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Classical numerical level set methods {{cite:a9420e74e6bf65bd6a86564b0f36c83d14f3d251}}, {{cite:bab2a199a57ef503d35a0ae3a464720a4c92a76f}} attempt to directly find level sets in the input domain {{formula:68d47616-2648-4e3c-915c-71a5595ba080}} via boundary evolution.
Let {{formula:a9aaa9ac-ddfa-4f14-bd4b-d1878fb7b043}... | m | c62c45753859215c0b90c42e4f8166d8 |
is called a {{formula:87e70250-9356-44be-928d-fdad6691ca7e}} -set of {{formula:a638d6e6-4dd2-43b1-8312-8bf3e9608831}} ; see {{cite:f4dd03f18d9cce5b6dda77abff430e65309441b1}}.
| r | 79d8207558dc13ebf34b8100b01b6ff7 |
We tested our new algorithm on two examples from chemical process engineering. Additionally, we compared the performance against three other algorithms from the literature, namely the original Vector Direction Method used by Atkinson and Fedorov, the algorithm by Dette et al. {{cite:1fbc2ff9fcf51e86b692a2ec795489f22d5d... | r | 8f653a5f2bde8cc32022b003428ac939 |
which is equivalent to the divergence of the radiative flux
{{cite:79e28d71c2e8acca75277512431b338c284efebc}}. the The opacity
is {{formula:a686ac06-94db-40d9-8382-5a2b1044b08f}} tracing gases with two different
opacities {{formula:dbe60b29-0fde-4949-8bc3-7ad86b62a9e9}} and {{formula:ec3fb080-2e28-4ddb-b078-902a296cb... | m | f1b03f8cce018f5c4a451f329250e809 |
Currently, many tools aid in signal processing, and some of the more widely used demodulation techniques include adaptive optics, background noise rejection, relay transmission, and hybrid RF/FSO communications {{cite:f2384d26e72afbda80e3d31a4a35b90b09aff0ab}}. While techniques such as adaptive optics aid in demodulati... | r | 76c82f42773fb87880a4641567c71b13 |
Much of ongoing research in the discipline of high energy physics (HEP) require using high-end computational resources. Examples include analyzing petabyte scale data collected at the Large Hadron Collider (LHC) {{cite:b779182315f785410c7ef0e75c3ef0b8dbec5ece}} or obtaining precise theoretical predictions that may requ... | i | ee1c8a9cb10805ae3f72e454d111377a |
We now turn our attention to the {{formula:5ebd0520-c7b0-48fd-8b86-b2d6cf9a6cf9}} and present the longitudinal and transverse LFDAs in Figure REF where we juxtapose them with predictions
from LQCD and QCDSR. Notably, the longitudinal distribution is a concave, nearly symmetric function of {{formula:0e6b4676-0d1e-420a... | r | 2f4467feb27209465e6baee0c5bcc64b |
Yet another avenue that might help disentangle the formation mechanism for this companion is a comparison of the bulk composition of the secondary with the primary. The spectral inversion technique can successfully recover gas abundances and atmosphere properties of brown dwarfs (e.g. {{cite:3f7feadf9b216e9fc4a05349c37... | d | 6477f8fa580b71a153f5a76d5fe99b25 |
Clearly, {{formula:2a5bf4c8-ff61-483b-9255-6bf316ce1eff}} , while, for the other coefficients, we have, following
Biggs {{cite:e982d2adc4e2c663414f0abeb5a25aa8edda17df}}, that
{{formula:46da7b00-0cfe-4794-88fd-64cb09cce627}}
| r | 6932031a7212acede8deb442ef8418be |
Mobile hardware accelerators are usually limited in the types of operations that can be massively parallelized for fast execution. Thus, more complex quantization methods are often not supported by existing hardware. As such, other works have focused on quantization-algorithm-specific optimization methods (Eg. targetin... | i | 68b1b81d77898a9e13d247883a60a84c |
To this end, let us first introduce a class of cycle statistics. We will denote {{formula:724a388a-38ba-4e34-9d7b-452fbfae3d4c}} as a cycle on the factor graph corresponding to the posterior distribution {{cite:b654ab040cd1f143733b9139c529e851815808dc}}, shown in Figure REF . Specifically, the factor graph is denoted ... | r | 8da708bfc80a2fc0ca9f0bdbf13dbb19 |
We evaluate our models using the official evaluation protocols, i.e. AP{{formula:95d6cf9a-010f-4126-990b-576df183df1e}} for UAVDT and mAP and mAP{{formula:2f7d86b2-2378-4cbf-b5a3-50f6d507e76f}} for VisDrone, respectively. Furthermore, similar to {{cite:a5a232eff4d4527d44e123b6158ee1fb34244215}}, we report results on ... | r | 6540681b30d1364bfe6c17df2b14f21d |
Table REF presents a comparison to the state-of-the-art techniques on graph metric learning. In particular, we compare with the different architectures proposed in {{cite:6aaf9984aef99ce607a5fa0aff13175c68b63fbd}}.
