text stringlengths 54 548k | label stringclasses 4
values | id_ stringlengths 32 32 |
|---|---|---|
Leighton {{cite:ce040779c557a0d8d446ce87342b5798d77a0630}} used the well-known Lipton-Tarjan planar separator theorem {{cite:fadec49a29025e0e142ee2c099ceee4ec62f68f5}} to give a connection between the crossing number and the bisection width {{formula:d1ed62c1-7467-41fc-bfa9-4460ee539992}} of a graph. Explicitly, it wa... | d | 3536564f29a7c7653de3134d8daaf722 |
Finally, the best results are obtained when using CPE. In contrast to the other PE approaches under analysis, CPE is adaptive depending on the input signal itself, which might be an advantage, although it makes the generated positional embeddings harder to interpret. Despite not having explicit information about the ab... | r | 8f26d497885cfa8a0db10325141ee41f |
Template matching is one of the earliest methods for question answering using knowledge graphs. In this class of techniques, a dataset of question templates and matching query templates are used. A new question is processed using linguistic rules and converted to the question template. The question template is mapped t... | m | d83889cfd7afde7db178bdd3d3e5d9a8 |
Typically, in literature, we only observe final results for the fully quantized setting, making it difficult to assess the impact of the individual quantization choices for each tensor. In this section, we aim at better understanding the impact of the individual range estimation methods for either gradients or activati... | m | 5039a6574db26a16b2b9ca9882310a32 |
As an example of our former calculations, we revisited the fitting of the Geminga profile using the analytical 3-D electron distributions and numerical projection to the 2-D surface and convolved with a Gaussian function with a kernel of {{formula:2231e20f-c707-489b-813a-63ec0a97fb26}} . The results are shown with a re... | d | d0e07abf83b1fdfeea24ca42a57d60ee |
Sun et al. {{cite:f0d74b01b2d3e465caa76cf2427ae122dc158192}} apply part level features because they provide fine-grained information for person description. The authors propose a Part-based Convolutional Baseline (PCB) network for part-based feature extraction. Part-level features are learnable by conducting uniform pa... | m | 9cf6c76b2417efb111dfd0870e75e26a |
A smooth latent vector representation for outfit style is learnt using Variational inference in a novel style encoder named Variational Style Encoder Network (VSEN). There are two main trends in denoting an outfit, as an ordered sequence of items {{cite:c268e674f9d099f4688be801f1ed9a363307d08e}}, {{cite:2a66e3adffb41db... | m | 8637fd57b6aa4185f4abc3752eafc587 |
The other three components of our architecture are optional and are introduced in an attempt to help the emotion and enrolment encoders learn their intended functions: the emotion encoder should learn a speaker-agnostic embedding {{formula:af46c0fc-3b29-4e46-af89-894ff64b5a35}} that the enrolment encoder will help per... | m | a7a0e153d5acc3e79cab4475bd681ef4 |
When studying the association processes of multiple chains, we switch to periodic boundary
conditions and use the smooth Particle Mesh Ewald procedure {{cite:6d18fb88861b23d52ddd2a86336b81d5db068470}}
to treat the long range electrostatics.
The procedure is a variant of the Particle Mesh Ewald method {{cite:79e6b37d042... | m | b0dea50a13761d9c3c5419434c206abe |
Recall efficiency decreases with an increasing storage time, in part due to addressed atoms moving out of the interaction region via atomic motion in the warm vapour and because of atom-atom collisions. The reduction in recall efficiency is clearly visible as reduced area of the recalled signal in Figure REF and is pl... | r | 0ffa1653f895b5b6b5993bf35700fd01 |
where {{formula:4e80fcb5-b953-4b71-9f7f-3e52db367357}} is the diagonal covariance matrix of
{{formula:ad57ad99-b720-41d6-943b-aece5db1e668}} . This result also follows from standard VAR-process
results {{cite:18dd55b069eefaeab744564a6c93bcb12c0c570e}}. We
will say that Equation () is the integrated
covariance equation... | m | 574831e2a01977a744ba213df537c817 |
Recent studies of FL provide promising results in healthcare applications.
