCD coupling, we can include the dilaton field {{cite:6972e58b3f7ccec659fe84fe83b822f8a9c58d5d}} in the background geometry and in the DBI action. In fact, various backgrounds with varying dilaton such as the various soft wall models and the Witten-Sakai-Sugimoto models fall into th... | d | c2c4ca3a62635855c3b10bf18221f6ce |
Despite the generality of the framework, there are a few technical limitations that could be addressed in future work. First, the assumption of the position-based model (REF ) is important in the current algorithmic approach, because it yields the linear structure with respect to exposure that is required in our Frank-... | d | 3ebdd3669400ec3670a3985caf933db0 |
1) DINO {{cite:17d85d8eff01272bd820504317b1ac9d3d9deb42}} is trained in a self-supervised way by the self-distillation of plain ViTs. In DINO, all transformer blocks enjoy the same feature scale.
The prominent characteristic of DINO is that attention maps of its last layer can learn class-specific features leading to u... | m | f0796a546acfb98626a4f0fc1534d432 |
The origin of such discrepancies between our study and the RGZ study might be a result of different selection criteria. Firstly, the total number of sources in our sample is {{formula:97195082-79a2-45b2-b61e-7ca19f45b77b}} 2% of that of the RGZ sample. Secondly, while our sample includes galaxy group masses in the ran... | d | 21c1be88bce6fd3ddc78e296d6ef1072 |
As language models usually fail when generalization requires systematic compositional skills {{cite:b28ed3349cbe06d677569ba48515ff6345e37458}}, it is important to
determine whether the probing model still lacks sensitivity to compositionality. On the other hand, capturing semantic relation from a static word embedding ... | i | 3fa9fcfea9f62ffa396a6d5d1d00fc16 |
Knowledge Graph (KG) represents a collection of interlinked descriptions of entities, namely, real-world objects, events, and abstract concepts.
Knowledge graphs are applied for a wide spectrum of applications ranging from question answering {{cite:c7bf1b330ffa4bceb9fd2a08b89d1329a2b1b391}}, natural
language processing... | i | fcc17302f40cd9f6cb35f2f1cdfc9066 |
The most common one-class model is the ocsvm {{cite:5588d959b360bfe000fcaf174ece7e589ac4221f}}. {{cite:cae8b50fdf7f3a3b05ff93cb518385666ba77fe3}} are the first who propose to learn one ocsvm model for each class in order to reject novelties using the ocsvm as dependent rejector. Differently, {{cite:0969a4c655bac00588b9... | m | 48d40c324a54a27885c7254d4ddaa4f4 |
Domain divergence relaxation.
Our foreground-aware stylization can be thought of as a solution to relax the domain divergence.
Theoretically, {{cite:5b88add4e34e9b03cfd5847f193dbb59147f634c}} showed that the expected error in the target domain is bounded by its source domain error and a divergence measure between the s... | d | e7320c817f7c0b5efb8d90631a69d5e9 |
We test our ZB algorithm's ability to correctly detect communities on 100 network realisations of Girvan-Newman (GN) {{cite:41f14553fe1d683e442e8b69337621b753ff3f50}} and Lancichinetti-Fortunato-Radicchi (LFR) {{cite:dd07716451c6778bb2031127d88c2475aab13257}} benchmarks. 4 examples of these reference networks, {{formul... | m | 61c82bca773a157f03330329eaa0d8c1 |
The past few years have witnessed tremendous progress in deep learning in many if not all machine learning applications, such as computer vision {{cite:8a08710193f66a68973ac5827f1d7b4a0dc0fad1}}, natural language process {{cite:d0b5acdaee28700d922b20fe6de9cdb7103d6246}}, and speech recognition {{cite:8bb65348dc4390d8af... | i | 2179b02ea8f9197ed71f9e49fd39d412 |
Further, taking the {{formula:0514496e-30a1-472b-b309-2fc1a46eb921}} term in the Finite Fourier series expansion (see {{cite:1216d293b0ec51ed14b252c2e36bd386cf4e6e95}}) of the terms {{formula:a91a6591-149a-4e39-96cb-2fc49aad6281}} we obtain the decomposition
{{formula:420def2d-2e9f-47c2-86e1-aee1c07a1b93}}
| m | fcf310589a8cb1cf859fc0c74b711c1a |
Note that, motivated by the idea of the cross-lingual language models {{cite:06150039e8c9af63594e973f58d580340b2135d7}}, we adopt a shared encoder across domains to avoid over-parameterization and achieve further information sharing via parameter-sharing across domains.