| r | e61dd10cdb6b4cce02201eb314f528e8 |
All experiments were carried out in PythonAll code available at: https://github.com/JamesFitzpatrickMLLabs/optlearn. Graph operations were performed using the NetworkX package {{cite:3f5f59beb6b8549b339b5c92c8da1667cd71d0dd}} and the training was carried out with the Scikit-Learn package {{cite:1e2db4baac5bc8dd738f0dff... | r | 27dd2bcf3c109853f6a9bff59c1525e9 |
The multi-label task for assessing the aesthetic quality of images based on different aesthetic attributes like aesthetic, memorable, and attractive attributes using high-level semantic information is explored in {{cite:1a7f86ec148b62ce4a125b5d468d733920b36809}} as shown in Figure REF (a) by designing a Bayesian Netwo... | m | ade27ae1e097f930bcfe7467efaeb14e |
The exclusion limits were calculated by employing the multi-bin limit setting technique in a program based on
RooStats package {{cite:9824c81f8cbcd244d54b96521f846877d2da8120}} with the modified frequentist approach, using the profile likelihood
as a test statistic {{cite:dfed3422c11bccabddd572b95f16d0dec53e8844}}, {{c... | r | 225953f89d70f3b3336d300d1237e363 |
We illustrate the performance of our algorithm on both simulated data and a real-world marketing data set. We focus on the generalization error of the minimizer of eqn:loss, rather than on the optimization performance of alg:asyncl2gd,alg:asyncal2sgdplus, since related approaches have been studied in the single-cluster... | r | d8e462b1a4d2a006898e11e8ed6216dd |
We evaluate the performance of the proposed receivers by Monte Carlo simulations and compare them with several state-of-the-art methods. Consider a MIMO-SCMA system with {{formula:d7eda718-c0d8-4e5d-bb42-36678cc7773b}} antennas, {{formula:ae01d707-3330-4182-afe8-f47143618fa9}} users, {{formula:5e3be212-9c26-4e07-895d... | r | 042793eba0929fb6019bffb4b51d034f |
where {{formula:b98ba8cc-2699-48e6-8893-b6549e2dee9c}} .
Note also that for {{formula:5090179a-d33a-4f9d-97ef-d34f46002e12}} the classical Azuma-Hoeffding inequality (see, e.g., {{cite:622b618f79de07b3bf2f3cb22b75925263a15592}}) allows to replace the constant {{formula:f7ca8042-6941-417b-8e9c-b06699d2e43a}} by {{form... | r | 408c75dd4a08dfdca0e7ef521122c999 |
The online nature of our learning algorithm allows it to be used in real-world applications where training data arrive in a streaming fashion, and learning needs to be performed incrementally {{cite:6b2af5cd8f5798c6a13c6b243d018b46d601fe7e}}, {{cite:be8b99175308c30615f1394baabf92e415396538}}. Further extensions may inc... | d | 13361378cf4695974971e8b333677d9a |
So far, two ways to supersymmetrize the Randall-Sundrum model are proposed in {{cite:6821a91a77851d8fb85c1efb6781788eff0d6170}}, {{cite:71e9a9d516ff251c176852d67ef0030531af09f7}} and in {{cite:bb06b2dbb5f1f86867be3d3ebc53911cc7f0b9bc}}, respectively.
The former involves a kinky gauge coupling which has position depende... | i | 56c9ed5ef4eb73c6ff542993bcc70739 |
Quantum phase transition occurs between different phases of matter
by varying the driving parameter, such as magnetic field, chemical
potential or interaction strength, at zero temperature.
It is driven by quantum fluctuations associated with the Heisenberg
uncertainty principle rather than by thermodynamic fluctuation... | i | 24e20d59c1a1c69b6757cf8e5f038dc1 |
Following the previous white-box source free UDA {{cite:dd093c002b3b9cc971770eee92cc5049420816b9}} and UDA with source data {{cite:0f1b83765d13e4f825b14ea3dab05eac20604f87}}, we used HGG subjects as the source domain and the LGG subjects as the target domain, which have different size and position distributions {{cite:... | r | 0614ce42a231fa5ac00edb081beac21d |
There are several directions for extending our work. One important area is to move beyond normal sequence models and consider multiple graphical models. The graph fused lasso method for estimation and structure learning in multiple Gaussian graphical models {{cite:fd4dc0787a135c488f8fc394a34269854b98feb6}} has got much... | d | 0a4a555068f2fde55bb3171702e26e5a |
In the “standard cosmological”
framework for the early universe (cf. the books {{cite:ca8783faf36afba6401f37986536ff1720990624}}, {{cite:164b75821b147f149f2e2f2de4e0086bbaa16859}} and references therein)
the universe starts with a period of exponential expansion called “inflation”.
At the same time, after the discovery... | i | fdb32f4d954142cf69a93c256be79a27 |
In this paper, we characterise completely a flag Hardy space {{formula:97fa5c9e-72b7-4454-ae41-44b501dc90d3}} on the Heisenberg group {{formula:068b602f-4ff5-45da-a61b-6eff0c91d245}} .