For example, data from 20 institutes across the globe to train a model (EXAM: electronic medical record (EMR) chest X-ray AI model), for predicting COVID-19 {{cite:12da8c24c3ff88befed1ecafdd855a3537a1ffca}}. This model is based on HFL that consi... | m | b5425f8a9c8b35b0d7761c52743b0d46 |
Theorem 1.1 {{cite:a8f7786747c6c1a04a418d2c048151a1fc4d0f3c}}
If {{formula:bdf8c6c2-e48f-46c9-a770-a51f8842617c}} and {{formula:a39e319e-b8cf-4353-960b-d129b5fa2ee5}} for {{formula:7e12a445-7c56-4d22-80d5-0bf5da62aa4a}} , then
{{formula:f9ea9822-7e4e-4570-97f3-7be3e01fb1a1}}
| i | 83f51f3832f7e5e9731cec31f094f1e6 |
where {{formula:05a2905c-d785-499f-8329-37dd9049e625}} is the pure electromagnetic contribution, {{formula:9d98b1d9-c6b9-48f5-867e-735b532777ae}} is the hadronic contribution, and {{formula:d5261526-50a4-43d2-a34b-b5341a329cca}} accounts for the electroweak corrections due to the exchange of the weak interacting bos... | i | 2f42a6c0f54d0078d79592d4946f7811 |
Though some recent works {{cite:bba3333eaaeda98aa53411b5caf8c345ba77f57e}}, {{cite:5640d99c8c2f10b5be9bd5edc25b0276f554a81d}}, {{cite:a3a1004f5903edd9bd8d8319281ac982e9fd26bd}}, {{cite:4c1395458abc3d5e186139cb01fc965ef3d1dd17}} on deep learning-based interactive segmentation have shown good performance, it is a great c... | d | 9aef8636e8c3d3b990385ec51e8ec281 |
Thus, we obtain the so-called Yule-Walker equations and therefore we can estimate the AR parameters by solving this equation using Levinson recursion {{cite:5682713fa4b9786fcd0f48ec676d0bed1f8e934b}}.
| m | 469a0b0abbf78e49b502c7a41bad8654 |
UniLM {{cite:a2f917bc230978ae07561a0d3a902421e0fbc5ba}} It is also a pre-trained language model. It unifies three tasks: Seq2Seq generation, conditional generation, and NLU.
| m | 4b05b402bfc94fb043a8121efb6800e8 |
Keyword Spotting
As shown in Table ({{formula:34964cc4-ebee-4000-8dbb-4429cfe50060}} ), the performance of phase1-{{formula:a65ccfe3-de84-48e9-85ee-c07458450145}} decreases compared to vanilla. This is because, in this phase, we pruned unimportant 90% weights of full net and quantize important 10% weights from 32-bit... | r | 2e0c0494883969e810736be88056c7fa |
We noticed in this work that the best mercury density and sound speed that fit the simulation and experiment are a bit off of the nominal mercury physical properties {{cite:7b9902f1e8ae599cb0db77e3c6acc07cb9a63caa}}. The reasons for having this offset are coming from three major sources. First, the limitations of the S... | d | db72401d0730b59c8de97cb7c2cc52a8 |
In order to active modulate the Casimir effect, one straight way is to
change the dielectric properties of materials under external means {{cite:f7b17945a09bc3b82a8df913a222634d2e822784}}, {{cite:24c07dc0025e967c353cabd36d3020ebe5c9b908}}, {{cite:449f71d1178e97403e7c42ce731c6dc6f0328a28}}. Vanadium dioxide (VO{{formula... | i | 48538c7003e935895f83713ebe1e05cb |
Training was completed in the Gazebo simulation environment using the SAC algorithm provided by the StableBaselines3 RL package {{cite:ac70c0dd7970368174ebedda565b73dd0fa70947}}. In each training scenario, policy convergence typically occurred within 3000 episodes/rollouts (Figure REF ). Compared to the previous work b... | r | caf82705b541f2a4a562408de9268873 |
When the output normalization is {{formula:25dd1f15-84e6-4615-ad78-aebf84d2a6ba}} -norm instead of BN. Experiments in [fig:identity-init-performance]Figure fig:identity-init-performance seem to suggest that there is still a gap between using {{formula:5bbaa08d-ee48-439e-9b84-2bb8965b6958}} -norm and BN as output norma... | d | 72f3507e4519e8f5c23439d45d9cf530 |
Apart from the alignment of existing implementations and the introduction of new abstract theories,
our future work includes a comparison and investigation of other approaches, like the aforementioned {{cite:56b8e3467d5e92516c24dbf316c40638d5d0bc4c}},
and other approaches to AMT {{cite:b4ea327566c0dd3fb53fccd4d3f329ac9... | d | b3573d399259b8078892465eaf8eaf85 |
One of major questions that we approach in our framework is how experimental perturbations should be represented in the model. While the focus is often on model parameters as representations of physical dials that are experimentally accessible, direct correspondence is not obvious for a statistical model inferred from ... | d | 00a03b3cde4c573a7edfcc34fb629599 |
We train all networks (both the original and the optimized) using 4 adversarial training methods: Standard Adversarial Training (SAT) {{cite:8596897b317da628a12b605437eec9eeb592f09e}}, TRADES {{cite:8d3c669317b4b7166d65bd0136de6352adbee856}}, Misclassification Aware adveRsarial Training (MART) {{cite:d79fb1620ea26f2c89... | m | fe998243fab2211689e8fb57786c8e0f |
libvdwxc implements these partial derivatives, while the calling DFT code
is responsible for calculating the density-derivative {{formula:79bbf1d6-409a-4b1e-89d8-16f3d33688a2}}
and combining the calculated partial derivatives (REF )
and () to obtain the potential.