| m | d16a43445e2c7422d06855c2931a9bf9 |
Discussion on collider based search is out of scope for
this work and we will focus on the second method. The same was initiated
in the seminal work of {{cite:0cb6072952a5c159c1b9abc154ecc890b4ff735e}} and then followed by many others –
over a considerable period of time.
| i | 6889f1c52831cf2b3ebac9cc0cbb0cf7 |
In this work, we present a Mixed-Domain self-Attention ResNet (MDARsn) to achieve two goals: coping with domain bias among different sources or between training and hidden datasets, and learning the relationships between ECG leads and CVDs automatically. We adopt feature-based augmentation method from {{cite:0c0756309e... | m | 042b4bb7bb3817f49f8ec2fbcaff0cfc |
Next we turn to comparing GN+PN's performance to BN's performance across a broad range of models trained on ImageNet. As visible in Figure and Tables and , GN+PN outperforms BN in ResNet-50 and ResNet-101 {{cite:d1002d624a6f341fff57f5c69ecaf76c011069d6}}, matches BN in ResNeXt-50 and ResNeXt-101 {{cite:2d9290420c3419... | r | 990a86929e03a1a530ed5b3eee3ebfa6 |
The comparative results, as in Table REF , demonstrate that the proposed PAS method with the CT scans outperforms the recent state-of-the-art approach in {{cite:7e4778a436eeb3145177c63d6139bff9751935c8}} by the margins of {{formula:05fbe839-5277-42ba-a0f0-a13d98264968}} and {{formula:c8094af9-8468-4d39-bc5e-9e423e7bc6... | r | b086d14169e467cd381ae4c4f61c79ad |
In the event the new model reveals that ODDC in stars can indeed be stabilized against the {{formula:12b5a8a4-0f82-4fd5-bc7e-8e6c3d01abda}} -instability by a sufficiently strong (but still realistic) magnetic field, this could lead to interesting observational predictions. Indeed, {{cite:beb3b600382bd835b902347a2dbbf3f... | d | f68bad44760957e4286a49948c2eedc0 |
D*0-Ix+i Iy-(H0+Iz)
,
Here {{formula:33c17ab9-5747-4830-a3a3-3a88e2ddb059}} , and
the expansion coefficients
are written in vector form:
{{formula:20b3f6a9-b149-4aa9-aed2-317e8f92cfa6}} ,
with
{{formula:3c473f07-cf24-43b7-b941-281189b1b9f1}} ,
and {{formula:041964ee-fe47-4533-bf39-854dbd028eb3}} .
The rank of the mat... | m | 69eec706229aca477f5ab79206e6a128 |
The essential ingredients of LVE are the Hubbard-Stratonovich intermediate field
representation {{cite:b50b9243615bd42aeba8ed140a332644f24257d3}}, {{cite:f6a3d91550683fbf3d18814f738b7c6ac0ea7974}},
a replica trick and the BKAR formula {{cite:d346495827a8ca05e44f93a473e04bea65d8a3bf}}, {{cite:180f389822dbcf0fe94682dc5c1... | i | c6adc5979630d67d4dd47c51389b5667 |
Finally, we should stress that unlike the scenario with the Allee effect in prey - which is currently considered to be straightforward in the literature - including the Allee effect in predators can be somewhat tricky since this can be done either by modifying the functional response or the numeric response of the pred... | d | ad0676d40a30e0741e7491a8ae72fb60 |
Once again the performance of the inflated (I3D) version of our model is much better than that of the 2D backbone version, showing that by simply switching backbone one can improve on performance or other desirable properties, such as speed (as in SlowFast {{cite:f2083dba1a00289f6853ff1c424b6c5c4eea3546}} or X3D {{cite... | r | b93f02277f00e2a2c187fb630de9fc1a |
Both for black hole (BH)-LMXBs and NS-LMXBs, around the soft-to-hard state transition, the luminosity shows a fast decay stage, which is the `knee' feature {{cite:b19b196b1139e4326de4dbeb3b5ddca038c313fc}}.