It is a proper subspace of the classical one-parameter Hardy space of Folland and Stein {{cite:620267dcf523fc6fe10dd9168a7299e2ac0a986... | i | 6dae66ec69c8bb78ce3b31c4b245dad5 |
Sampler for meta-learning. Tasks in meta-learning are heterogeneous in some scenarios, which can not be handled via globally sharing knowledge among data. Therefore, it is crucial to address the task-sampling problem in meta-learning. {{cite:deefb5bee2eee7113bb5345ed1a29803cd2ce955}} assigned many tasks that are random... | d | b95bc77983a87adca46f9fbe19e309f6 |
In this section, we construct many group LCD and group reversible LCD codes using the code construction {{formula:5b018994-60cc-4640-b1c6-8ba7af1040db}} given in Equation (REF ), where {{formula:5a6cbcd7-8821-4a3e-9330-ed1bb93f76b2}} are some of the {{formula:1e764699-8d7b-42ba-9824-88a05ca1fee1}} matrices described... | r | 0332d1bc06f86a5b91fab06d4721f417 |
The results of mass-radius relation for NS discussed here and shown in Fig.REF . The constraints from the observables of massive neutron stars, PSR J1614-2230 {{cite:d4fa27b1db1450d3a3867f4ca63f6fc2b8a38961}}, {{cite:d38a62a2137d339a439416aac4a43d137b91a100}}, {{cite:0909e6e7efba39b1351d31217fa7c534398e2a43}}, {{cite:7... | r | 4b9264ebd993fde89226e36cedc0cd20 |
In this work, we propose a novel Graph Relation Transformer (GRT) which uses rich, vector-based edge features in addition to node information for graph attention computation in the Transformer. The proposed GRT outperforms the M4C baseline model {{cite:e0b5f8fbba52361aa912b4678691eba1b62721f8}} while also improving the... | i | 8f164e5ac59a1ca1ab7f4ebe69f1df2d |
Our source, like the majority of photon sources, has a greater than 50% vacuum component. This property makes the generated heralded photons less suitable for quantum computation applications {{cite:fccb22e59e4f22bdd4699e1f32701d0738e29543}}. In the future, this limitation may be elevated by using detectors with higher... | d | fb83f0b5ec444a0673e0a3043fdf1434 |
Nuclear recoil (NR) events from background sources including those from
detector components, surface contaminants, cosmic ray muon-induced
neutrons, atmospheric neutrinos, and so on, are expected to be small
over the duration of a typical SN burst event. For example, the LZ
experiment {{cite:61914d5b711bcb98b809904522d... | d | dccf6d2a73cdc6efc9082d10224097bb |
This comparison of our results with the existing literature {{cite:d34a5f5d87756add5756105e7211a4e2973e9957}}, {{cite:ee668b49fd1e4025aeeeeab72e3d42cc94ecaada}}, {{cite:e036e35428d661293a19f3bbf2b201c658cb7c06}}, {{cite:9f0475d964e4d77347655989147a41b7bf38a3f4}} brings forward the issue of model dependence in EoS const... | d | f6e720247afcc06719436464dd5693ed |
On the other hand, recall that the regularizers {{formula:f46b0d2a-1f78-4c08-ba7d-ce03ef2c9328}} are important in controlling set sizes, so Figure REF examines the marginal results over 100 different pairs of {{formula:d2ecc9a9-b773-4078-aa52-231ae0670c69}} at a fixed {{formula:ae728087-d59a-4402-bd74-d34133085d74}}... | r | e9b711f23e076bc2011d54a83d4df337 |
We note that all of our results hold for simple MH algorithms as well as refinements such as
Hamiltonian Monte Carlo {{cite:e2199630601686f232fbbefea478e409a3492967}}, {{cite:c25834304b198e509e794bc6874f7e88db9905b8}}, {{cite:dd27f6a7ac56c83cf2e63f0128d68e188e7c374f}}, the Metropolis-adjusted Langevin
algorithm {{cite:... | i | 082a790cdd3464a368ca2cc9c95e5189 |
Through diverse experimentation, Kolesnikov et al. {{cite:3e1744937cbf59b2e12f67bc8cef68d4d0ee9d18}} showed that neither the architectures are coherent with the pretext task nor the self-supervised pretext task is consistent with the architectures in terms of performance. One possible future direction is to develop suc... | d | df9951f66edad7b5de64f166855e8286 |
On the other hand, a number of astronomical observations confirmed that the late-time universe is undergoing an accelerated expansion, such as observations of type Ia supernova {{cite:41a0c43cd0dd34ac502e277c84d2382060e34f55}}, {{cite:b454d2ec613d06a20ed782477ef468a0056d2df3}}, {{cite:2f2df911e06711e5c1ec92b1ebc85d2f0f... | i | f271b6f118ff3307b8584a901be3f471 |
Domain Generalization by Solving Jigsaw Puzzles {{cite:307889b793246bf6c7350c1ea8ae763e31f0b24a}}.