Any DFT code that supports GGAs already implements th... | m | 0f659fb282016692aed0a4a33cec174a |
Our proposed method differs from the original RuleFit in two ways. First, rules were created based on the transformed outcome rather than the outcome. Second, the original RuleFit fits the rule terms and linear terms into a sparse linear model using a least absolute shrinkage and selection operator (lasso) {{cite:1e50a... | m | 90b575f2b4cb4646badd68a65ae913d5 |
kutuzov2020uio report that different test sets from the shared task manifested strong preference for either the PRT or the APD method, and that this is correlated with the distribution of gold scores in the test set (but not with its language). If the right method was chosen, then using contextualized embeddings to ran... | m | 58c1ce4778bc7cee874aab42b150145d |
The dual-gated architecture of the device allows independent tuning of both the charge-carrier number density, {{formula:d98bccd3-f959-4426-9967-52d471955854}} , and the displacement field perpendicular to the device, {{formula:f80cea16-d1e8-4158-aacf-7d4df52022ea}} , using the relation {{formula:d1dfb8ab-1ad7-439d-8cc... | r | 26d4e732c093d4b330dc3934de062e42 |
Supervised Quantum Machine Learning is kernel methods{{cite:386a8b9e4336307634e7dd4187fe9cfa13e4ac17}} - algorithmic solutions for pattern analysis, which can address non-linear relationships such as cancer evolution. Specifically, kernel methods (such as Support Vector Machine) solve data-driven problems by transformi... | m | f2f1053a40f940cec1b6489c179e1bfe |
There are many studies showing that the jet helical structure can be produced either as a result of the black hole precession or the action of magnetohydrodynamic instabilities {{cite:f48b96a504265c0fbffe984161bf44f41d3ff551}}. The long-term brightness variations and apparent motion of superluminal jet components are e... | d | b593bc66b3df7a9066214ce18ba8a250 |
In the simulations, we use random initial value data with {{formula:25cdbec3-90f5-4e0f-9449-7854c1e444e1}} mesh so that the size of the input data is {{formula:83af5937-ed75-4c08-b20c-92d2665ff71a}} containing a pad as a boundary condition. Also, {{formula:87952c29-c8cb-4fc9-9084-84597ee12bab}} (Heat, Fisher's, AC) ... | r | 3c484673564ed047388993dad8f7e2ac |
Another important set of demonstrations includes the macroscopic cases shown in Fig. 2(e-h) and Fig. 5. We chose the example of Nd:YAG for concreteness, familiarity, and relative ease of theoretical description. We wholly expect that there are better lasing platforms to realize the physics proposed in this work. Howeve... | d | 48c32a0f5892db7d2e2372b12eb01f78 |
DP theory is based on the rigid band approximation. Surprisingly, this
approximation works well for most cases when the electronic bands around the fermi
level are not highly degenerate{{cite:b9bf1cedf0bc7987a69e01c362620b9def40e7a2}}, {{cite:02af785c818beec93c9e15430b56083730f21f8a}}. Besides,
VASP, Quantum ESPRESSO, ... | d | fc06bfb4869baef4ab606dd24ca60772 |
The {{formula:92c89411-91c4-4c7c-9b16-667dd239e9fd}} obtains the classification results including {{formula:196ac4bf-ff4a-4a2a-a1f8-c451e71492a4}}, {{formula:24058491-ad66-4fb9-8ffb-9605266fa324}}, {{formula:b2461a24-2380-4a7e-a87c-48433931dea4}}, and {{formula:1afd8f9c-e053-455f-be8b-638c48d3fa27}} over Clean, PPMD, P... | r | 5db5e57d6a28ccc75113a637c792fc64 |
In contrast to the literature on semantic textual similarity, where deep neural architectures predominate - e.g. {{cite:09b355e3c6feaa990154e44a9b1348a3b4955771}} - text de-duplication overwhelmingly uses {{formula:44524314-b9eb-4bae-9e3b-92c1172ddd67}} -gram methods.
There have been few efforts to formally evaluate th... | i | c7ba6cfc16436d2eb06207a585a7d940 |
As an information distance between two random variables {{formula:c864c402-d88c-4638-bd8c-3185421131f6}} and {{formula:8bf00060-06f3-4c4d-9c55-0ab42af11352}} , {{cite:5d6f0894f11f19b019e110f8c64852791859c40f}} proposed a directed divergence defined as
{{formula:a50f7cf8-0bc2-4ccd-8407-dfd300a01cf9}}
| i | ca6251a56d07efe93907283ac7c81985 |
We used a within-subjects study design where participants were asked to imagine they were at a train station. Tasks were then completed on both a touchscreen and LMC using on-screen point-and-push. To prevent step memorisation, the tasks in the two conditions differed slightly.
The order of the touch and non-touch cond... | m | dab44c73c5f5e04f0ddecfcf73a29065 |
Restricting to abelian C*-algebras and to orthogonal projections,
we show that our parameters coincide with the
Choquet capacities of Haydon and Shulman (see {{cite:8a8baa0fe4d6cd21675ab854ef709b6688cc251b}}).