For the NS-LMXBs, it could be related to the propeller effect. However, for BH-LMXBs, the propeller effect is not... | d | 895352b86c41ff236165e9a8e37a9900 |
More generally, superconducting circuit QED is a particularly promising platform for experiments in quantum optics and quantum physics generally due to its flexibility and readily available set of microwave tools. Possible applications include exploring the ultra/deep-strong coupling regimes of light-matter interaction... | r | 85fc515057e0b8e788ba502289588ae1 |
For example, video-paragraph shown in Figure REF (b) is clearly more coherent than Figure REF (a). This can be attributed to the ordering of the discourse relations among the sentences. In Figure REF (a), the sentence S1 is the cause for sentence S3; S2 is the cause for S4. In Figure REF (a), the sentence S1 is the cau... | i | a882b0bd820d79ef0268112a9bce10f5 |
Phenomenological studies of high multiplicity final states at collider
experiments present a substantial theoretical challenge and are increasingly
important ingredients in experimental measurements. During the last 15 years, a
dramatic improvement in computational algorithms for one-loop amplitudes has
led to a number... | i | 8112926eac8fcfe37d339c2214eb6354 |
The family {{formula:ba36feca-f5f0-4c5c-962a-e3e4ae3af95c}} in Ref. {{cite:09b39929f5e6bbe83b6f8133f612425bb104a1c7}} was defined as
{{formula:b167f3d0-4f02-4409-9903-2e0303dd5243}}
| d | 0a053b2ffd410adedec624851621b80f |
It is natural to ask if the effects that were uncovered by our analysis are specific to gravity or if they would also arise in electromagnetism.I thank Norbert Straumann for raising this question. The calculation I summarize below resulted from that inquiry.
Are there interesting quantum effects on the non-radiative, C... | d | c4be088be39628a67435214c6eb89399 |
Sequence labeling is one of the most fundamental NLP models, which is used for many tasks such as named entity recognition (NER), chunking, word segmentation and part-of-speech (POS) tagging. It has been traditionally investigated using statistical approaches {{cite:4c664ae89bb633f6ca47e75c33d308e8fb7df995}}, where con... | i | 54ec392ff715ab0335a503e84ff47d76 |
We run the algorithm using AlexNet {{cite:ddeb0be1d93e32b7c376167428e069d776f63e3f}} and ResNet-18 {{cite:e4bef8fac5b0dab92296669594a8b67fd55a6f25}} with EMNIST dataset {{cite:9761cc5ea8cd8640b2de04a06dcbad6d095a9b95}} and Cifar-10 dataset {{cite:7112206ac1532f625e49ba996b9cb42a0e8013e0}} for comparison. We split the d... | r | 586ff45f16a0804335210fa941387553 |
We test our speech dereverberation models on reverberant speech generated using RIRs from four RIR datasets: BUT Reverb dataset {{cite:0c508e25d008d112d7b838a61992155d99db67bb}}, MIT IR dataset {{cite:71e64b393d3bcb8cf00a7b76f181c3e700cbe8c7}}, RWCP Aachen + REVERB RIRs {{cite:118684d820f59ee08cc11faee2cd10a50cd9f11b}... | r | d75340864f61ff7cb56295f41bb7b781 |
This work was supported by Grant-in-Aid for JSPS Research Fellow 22J01501 and
MEXT Quantum Leap Flagship Program Grants No. JPMXS0118067285 and No. JPMXS0120319794.
Review of the random Fourier feature
Machine learning with random Fourier features {{cite:d14ee9b8eae5eb562f54d3145dea1adb6b2cbd32}} is a widely used te... | d | 061563e4ef19c8817a32e83a85ebc321 |
The Standard Model (SM) of Particle Physics has relished a lot of success owing to a multitude of very precise predictions about the features of the subatomic world and their excellent agreement with experiments. The discovery of the Higgs boson {{cite:3fa127619d785eadd69190a91efbfde381eefafc}}, {{cite:1e0a9c57c189a72a... | i | 6a4180d97034c9a23a80a2224bd73ee0 |
Although we have focused on the donation game as a well established model for a social dilemma {{cite:d47c5b7144c53f1db287a684e7af985b9cae6e22}}, our method of analysis provides general conditions for the evolution of cooperation for arbitrary asymmetric pairwise games (see Supporting Information).
| d | 0415c4faccb2f57d1fc1f7386028a4e7 |
To understand the difference between our approach, UBaPP, and the Laplace mechanism LBaPP, notice from Fig. REF that, for low values of {{formula:87652c72-8418-4d5f-8715-0986b70bb1c9}} , UBaPP outputs a deterministic estimate around {{formula:b99c4b86-c639-43b8-abd4-f53c6d3c46cf}} (which corresponds to the mean of th... | d | 87169f741a6e91cd4334dfc20be6b11a |
One potential limitation of the current system is the scalability of the parallel coordinate view.