This paper studies two setting. The first is the robustness setting, exactly the same as in {{cite:c414fda2bb29bb30bbf5c0f6598ffa0e524f16e5}}, except that evaluation is done on MNIST{{formula:4885c58c-0516-4654-a16f-e91ca... | d | 4587ddfe569d618c2b27ee0e65c7bb67 |
In this section, we present results for object detection with the EfficientDet {{cite:c30b1864ac2e6a08b6511a89b9f3af07bfb8bf50}} model. In particular, we focused on the D3 variant of the model as it has shown competitive results on other object detection tasks. It has been demonstrated that EfficientDet models can reac... | r | cc6ea27d0bf505c4049a53e550de5840 |
Intermediate Pre-Training. The second group contains models trained on specific content types and domains, i.e., SciBERT {{cite:9812c2ec09f0f3c39c289dd8e9112c8ecc792d30}}, BERTweet {{cite:057e66786ae4f73c6210ba09b0f1f828dae5c437}}, and BioClinicalBERT {{cite:c4562d18941db4424d890996c7d2cdb90a6b643f}}. For example, SciB... | m | 722b1045bd5fde83b8ae5bfac73407f9 |
We consider various other attribution methods for our analysis i.e. Vanilla Gradients, Integrated Gradients {{cite:3edc9a2b1223e0dff8727f1150f3eec5fcb45fa4}}, Vanilla Attention, Attention * X (or inputs) and Explainable Attention {{cite:71b615a29ec8454ad1fe0357004384408befce1e}}. We do not consider techniques such as L... | m | aafd2c882191137de7b6fd96d7778570 |
{{cite:63b34ed7571b3af3ed2787a8247d1c0989287777}} argued that the magnetic carpet on the photosphere continuously pumps out the 2D structures along with a minority population of Alfvén waves (i.e., slab turbulence) above the photosphere. The 2D structures advect through the chromosphere, across the transition region, a... | r | 767b08c22f58d3d2afe5609e34bdc869 |
Nearest neighbor (NN) search is an important computational primitive for structural
analysis of data and other query retrieval purposes. NN search is very useful for dealing
with massive datasets, but it suffer with "curse of dimensionality"{{cite:8d88595c7ab767ed5f56156923ce622a68612915}}, {{cite:c9c09fde6ea698b51bf16... | i | ebb02e533d9e2771ad1758aa628d0eb4 |
While adding robustness to machine learning models often leads to better generalization errors on perturbed data, the accuracy on clean data often decreases compared to the non-robust models. This trade-off between robustness and accuracy was extensively studied in the ML literature {{cite:0aca7052012f01d7f8eb25f361545... | i | 56ef96bbb7c0cb33c2aa8a06a8c8a4ff |
2. The aspect of quantum information. According to “{{formula:0a7c1b71-f1f9-4929-aef9-5e2a36277389}} ” {{cite:19f162e6f54473990ebb31469877cd222f4cff4b}}, the entanglement between the DOF of two subsystems produces a connected geometry–wormholes that bridges them to each other. However, the island formula only provides ... | d | a9d6df2535ae024562cb7ae1d94edc8f |
Recently, reinforcement learning (RL) algorithms have outperformed human experts in many fields that require decision makings {{cite:f19554fabd21ce7804cbec37719bffc1933ac63b}}, {{cite:606bf3ff3925dbc8b64ae347c5010e5fcc7e7b9f}}. In contrast to the traditional control methods, RL can complete some challenging tasks in le... | i | 2de0cff3d085af7ee9279e66481e0aca |
In this section it is discussed Model-Agnostic methods, one of the two taxonomies from the Method-Generability approach. The Model-Agnostic Methods do not depend on the specific model used; instead, these are methods that separate the explanation from the machine learning model {{cite:deba91a6e497cde4b87e1ff96827e9af27... | m | e1284bfc948f23de4c6b67c3e4b6130c |
Note that we include in the definition that the derivatives are uniformly bounded.
This is not the same as the Whitney topology on spaces of {{formula:b63e704a-5b05-4940-b705-6294f2cc1dd0}} times differentiable
functions in a {{formula:2c83806e-168d-4ca9-a5da-c711993407ab}} -compact manifold {{cite:bbc280b8ffb30266ac3... | r | 585a88268667d02ca386aa8e04a61425 |
All of the circuits in this study are trained in parameter spaces {{formula:d81a89dd-93b5-4af0-aa5e-01915b835394}} with {{formula:0d9b178d-7ab5-49b6-825b-0b1b79c60d08}} . Contour plots (two-dimensional cuts) are defined by projecting points in the {{formula:7f7854ac-94fc-4f89-b22f-f01c281cd37e}} -dimensional loss land... | m | ea2df7573dde1e0c78189458f48f7775 |
In this section, we explain DynamicTriad {{cite:b8b2419b8c0d43f7477caf3caa83c7570601fed7}} method that we could not fit into the above categories.
| m | 563316e0fdbbc5c794274b81660bff85 |
Now, we seek a condition on the cocycles to know when two {{formula:547d6e62-e4e2-43e7-883b-d4feefdcf512}} -central extensions are isomorphic.