The positive operator {{formula:0aa1b6e4-8330-430b-8c89-0d6885babb22}} can in this case be thought of as a me... | i | a0878f09c074f44ce33303a9028539e5 |
The Gauss-Seidel method is known to be convergent when {{formula:df3418b2-174a-44fa-aff8-9aedffff947f}}
is symmetric positive-definite, e.g. {{cite:718108ad64e986fa9aa77771e62f4e6945582080}}, or strictly or irreducibly diagonally dominant, e.g. {{cite:1f081dd5f14ac009c4b8a71d0ca819a50e4a3a2b}}. The case when {{formula... | m | dd4a2590a51d3644cb7f5018dcfa55a4 |
Since the policy {{formula:c8586675-02df-4583-babe-8d1e13d9f63c}} is a discrete binary variable, it is intractable to optimize the policy network with backpropagation due to the non-differentiable problem. To resolve this issue, we propose using the Gumbeling Softmax sampling method {{cite:ed59f9d304c768ad251fbb7a7690... | m | ebf79bd58716a2716d994e9407595798 |
Some of recent works try to solve the representation limitation directly through joint Q value function with complete expressiveness capacity. QTRAN learns a joint Q value function with complete expressiveness capacity and introduces two soft regularizations to approximate the IGM principle. QPLEX {{cite:878928e2755b44... | m | d489ee8f814246b61d3f21075d3077d9 |
For validating the proposed approach, we have evaluated architectures with vanilla algorithms as Maximum Likelihood Estimation (MLE) {{cite:a2d923005720493457f807b21d51fd288a103b50}}, Support Vector Machines (SVMs) {{cite:b399e9551fdad3732964bcf8e249fb08139c4f24}} {{cite:b09601f8871d4c54b3314d61f4c837fe02a8a1c3}}, Conv... | i | 781e6e3b28677d34eab918f2fd78d10c |
Inflationary cosmology {{cite:65c6407e75c498be9fa9332593153aa7cf37fdcd}}, {{cite:293b170bee3e4c5f1937bbde0c29610610d9e9df}}, {{cite:ce32403479d8d9ccc1857d54b23a5f13462dc5c4}}
addresses many conceptual challenges of modern physics. It has become an integral part of the cosmological standard model, which assumes a period... | i | 7c4a8da000a164f83d6ba912c3bb41b5 |
In recent years, advances in machine learning (ML) have achieved astonishing successes in many applications that transform our society, e.g., in computer vision, natural language processing, and robotics.
Traditionally, ML training tasks often reside in cloud-based large data-centers that process training data in a cen... | i | e58d6913fed92742c2cd42226aac3d77 |
In addition to the specific problem of variability reflected in multiple data sets, observations of epidemic dynamics are often incomplete in various ways: only certain health states are observed (e.g. infected individuals), data are temporally discretized or aggregated, and subject to observation errors (e.g. under-re... | i | 6c26c37f9ffd19413bc7b56377bbab1b |
We consider estimations via the three-stage kernel anchor regression with disjoint data set projection (KAR) and two-stage kernel anchor regression with joint data set projection
(KAR.2). The baseline approaches include the kernel-based nonlinear methods: kernel instrument variable regression (KIV), kernel partialling ... | m | f843e665f215490ca53277288b642e48 |
with the functions {{formula:51ce3801-cdb1-4c8d-b1d5-60bd99ba54bd}} belonging to a suitable neural network space. The use of neural networks in this setting has the advantage that one can easily implement meshfree methods by randomly sampling collocation points (see {{cite:8e6bff9226dd272b4eca38d0d0e9c4ec7e883dfe}}, f... | m | 0f6f11e01e38704083f38801df1e2557 |
Action recognition in videos is a fundamental task in computer vision. Recently, with the rapid development of deep learning, and in particular, deep convolutional neural networks (CNNs), a number of models {{cite:9944d507af1df170db8e9e9221bb5d5805f2fac1}} {{cite:49a2044dd9b750e2bfa02f99d4229513aa5b49a2}} {{cite:575563... | i | f175e27b96b7dcf2f0e9e83f0dbf007f |
To evaluate the above algorithm, we numerically solve the time evolution that implements the protocol for the density matrix, using QuTiP {{cite:d32ed50faa0069ae7f5ee4d17b276fb896c33d7b}}, {{cite:34904651178c0cd33b9dac5f7edefdeb5737270c}}, satisfying
{{formula:f8407953-f677-4212-a4d9-997d31b2f030}}
| r | 3d144fa2f7066798fc20e2a73f70e290 |
We have also shown that the general procedure of
{{cite:b6d9dc8d552a3126e35229ae5237ea5c9b284f48}}, which solved for the non-local boundary
conditions related to QNMs in terms of monodromy data of the Heun
differential equations involved, can be used effectively to find QNMs
in the zero temperature limit. This limit le... | d | 17a0f993f311268e416bc7f8fb12806c |
SMILES-based methods {{cite:ee54f2ceaa38df32716799837dd22615cde9eb52}} are infeasible for scaffold-based tasksA drug design scheme where the drug candidates are designed from the given scaffold. since molecular structures can substantially change through the sequential extension of the SMILES strings. Also, as explaine... | m | 4f23f61280c1e5295558173d4e07366a |