As for more complex datasets, such as Imagenet (1000 classes), visualizing all class direction at the same is not practical.
However, practically speaking, it is important to note that even with a more scalable visual enc... | d | 29e2520fedc1a2286d15d826b2948239 |
Perhaps the most striking result from these calculations is that for efficient shocks near 1000
{{formula:de359da5-c946-4cb2-b783-f5d6c878979f}} a substantial number of H{{formula:24a76924-3dc0-4495-99aa-be615411b9fe}} photons will be produced in the precursor. These
will usually be included in the narrow component, ... | d | 2366ceb354eb870575ef78583c87d180 |
Results also show that our use of a z-buffer outperforms the consistency-driven heuristic method of Gordon et al. {{cite:3f1866ed8beee02049da2a894ffa75b3276ad3bf}}, even when inserted to the training pipeline with the same timing.
Their method excludes every transformed point that is “behind" the predicted depth of the... | r | 9ba89a8e30661d627c2c7939fabd9d76 |
We simulate our experiments using Raspberry Pi 2 as our user device with Google Speech Commands (GKWS) {{cite:76c9e37448353ffe2a7c71a1e00192d4d6ee181a}} and UrbanSound8K (US8K) {{cite:a67374a7e1f3b6eca56f02610be4148e7dfd2237}} datasets across 10 FL iterations/communication rounds using our proposed framework. In GKWS, ... | r | 3625c0632ed50b365d5aa5fdc3e8dfa9 |
Of course, with the benefits of GPs we also inherit their limitations. GPs are typically slow, naively requiring {{formula:fc244933-b382-4a0a-91e2-88811d2bc59f}} operations in the number of context observations, and our model inherits this complexity. While this was a non-issue on the time series data used in our work... | d | d6503c8edc13cfc02022bd832cc49af4 |
In order to challenge the generalization capabilities of MonoNHR, we also train and test it on the HUMBI dataset {{cite:7fd8013ee7e870ddd55af92bf5b4768a879ce3be}}, which has a high diversity of human subjects in terms of body shape, age, race, clothing and accessories. We train our method on 9822 frames, taken from 334... | r | 3e204ce17696ad32a932b753290a83cb |
The existing methods {{cite:bb1b2d3c8aab1cd26aa300caf0431649b0b75f92}}, {{cite:a3026ce507c498d37283e2d9132d8d791d0c9df8}}, {{cite:d4890222b3f0f39e66151e5367c6711e314e2751}} formulates the GEBD task as binary classification, which predict the boundary label of each frame by considering the temporal context information. ... | m | d25de418c0727ffcd85a2794f271cff6 |
Recent advances in Artificial Intelligence (AI) and Machine Learning (ML) techniques are massively revolutionising healthcare as new capabilities of automation are being applied in electronic patient record analysis, radical personalization, medical image analysis, drug discovery, etc. {{cite:94a219cf9f7dad1aeb15a1bc1c... | i | f5debef86f2fa8c0f571a60ba58ad69e |
Existing methods have a clear limitation in that they directly use pretrained features to predict boundary points.
The features are extracted from the classification-pretrained networks like ResNet-50 {{cite:882757728976d76f558842d1ebfe88c2d728593a}}, so that they inevitably contain class-specific or object-centric inf... | i | 3707bc9e2fbe7878dd92696ef5600978 |
Lemma 4 (Schwartz–Zippel lemma {{cite:04e090cd2ae90d50b669e2e787fd0d0a232a411d}})
Let {{formula:ab7bbde7-1d4a-440a-bc06-2236e743d135}} be a non-zero polynomial of degree {{formula:80f2a48b-3925-4e63-92ea-007b8745f9d2}} over a field {{formula:402d41a6-2642-4ea7-aa96-7b1232d4a501}} .
Let {{formula:d2048653-4883-4e4e-b... | r | f7e257ce7fee9e579e891b3c7801e4cd |
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