Let us fix a basis {{formula:d26abbee-7e4c-44ec-94f5-c42278fbab07}} of {{formula:88e165fe-e3e3-4923-bfdc-cf88b434d403}} , and {{formula:55a3ad63-3726-4ca7-9d29-27bec6cc684d}} .... | m | 9e4af2a527ced3ef12cffc246cbbeb36 |
With the proliferation of large-scale pre-training data {{cite:97ec63f3c7d0c34eb56ab6a48fe2c2958bbef350}}, {{cite:66f2465e4d2d121302cb8fc7d9c2185851566550}}, {{cite:fc41ad17cd5d5eae97fa1775949f4c2877381779}}, model size in neural networks has also been increased correspondingly in order to reach a certain learning capa... | d | 86a27c3699644bc826822b072ae39025 |
While our approach learns network architectures of increasing complexity by adding connections, network pruning approaches find new architectures by their removal. It is also possible to learn a pruned network capable of performing additional tasks without learning weights {{cite:6dcbaaf792d1af061a2dfa8a3749c7df86c063a... | d | d4a550196a818ac895f1f764142018f6 |
Spontaneous polarization can occur in the AB{{formula:633fbeec-9730-486b-a1c8-b87dee5621db}} -stacked few-layer and bulk {{formula:787a295d-77ad-49d4-b1df-5dabfcccc96f}} -GeSe because their atomic structures are noncentrosymmetric.
Group-IV monochalcogenides such as GeSe have already confirmed ferroelectricity in other... | r | 9bd2ea3af2af47503ba11364c7e289cf |
For the proof, we leverage the algebraic characterisation of hitting times {{cite:9b1604d9feab4216ac49feda0eb09fe06f384710}}, as opposed to the standard analytic characterisations. Indeed, the hitting time matrix can be obtained as a solution to a certain matrix equation. Our key observation is that the matrices in thi... | r | 45b447eca6b4e7eaceba2b3796e8605c |
Musicnn shows competitive results in MTAT. However, other models (sample-level + SE, self-attention) outperform Musicnn on larger datasets (MSD and MTG-Jamendo). This confirms an intuition that domain knowledge can be beneficial for relatively small datasets, reported in {{cite:9ada7463ee87638ec9466e2742785229a7fede4f}... | r | a3e87d6384425119518e8408b75be838 |
Monte-Carlo Dropout. We implement the MC-Drop method as introduced by {{cite:0d4d096a8d6a710dccd6062355ad09cc70412e26}}. Following the work of {{cite:1fc956e447a3ce0be6cc4ea5b9f7c6cd2e7fa3d6}}, we configure MC-Drop with a dropout rate (DR) of {{formula:7dc83392-14fe-4158-9a07-e36eaab05948}} . In line with other methods... | m | 73083c103083f21aea1bea7795fdb4b3 |
We first consider the class of sharp configuration which are even order designs. The definition of a sharp configuration was first given in {{cite:fba4d6811c29f463912bbefda52e2323555a09fd}}.
| r | 101ba1be90116423d6fca264ce52c668 |
The complexity of ICA is harder to characterize. Firstly, the FastICA algorithm assumes that the data has been centered and whitened {{cite:c0a51be09eb38c273be2a763f0439f0ce8febe46}}. The implementation we use, from scikit-learn {{cite:3c2f5d909def2248dc4d6a50cd181abeaed461b2}}, uses PCA to do the whitening preprocessi... | d | f003e87fe2e3d22a49ccc9a935a84f76 |
As shown in Table REF , we compare our model against class-supervised, visually self-supervised, and textually supervised baselines. The results of class-supervised and visually self-supervised baselines are obtained from {{cite:f4c462a9d52e13e0b1697a2530e8ed5423ebddb3}}. They are pixel-wise classification models finet... | m | de4b80643455c9746affb7af8ff7d576 |
Lattice Monte-Carlo simulations with magnetic fields do not suffer from the sign problems.
There have been lattice studies on the chiral and deconfinement transitions {{cite:3d53fb7bff964c8ed2cd15790c3437ce0fb45fa3}}, {{cite:33be418a78abf12f864fa6f73b1870d7ce95ea04}}, various condensates such as chiral condensates {{ci... | i | 51d4a0d28f73d949670a2a0966995836 |
Next we compare the performance of the proposed approach and the classic feedback-based algorithm. For fair comparison purpose, we still employ the factor graph and message passing algorithm for the feedback-based schemeExisting feedback-based methods {{cite:163f8d9ad0a1bbbb855dc2d4146e14bfedba5bff}} rely on the EKF, w... | r | ea917c785ab9ca5c1f3eb7896122cd30 |
From a theoretical perspective, virtual constraints extend the application of zero dynamics to feedback design (see for instance {{cite:78283428abf6ac82d91722659d120973340cf45f}} and {{cite:5d4ade917bd9b798e2fd8511b77e95ffcd9b6465}}). In particular, the class of virtual holonomic constraints applied to mechanical syste... | i | 405ed5f27638d96a3e41af35a1a5f828 |
It is unknown whether or not there exists a loss function for which the original system of equations can be exactly recovered from one-dimensional measurements and random fitting parameter initialization.