The current trend on video captioning is to perform Dense Event Captioning (DEC, also called Dense-Captioning Event in videos in {{cite:056586ab2ee5dd5f4351794704ced70dd1d55095}}). As one video usually contains more than one event of interest, the goal of DEC is to locate all events in the video and perform captioning ... | i | 14ca96334ed4c5149016f0f22defae20 |
We also compare against SAC {{cite:20874e02018d858cbbdd9e23450cb978b0b2c136}} and QT-OPT {{cite:31f16c4736c1918bbc553d294d586b7b17c7797c}}, two commonly used off-policy RL algorithms. Despite significant effort, we have so far not succeeded in training a successful policy using either method on this goal-reaching task ... | r | c7585a2aba5065f0126e3503ab89627a |
Remark 1.6 In the literature (see e.g. Hatcher {{cite:a58719ccd2018b4bd51626024b490564a4fbcbff}}), an (open) {{formula:4929ea3f-f014-40a6-a5d0-10b48fae7470}} -cell of a CW complex is a topological space that is homeomorphic to the open unit ball in {{formula:8aa6fc47-1b47-4769-8f6e-d6ad6cee78f5}} for {{formula:a9aef13... | r | c214e6a848957bb3f98c95e5a8b53494 |
In the literature, UDA methods have predominantly been designed to adapt from a single source domain to a single target domain (STDA). Such methods include optimizing statistical moments {{cite:0a96bfbc7201fad41f3d33c747819f9539b1eecd}}, {{cite:c5fcb8f198f451870e3be504b379c3ef73aa3565}}, {{cite:ce85860931af0d05c71c6693... | i | 1c149187076380ca62d2021e2ca2ad69 |
We can apply a simple example of bias correction following the
procedure outlined in {{cite:ba93064a4df981e3a7bb0382681f3156237ee239}}. Given the simulated and fitted values
of the Sérsic index for the Ser model, we plot the bias as a function of
the fitted value output by PyMorph. In this case, the output value repres... | d | a00dce4e859a8a7c1ef3d9d86e4dbb7f |
Is such spin-induced descalarization detectable with current or future GW observatories? For such descalarization to be detectable, one must first detect that the binary components were scalarized during the inspiral. Our simulations showed that the scalar charges lead to scalar quadrupole radiation because of the high... | d | 1a799689e6b5f5fd3d6e17360f411d32 |
For models trained with DG methods, we observe a large gap between the certified loss and the empirical loss on benchmark and synthetic domains (see Fig. REF ).
Therefore, to improve the certified loss of the models trained with DG methods, we also propose a DRO-based iterative training algorithm, DR-DG, that augments ... | i | 46ac35cfa979aa3bb1700fd05ffba603 |
Ab-initio nonperturbative approaches are called for to solve QCD, as Lattice Quantum
Chromodynamics (LQCD) {{cite:08481f2b924630b5b4ba921ee2cc131aa70d0f40}}, {{cite:97f1b270e5cb0c8ce34b48a42dbb7ef492942bd8}}, {{cite:b993e9c50340db0e6ed02829e826263e6c8fe39f}}. This well known method
starts with the QCD action, {{formula... | i | fb84f87977ab0620a728863c070b9b10 |
We have analyzed the optical imaging and spectral data on CTD 086.
Surface brightness profiles of HST data do show a minor intensity enhancement in its central 1-2region, but it is not well resolved to comment upon.
Based on the isophotal shape analysis, we may conclude that CTD 086 is an elliptical galaxy free from du... | d | 1051ba439a3b2396a203c4b82ef7f44e |
Very recently, {{cite:37d0857fa1d7f7bd92f07cc4e4004f761c280448}} reported an marginal candidate, GW190920_113516, whose secondary could be a heavy NS. With an effective inspiral spin of {{formula:a3a4263e-a720-4475-b437-46b32630aded}} , this event could be a potential NSBH merger with the NS first born. {{cite:f87368aa... | d | 1456056047c9d734d42e517a20e8ec29 |
If we replace the auto-correlation function {{formula:87f9418e-4579-4bd5-acb2-1164e09bbe2b}} in Eq. REF by a vector containing the density, the
momentum and the energy, the GLE transforms into a matrix equation {{cite:dd2bc2a8c46990ce29e383896aa6cd5f66e604b7}}, {{cite:1ce510bab6873b2270ff7947ebdc0916295f7ba4}}. Apply... | m | f8c46445c7f2b4330ae84ec154e77a8a |
The mathematical structure of chemical kinetics and chemical thermodynamics, since their establishment, has been continuously developed within various fields such as physics, applied mathematics, applied chemistry, and systems biology over the past century {{cite:bf750ea8e3538a222baf5480578fb4c64a4f5d88}}, {{cite:6f58d... | d | c0c39a649de9b6e1486f6fa416ece5e6 |
Eq. REF indicates that GaitSSB can bring much of the ability to pull negative pairs away (increasing the inter-class distance) into the evaluation stage regardless of the challenging cross-view factors.
However,
we notice that some contrastive frameworks{{cite:19e9b0b30169d17ba9c0d255d338b7dcdb125b07}}, {{cite:5302d88... | d | ca42a54ea568c7ae8e1668063523a1aa |
We have computed the Hubbard {{formula:b643a483-8eb9-4e1d-8dd6-087b48a60f00}} parameters both for Ag and F using the linear response approach of CDG as implemented in VASP software package.{{cite:b425b9aa3dc230d210dbbe8fe5e2e8edc6755331}}See also “Calculate U for LSDA+U”- Vaspwiki available at https://www.vasp.at/wiki... | m | 9790e13ced1978610c85c6a287f82637 |
However, if the problem is {{formula:06563dda-43f7-4f30-8ea6-871fea6e5db9}} -critical or supercritical, i.e., {{formula:3aeaf0d5-d837-4f15-b936-08821a5479e6}} , where {{formula:fac960ce-7a96-48ec-a3fd-64b3a210aaf7}} if {{formula:dea59220-d3be-44c3-a1fc-c88bc256de87}} and {{formula:73dc3c6d-199f-48c0-b470-daa62c23bb0d... | i | 0455cd1879e4e19c5f19bdb8a3707c12 |
One can compare Eq. (REF ) with the following Eq. {{cite:31f45b4fd6445e2cd3f15ecaa4fbe36ef1af3318}}, {{cite:6e9aa02e5ea5324f74d9086452c0fc0aa11915a9}}, {{cite:5e38d1da17acad27263de2469fc87d68a2601fac}}, {{cite:ce43db60ca087d8ff0a778e09596234de8846ce6}}, {{cite:288365ba46805b0c8b112073a10206288b3fec65}},
{{formula:3e64d... | m | 13805a5ec9c11af79946c0ff188e01db |
The spectrum efficiency for the proposed DAP architecture is shown in Fig. REF , with the same simulation settings in Fig. REF . The SNR is set to -10 dB. The baselines include the fully-connected hybrid precoding with 8, 16 RF chains using an alternating minimization algorithm{{cite:052c152fafe60888450b90de5a0734c8000... | r | 4d03176323d4d9de5853e97ecc7ad60c |
The subclass of {{formula:2c9f0d07-d4ee-4193-a6ed-b98501c0ee38}} spaces having an upper bound on the dimension by {{formula:07f4ff05-5fac-4297-8460-348e971f8813}} in a synthetic sense is denoted by {{formula:6698b90c-4599-4469-a8da-d0035e7af417}} , see {{cite:0c5f533466464968159b2dc0da738c2b1bf7207a}}, {{cite:da2c9fb... | i | d239022dfca8c82eff0ecc0d54f1afbc |
Using {{formula:402f772d-8aea-4508-85f4-544a4da1e6cb}} GeV{{formula:c1c8f8f1-418f-4bd2-bf51-7eab71bd6e53}} {{cite:9fd2b3cc404dac593b3327552aa8f16a36af6bbf}} and {{formula:5d26d103-f731-45fe-972a-f805465e033e}} {{cite:e53ba5038782ff0d852089b64ec35f8ca48df6dd}}, the hadronic decay of the W boson with QCD corrections t... | r | 205edf583637f1897310b492390628ea |
Panoramic image stitching is a fundamental problem in computer vision.
When solving this problem, we are given a sequence of images taken from a single point in space with a camera rotating around some 3D axis.
The objective is to map the images into a common reference frame and to create a larger image composed of the... | i | 28f5efe3e855e2ad9a83fc0d37927ffb |
To further verify the impact of teacher forcing, the integrated models (row (e)) with high inter and inner-layer teacher forcing probability (rows (f)-(h)) are also evaluated.
Note that when the teacher forcing is activated probabilistically, the strategies are also known as schedule sampling {{cite:58f6985fd785dfa2faa... | r | b390a5762448d4cfd6faba31d19a51c5 |
Lemma 2 (Hoeffding-type inequality {{cite:5ab00fbcd19729d480098fe7911cab2c60f13e48}})
Let {{formula:73a94f1b-5b57-4e3e-8cbc-7e6bd68183fd}} be independent zero-mean sub-Gaussian random variables, and let {{formula:a7bddbeb-7b59-4e93-bc69-458c7edf2b3d}} . Then, for any {{formula:a794f7cc-9590-411c-b507-54ea28423826}} ... | r | 8d93e60150d00d6d632f5cbe31522aa0 |
In order to obtain a numerical estimate for the HFS, determining the {{formula:a51cae95-73eb-4df0-bdb6-ea15472af6df}} couplings is almost as important as fixing the sign of {{formula:347639c4-76cb-4887-ad59-49ccc0c25555}} . In the following, we use short-distance constraints, that allow to relate the nucleon Compton s... | r | 2481a74855e771f891128aeeafb6af0e |
Traditional feature selection methods including both unsupervised and (semi-)supervised methods all exploit the knowledge of seen concepts rather than unseen concepts {{cite:20707bb64855d79286c8218d86254e170e7a6cb6}}.