There is always the possibility of adding more physics-informed constraints to the loss function, such as known pro... | d | 8ba1969c744beee7ae1056817dea6b78 |
Another key aspect relates to the question of the best shape representation.
While numerous representations have been proposed, Signed Distance Functions (SDF) {{cite:e1039b01aa2e0e3ff93082252ddc7668eb5aec57}}, meshes {{cite:980bcc03d59e4cfc34d7a1eb3b1cf3e9a30fb6d3}}, {{cite:0d96182e52cb153515b5b8a4c0328cca5e55ea17}}, ... | i | d3f99f5a979798ff0aa5b0795fc8b9d6 |
NeuralSparse {{cite:5f5ce87ab455e12df00a24fa0edf4db619fa2bbf}}. NeuralSparse learns to select task-dependent edges by getting signals from the downstream task. Given a hyper-parameter {{formula:9299ec79-ab54-440d-afd5-c71b79df3ca6}} , NeuralSparse samples k-neighbors subgraphs, which are given to the GNN as input. The ... | m | 551081b512bc0c0b695f10bc81df5a50 |
We use PatchGAN {{cite:13077c6337900aa1722088b671656879c5041bd2}}, {{cite:c132e0321fbb2d273b3a43c9f711395fcfdfa0e4}} for the discriminators {{formula:9c501980-aad7-4bc2-821c-d9d4ff6937a2}} and {{formula:9c4c3ef9-4a91-40ee-9605-b8863f3de0d8}} , which takes two inputs (e.g. source and target probability maps or entropy ... | m | aa5c73b17548f55746fc43443035f80e |
To work out the matrix elements in the coordinate space, we follow the same method
adopted in our previous works {{cite:8cd0195753365c24657735e52dc40b1c64de980d}}, {{cite:89730459fe82d0985f3cfdc6ef609fae94695079}}, {{cite:c26dc7bba6c6af35fbd028d61050e0c04e042587}}.
As we know, the relative-motion wave functions {{formu... | m | 3db2c1adf6bad983ab01ae57e7c56d68 |
Now, one goal of the Everett program has been
“to take the mathematical formalism of quantum mechanics as it stands
without adding anything to it{{cite:2ea38158844c319fa940fa820015d96e0386f125}}.”
If we find it necessary to modify the formalism,Van Esch{{cite:370a2c2634be4052b1bf165050ae7269e70435c8}}, Barrett{{cite:a8... | d | af5c73216a769cff9e2333d079b66a0d |
The best understood avatars of the AdS/CFT correspondence provide a map between geometries and CFTs preserving maximal supersymmetry, which for AdS{{formula:5595431b-4f6d-4144-9b24-a749ba9db818}} solutions in ten or eleven dimensions is 16 real supercharges {{cite:79215df2056abdd7762ff552457a128beaa5e3c5}}. These maxi... | i | cd8b6ab484b61b9197d53ec8308cf22b |
The endeavor to understand certain geometric aspects of decision problems has lead to intense research in statistical learning. These range from the study of data manifolds, through landscapes of loss functions to the delicate analysis of a classifier's decision boundary. In the present work we focus on the latter. So ... | i | 408ae956a2030b4a74c4306d42e9a642 |
Our empirical observations show that, when the optimal regressor deviates mildly from the dummy vector set by the IRM (i.e. a vector where each dimension is norm 1), IRM seems to find worse solutions than ERM. However, when the norm is close to the dummy vector, it outperforms ERM. Our observation might be related to o... | d | bfe69698d03ddb8f29a917f2037fe0b0 |
DSLR: To capture LDR images with DSLR, we mostly used auto exposure settings and captured a total of 25 LDR shots with such configuration. Notably, we choose stochastic lighting conditions like middy sun, low-light condition, high-contrast lighting condition, and sunset as shooting environments. Which allowed us to co... | m | 86add3b8881497829632f7a6d4db72ad |
Before the success of masked autoencoder, visual self-supervised pretraining had been dominated by joint-embedding methods, either contrastive ones ( {{cite:96f2d56290f524b3433d5b84ee520a323663ba02}}, {{cite:81d73ee81c91cda0953bb4902b435175eca39990}}) or negative-free ones {{cite:dbb633c3028a5b797b3c5d8c26bbad410954fa3... | m | 9646373d5b40b9d7af28988cd1f709a6 |
To demonstrate efficacy of AVAC, we further show the performance and energy gains for a real-life Edge application called HealthFog {{cite:c9ac3596f97109527eb0257ecd5b558786653718}} which provides high accuracy healthcare services using ensemble deep learning. Figure REF shows the application running in 3 major stages... | r | 41f41b69aab44f3e1116362bec52acf9 |
Multivariate stochastic processes have been the focus of intensive research in the last decade {{cite:4c801196d1c8fa01f067853d77853b1ce637eaf4}}, {{cite:49af611f3d7eca976b18ae5e42275fe979855449}}, {{cite:72c4f830582237da0750c3cbaf2051d2d13cc3ee}}, {{cite:bcadc5ee2cc3b7823acbbe37edf6a9610dc0d61e}}, {{cite:e0d836dc95e12a... | d | 39581e84924827d97aa339565f24d992 |
We include an alternative view of the results shown in Figure REF here in Figure REF .