Specifically, unsupervised methods generally prefer the features best preserving the intrinsic structu... | m | fa6490c94fcc0d0f9effdd3105b4d760 |
Prior methods {{cite:1008431812e51846b7b637ca681789323783064f}}, {{cite:d6c41487dace8efa8f67cf3749df5aead231ecf3}}, {{cite:c2d5006cdc2e6e7f00c6e66a4d06e0cc88889598}} are tailored for dense prediction and use pre-defined manual rules to match corresponding pixels. They emphasized on local feature learning and largely ig... | m | 21701630637dc4c622e6f0e975200922 |
During the encounter between the primary cosmic rays and the earth atmosphere, a non-negligible number of muons are generated over a wide energy spectrum. The fundamental basis, on which the muon scattering tomography is founded, is to follow the propagation of the cosmic ray muons within the volume-of-interest (VOI) w... | i | 5d773474dda78de1d37866d22861e00f |
Experimental results obtained from the Poisson, the gamma, the Weibull, and the CMP models are computed using the stats {{cite:6b2dff190c28ab824ecb445a62c49c107dbcd41a}} , Countr (gamma and Weibull) {{cite:d85a7d30209800be5ebe59186e11c660a17a694b}}, and COMPoissonReg {{cite:c6d1a058623c5cf10b3fc0cd72e82bf456f9f127}} R ... | r | 775a9644d621e69e34bf7b8b8c5b340e |
PROBLEM: Under what conditions a datum can survive in a sensor network?
Given that the nodes as well as the links can fail with some probability the obvious model can be a Markov chain, but such a model can grow in complexity very quickly because the number of possible states becomes {{formula:58cd3bea-0962-4aff-b8c7-1... | i | c5b0f3d9554e995b65a307a78e056e50 |
Using the subspace encoder method (REF ) we estimate a model where the three functions {{formula:88946d8c-9bc6-4373-b317-d104a199fea9}} , {{formula:4d402ebf-d66f-4ca1-bcec-595ea59b35f4}} and {{formula:f8ff31be-69e9-42be-bc0f-2a292757b71b}} are implemented as 2 hidden layer neural networks with 64 hidden nodes per lay... | r | 85eb98a97badc5e1d8e8d84c7281592f |
Further, we do not require a dataset where ground truth body, hands, and face reconstructions are all available at the same time: creating such data at sufficient variety is very difficult.
Instead, we only require existing part-specific datasets.
Our network features four task-specific modules that are trained individ... | i | dd9ba118c5e817a269b76f08fd8518df |
Classification
Promising results on reinforcement learning tasks lead us to consider how widely a WANN approach can be applied. WANNs which encode relationships between inputs are well suited to RL tasks: low-dimensional inputs coupled with internal states and environmental interaction allow discovery of reactive and a... | r | 378e69c1d698f8ca813b5d187577b798 |
The existence of this singularity has direct implications for understanding generalization in terms of the training process, especially the Neural Tangent Kernel (NTK) {{cite:d31b805ea769ab60f19d61f5165749fa944de9d8}}. First, this confirms that training is a dynamical system, not a difference system. Second, NTK and ot... | d | 7d154981ecbb21e6159129f290842562 |
Several studies have been conducted on the linear MRI theory ({{cite:5b723287220b53d22cba810442c0f0e065bca9db}}, {{cite:76931c45283945666c02d5ae6783ab7fe1cf112f}}, {{cite:282cf548ed599fc8fbf1ca8b8ef74d06da852ab0}}, {{cite:3f400a7ccbeb5d60d2eadcade9dea604717e861a}}), but the full nonlinear development of this instabilit... | i | ef29f2f8b10ded9d7cc44234cc33146a |
In Table REF we present 3D FILM results but using the contrastive pre-trained encoder described in Section REF . Specifically, we perform an ablation on the type of data augmentation used during the contrastive pre-training stage (described at the end of Section REF ) and find that 3D data augmentation is essential fo... | r | 13d6504279dbf55fe75bde87781f7eb1 |
It is a common practice in training SGG models that the relationship detection module also learns to output refined object detection results, which either only refines the object classification (labels and confidences), e.g., {{cite:7a4a540b37ed8857efaa4a6b87a97bc38e925d8d}}, {{cite:981f405f2f12ba34d68326824251e15c3676... | r | 3e907caa4c482bb107104fb9ec233252 |
Figure REF provides the overview of the proposed method.
Our SLU model is a combination of two pretrained models. First, we
use Encoder block of pretrained end-to-end ASR model {{cite:140e0809ee0104c5ff7e6f78cdb57caaa2e1724b}} in order to covert acoustic features of speech signal to hidden representation.