In this Figure, we plot each editor's performance on a growing history of previous edits and its performance on the pretrained model's upstream data.
For both Figures, optimal performance is in the upper right-hand corner.
As expect... | r | 124e7b75176fedc07d5af96090cf3ef4 |
Our approach assumes that a linear spacing in latent space corresponds to slice spacing in image space. Although, alternatives such as spherical latent space interpolation {{cite:6ba8ba68699133463521421316e37b14ccdca0b5}}, {{cite:7751a3c74a3ce157bcd441e392a1dc5f6cfff6bc}} or an enforced Riemannian latent space {{cite:1... | d | aa961c1033c012d3737e023e26c68bc8 |
where {{formula:c414afda-8601-4145-80fa-e8d32743eb46}} is the NS spin frequency and {{formula:d5cbaa57-c558-4f3e-a96c-0bf5bb6eb21e}} is a coefficient in the range
of 0.87–0.95 ({{cite:321d3584c111cc38e9783ad91630d31571e14999}}). Similarly,
letting {{formula:4f0f9e21-0875-4ca4-ab21-f452a2a3071e}} , we will obtain the ... | m | 9698893daf6fe9772857fbe0cd8cb07a |
we would like to solve for {{formula:cbdcd579-3b2c-49e6-8d74-d6fd594e94b2}} , but this is a system of inequalities in {{formula:a1db8f2c-d651-4cb5-b392-c8ba2cf46c78}} unknown integer variables, with the additional constraint that the entries of {{formula:ca0b80e3-74dc-42c7-b54a-c58947914ddc}} have to add up to {{form... | m | e945302b1793ede41121ff3a52ac08eb |
This section presents the proposed architecture to estimate 3D human pose from 2D. Inspired by recently developed transformer approach, namely, Poseformer {{cite:8db8d417a889ce9f70acf537274e99710c42ab01}}, we propose interaction modules inside the spatial and temporal encoders to make the transformer more efficient whe... | m | 0036c7ccbb4dd71dc40a60b274522f3a |
Here we also argued that given a geometry is optimal for one simple objective function, it is therefore more likely to be an optimal geometry for another simple objective function, as compared to a null expectation based on random functions. This is a rather surprising result, given that we typically conceive of expone... | d | c4ead0de220f0b12d29d9d89d6df3db5 |
Image translation problems like denoising {{cite:6bb885192ceb94fc3463fc8efbb0731d0d005bb1}}, segmentation {{cite:71f90d79a46148edf87ff712d63f5d38b528f5db}} and super-resolution {{cite:dbe51536fbbc34a1c3efd12d6baa8d7a64d9ceaf}} are spatially proportional/similar translation and much easier to solve like pseudo-condition... | r | 2fd0c1ce0f04686cda34d158830c10a0 |
Let us start from the strong disorder case {{formula:c0af0f97-e527-493a-8351-9344d9ac4773}} illustrated in Fig. REF . When the typical tunneling transparency {{formula:3bbc89d7-6cb3-4e2a-90de-af80546aaa71}}
of the insulating barrier separating neighboring puddles is small (the action {{formula:7bbf1926-a806-4b24-b515... | r | d5141958c5dc60094494aab707d67849 |
For a long time, it has been known that the distribution which satisfies the problem-defining constraints while maximizing the entropy functional is in a sense the least-biased distribution {{cite:0130e5cd761b5a5d425f8f70c60c12a1628bd0e4}}, {{cite:0592a02007cdd1e54bfadae65eed5d8c00ab9238}} that incorporates the provide... | i | 14a6e7b416a4ca9d2d7a167a47ddb629 |
In Fig REF , we show the facial editing results of our method with a different number of condition attributes.
It can be observed that our method is able to handle complex disentanglement between face attributes by adding more condition attributes into the conditional manipulation operation using the strategy presented... | r | 28fcdf9a43c93c49b8810c70234dfc66 |
As noted above, for the GCM to be equivariant to large transformations
of the input, the parts need to to be detected equivariantly. Some
capsules papers have used the affNIST
datasethttps://www.cs.toronto.edu/~tijmen/affNIST/,
but this only used small rotations of up to {{formula:7324fa31-1c7f-4bb1-b016-a9a7d9dc9b93}}... | d | 5949485af6f04aa88aa5320a28938261 |
This has historically not been the case in finance, however, where PCA based methods have dominated since Markowitz' modern portfolio theory {{cite:1002143495f7f4b86f9aea2f8a2fb56677df5761}} {{cite:792e14832424305bc5caf3cdf7df3bf2a88ec33b}}, and only a handful of studies have even seriously looked at ICA {{cite:e4bbac1... | d | ce642dc759bb51fc1ee43fe107dc6d1c |
We studied bi-modal variational autoencoders (VAEs) based on a product-of-experts (PoE)architecture, in particular VAEVAE as proposed by {{cite:ab99367126426abcaf4cbe8d00eac7ad1ea96502}} and a new model SVAE, which we derived in an axiomatic way, and represents a generalization of the VAEVAE architecture. The models le... | d | 192a335ff7ffd3a37139c09acd84236b |
Our approach in synthetic dataset design would be to understand how DL models perform classification,
based on the existing real-world datasets (i.e. problem under study for classification or counting).
To achieve this, we take advantage of the work in {{cite:088a198a627f846686b4dedc97ed191da477a2fd}}, which allows to ... | m | 390f653a04215ddfdb6692ab0beb3a35 |
Speech, as one of the most natural and common media of human communication, carries a rich array of information that extends far beyond the verbal message being expressed.
From an utterance, the speaker's gender, age, dialectal background, emotional status and personality could be identified by human listeners.
Part of... | i | 1315df1545e43842174088dc7d9ae7b0 |
We compare
our models with several state-of-the-art neural NLU models on two
publicly available benchmarking datasets: the ATIS {{cite:5af231dd92728dced9584d75f3c9edcd599336c4}} and SNIPS {{cite:4fcbaa24e81cfabdbdf408f411658ac9cabd01c8}} datasets.
The results show that our models outperform previous
works.
To examine t... | i | 131412dae8e023027ef20f6d40f39b02 |
Fig. REF shows the style transfer when the style and content images contain a word cloud. The challenge here is to supervise style and content features while maintaining the readability of the text. DPS {{cite:21702dd039853dc943ae2c5a06892935b4ec8ec4}} spills-over unrelated features on the word-cloud. To investigate t... | r | e699a17a5f305531ba48b2cfe371f14a |
While designing a hybrid functional, the choice of mixing fraction({{formula:70f4424d-acaf-4a77-82be-5f337ea835b8}} ) is crucial to the bonding character, ordering and alignment of bands, and thus in the electronic and dielectric properties of the material. At an operational level, the parameters that define the screen... | m | 571805fea5ec993b5e19940b4cce2007 |
Quantitatively, to have detailed grasp and contribution of each input parameter in understanding mixing process, we perform feature importance.
Three different types of feature selection methods are studied for consistency.
These include F-test, MI criteria, and RF.
Entire simulation data is used as there is no QoI pre... | r | 1cc02c6cd0d57fa1bdc3c045eb8f1cdc |
Effectiveness of Graph Transformer Networks on heterogeneous graph datasets. blackTable REF . and REF . show the classification results on six heterogeneous graph datasets. In large-scale graph datasets (e.g., CS, ML, NN, DBLP, BLOGCATLOG, and FLICKR), we trained GNN-based methods and GTN in the mini-batch setting with... | r | 2208052a65b9dbc88ebb2a3ddff45e16 |
Hard labels were utilized in the baseline to train the target network directly. We also compared the proposed method with label smoothing regularization (LSR) {{cite:478f2d94292850f3a29e88c05357842f20aef90e}} and self-knowledge distillation regularization approaches, including teacher-free knowledge distillation (Tf-KD... | m | c6fdd2374a35b57fd66bf17c5be5c5bb |
In this section, we evaluate the performance of the proposed algorithms.
Both the DL and UL channel power gains are modeled as {{formula:aaf6c527-cb74-4deb-ab4d-5f22e6b65524}} {{cite:93b60d68b4c43fb901116b794c3a3dd210b78b9c}}, {{formula:96cde8a0-a794-4163-84cf-7f7d8899392d}} , where {{formula:0e6bdf33-bb99-42c9-8eed-d... | r | ec31c99eb72a8737b31cbc07bb190326 |
Besides point-cloud-based methods, the voxel-based approaches compress the event data into voxels, bridging event cameras with conventional CNN techniques {{cite:a8ad6d663bc112127cb216fbccbb082318837965}}, {{cite:36bf61db9ca930502bc646fd2430cf68e9e0dc3c}}, {{cite:76056652a6f36bd14ce035fbe1d5f80094d1dea3}}, {{cite:ad754... | i | d1544032f51bf204a0e1fbd73603b416 |
In this paper, we focused on the simplest one-loop amplitude with the lowest Kaluza-Klein level {{formula:9de2a642-1754-46be-a3d7-2a0066b4e857}} . To further explore the loop-level dynamics, we should also study more general amplitudes with higher Kaluza-Klein levels. Computing these amplitudes requires a thorough ana... | d | 2f0b794613b5e59d16bac57bfa057de3 |
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