Second, we f... | m | 35f451f92d7bb8748df51c783ab03d06 |
In present work we provide a rigorous definition of parameter concentrations and demonstrate it for the variational state preparation. The definition is motivated by how this effect can be leveraged for efficient training. However, different approaches claiming concentrations appear in the QAOA literature, yet our resu... | d | 82b13d1bfc6e6a74359187d7c6ff0034 |
Fig. REF shows the style transfer when the style and the content images have an extreme mismatch of image features. DPS {{cite:21702dd039853dc943ae2c5a06892935b4ec8ec4}}, WCT2 {{cite:b7933280980ba987620b1935ce9f99f68b8020a1}}, and STROTSS {{cite:e3544df5b1e52f0b9738f4936a4025abc4a28e7e}} output images with higher perc... | r | c9d2dc39c2a003b5bd7b38b4702bf260 |
NSF-TransE (w/o SDBN) and NSF-DistMult (w/o SDBN) are the models trained using our approach without the ShuffledDBN layer. In NSF-TransE (w/ SDBN) and NSF-DistMult (w/ SDBN) we transform {{formula:b0510528-62c5-4e2c-89c3-1263d494aefd}} , {{formula:95c4480f-6d33-4146-a0e9-c5e998d83dd7}} , {{formula:604a923f-97f5-41b3-9c... | r | 8b7d79b2931ece6a5770ea78f0d45635 |
Among various extended models, models with no dimensionful couplings in the tree-level action have been attracting much attention recently {{cite:c4edb0207a639c6907517a1bdf4989aa6e39457a}}, {{cite:e39c25456ce535f0a3454d3ee73ffc233ce2dcb0}}, {{cite:2ab6db3da89b785f98be311a7c4c9e55703b6b0c}}, {{cite:7c5384ea9625f4a5aad18... | i | e6cad01097c9f9caea361daa26fb791a |
Table REF summarizes the results achieved by
{{formula:a01b09b2-b2c2-486d-b788-6e225c99b2f3}}ARSRG on COIL-100 database. In order to
perform a direct comparison with the methods employed
in {{cite:26e1686d8380705913b1da0bc5c4a6ce77b5dfb5}}, {{cite:43112d9992a67ecddcc3865207f602d96188b57b}}, the same setup is
adopted. ... | d | 146da446845225d2fb6f0cb0cdb1d4da |
Over the last two decades, extensive efforts have gone into producing
efficient, fast mixing MCMC algorithms, in general, and in the context
of GLMMs, in particular. For example, effective data augmentation (DA)
strategies have been proposed for specific GLMMs {{cite:544aa63cbc07008a47044e1c3ac5878b53d41bcc}}, {{cite:f... | i | c2024292aaf9a27d54ec73d422474588 |
Classical orthogonal polynomials as many important special functions satisfy remarkable relations both in the physical as well as in the spectral variables {{cite:b2aceb619632ce008ad24e7950a4c32c34799809}}. More precisely, they are eigenfunctions of an operator in the physical variable (say {{formula:6e07d148-4a11-4197... | i | e2e54c6998d6d53baddad0bd52cf54ef |
Key challenges: We present the first theoretically grounded work outlining how to create good graphs for learning from unlabeled data. Graph-based semi-supervised learning literature has largely been focused on learning approaches given a graph and very little progress made on the arguably more significant problem of d... | r | c3f0b320fcadde28d4f5e71f26a00f4b |
The new dataset follows the same
principles as Kinetics-400 {{cite:909069f39962e6a87a8cd23e740cd144449b95d2}} and Kinetics-600 {{cite:36ab7ca2c48e97dfba11653120b93e81b5474218}}: (i) The clips are from
YouTube videos, last 10s, and have a variable resolution and frame
rate; (ii) for an action class, all clips are from d... | i | 95d30827253f9b4d4f363d6e06e79d32 |
Self-absorption is not expected to produce double peaks in lines of low species, as their concentration in the low density foreground layer is not large enough to absorb photons emitted in the dense core. Nevertheless, self-absorption has been considered as a possible contribution to double-peaked line morphologies for... | d | 4438e9ba5e46edc899bc79ca0029bbf0 |
Lemma G.1 (TPM, adapted from {{cite:0ab8b11e0f58eb7f6fd6d2998a7e126af2366202}})
Consider an increasing sequence {{formula:d9b3951e-5780-413c-9981-4e51c4783d6f}} defined by {{formula:6ef02738-a963-433b-81aa-9107eefaa0d4}} for some integer {{formula:0876e28c-c8d0-4972-9365-2bea46bed303}} and {{formula:00178979-bf90-4... | m | def7e217f458ce44532d787b3b583954 |
Note that, by definition, {{formula:8b5ccd74-37b7-4190-ae36-6001ac80887a}} . The Lagrange functionalThe derivation follows directly from the AL method discussed in the next section and hence is omitted. {{cite:b6ecf9e6b3335670533e8d1f3cf401ad11b53049}} associated with the optimization problem (REF ), i.e.,
{{formula:f0... | m | 0a68be5a32fa080994f95dd78907c400 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.