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The presented results were successful for the LC task, but our trained models presented some failures when increasing the difficulty of the tasks. This may be addressed by adjusting the fitness score to reflect the success conditions, as well as by applying curriculum learning {{cite:5128dc8f4d368ac06893c2b073c029c2356... | d | 3f2629e3aa3d75772a50cf491f3ac057 |
The {{formula:44e1c45b-cb7c-45e6-9790-2b6d7059a363}} resonance of {{formula:90aaabde-b47f-4b5c-b1b4-92a5cf75ff42}} MeV and {{formula:2e6b5000-2175-465d-a8fc-7c9eee3e5bb7}} MeV was first reported by the KSU group {{cite:c0f0b77c524d1ce67ae4853e1be37a04562d61c5}}, and the bump was confirmed by JPAC analysis {{cite:ff3... | d | a1c3a1ef1d93dede664ce3ce4c582c7c |
During training, when the ground-truth epipolar geometry is not available, we use a pseudo-geometry predicted using a pretrained LoFTR {{cite:d9f9fe0de0c6244f8c50369510f923b3651eaa02}} model for matching and MAGSAC++ {{cite:a6437fa03904f1500cc2f723a7329a5246337d7c}} for robust optimization. The quality of the predicted... | m | 65aee640db8306c93535a9e8392c598d |
Surprisingly, despite their widespread popularity, there is very little theoretical understanding of these gated models. In fact, basic questions such as learnability of the parameters still remain open. Even for the simplest vanilla RNN architecture, this question was open until the very recent works of {{cite:a443062... | i | e250b6e0c11d1b158915d3c7e9a8c6f6 |
Our data sample is uneven because of omitting some damaged images in between. Therefore to analyze the temperature oscillations, we use the Lomb-Scargle method. This method is developed to use the technique periodogram, in the case where the observation times are unevenly spaced {{cite:df86c3e22648c8abf184c4137aa903001... | m | 3d859730666eca16d4c7d83693784037 |
Regge-Wheeler (1957). Even before the discovery of the Kerr solution physicists were interested in the mode stability of Schwarzschild space, i.e. {{formula:f3fae279-befb-4401-ad1b-8a5d238919ab}} . The first important result goes back to T. Regge and J.A Wheeler
{{cite:b9d4834dc23c2abc877af5e5cd96bd2d4146af3d}}, in wh... | r | 0a750a76a3517d8651a26455987ac0a8 |
The most recent learning methods in depth estimation use deep features to perform dense multi-view matching robust to large environmental lighting changes and textureless or specular surfaces, among other things.
These methods take advantage of well researched multi-view aggregation techniques and the flexibility of de... | i | b5015a44607e6346fb32817308c6e842 |
As shown in Table REF , compared with the SecAgg protocol {{cite:95bacaed1b5ee9245aa3a3880d420ead847fa3a2}}, LightSecAgg significantly improves the computation efficiency at the server during aggregation. While SecAgg requires the server to retrieve {{formula:6c1c10b9-8c0b-4652-848f-c175f34b3e30}} secret shares of a s... | d | 58611ad9ff4184f72014fa6afb141895 |
See {{cite:aea861875ef4f0d8b2f8e3f220378cdeb92da212}}, {{cite:64a48d97f901d5e08c8556b39a1392f5a54400b8}}, {{cite:806d1580cbfd64e93fe9012fb70af4e22db0e7b0}} for the detailed description of the Strang splitting and higher order splitting methods. The Strang splitting method applied to (REF ) yields
{{formula:19e43a0f-56c... | m | f71c00741f69fc511d6ce97d640033c5 |
theorem:Axkweakconverge:sum: Combine our assumption with proposition:Axkweakproposition:Axkweak:xkJck to get that {{formula:50b10f66-5c05-4c80-bc44-084874c60422}} .
Hence, the desired weak convergence is clear from theorem:Axkweakconverge:lim above.
The convergence result of corolary:Axkweakconverge is consistent with... | r | 2a4377fd07c5abc49848d590bc1399de |
The current research pursues to explore the fundamental role of the order inside a non-isolated system in shaping the information thermodynamics coupling between this system and the environment. We begin with a thermodynamic perspective to characterize an arbitrary non-isolated system {{formula:6e7b7155-e5f7-4b11-9f6b-... | d | c35cb3491c98b5237a3981e67672fefa |
There are about {{formula:290c016e-8008-4726-aac7-957175ea0d05}} materials needed in a single event, which can be a trouble for a supernova that has exploded more than hundreds or thousands of seconds {{cite:a87f31d6687c81dbf9602db2ee75e7afb4c4aa90}}. The magnetosphere collects the fallback materials onto the surround... | d | 0d14f038f1538c0013213b2e5ee46aa4 |
where {{formula:62b65fa7-93ab-4a65-bec9-63c1ed27f737}} is the input vector at the {{formula:670472c8-171f-496f-a40e-1c1b02069ba1}} th layer, {{formula:93c09c27-56a6-40a5-a642-ba0793d9e8df}} , and {{formula:bcbdce8f-cf8f-4bff-8d20-ae19a75464f1}} are the offset vector and linear weights matrix with learnable parameter ... | m | 1f41d9bf70afca08981182be9a6d5a5a |
It is natural to compare this
pattern with other mechanisms of Hilbert space
fragmentation which influence the localization,
in particular quantum many-body scars (QMBS), see {{cite:b0bf4178afe53b7140c077c16c761fa978c1d80a}}, {{cite:e01b2921908c2eeeac777d8cacf1c91a2edc81a0}}, {{cite:2f7494517031717c4fbf008275e9947061d7... | d | a0b040b49b0aff3e0e093170489682db |
We also perform post hoc comparisons of completion times among the interaction combinations.
The results are shown in Fig. REF . C1 is on average more than 33s slower than C3 {{formula:e58e52cd-083c-4024-9b4f-b6c817bbcbdb}} , while C3 is on average 23s slower than C4 {{formula:23dee0ca-a436-4392-af53-3965d201e07f}} ;
C... | r | 87cbbf6b4047896bebd70f249b5861b2 |
We measured the transmittance of energy in an FTIR process with an interference pattern as incident light. Both experimental measurement and simulation result show that the transmittance can be controlled depending on the intensity profile of the incident beam. We also show, through simulation, that the transmittance c... | d | 300b31ad72712fe9d528f00f85802329 |
Following the work of {{cite:067b50139b4fb09d1019e46355f2f98f4ebd708f}}, we have adopted a blob paradigm in which we interpret each sub-flare as emission from separate magnetically confined plasma structures from the cold pulsar wind which interact with the shock. The blobs are sufficiently separated spatially, but in ... | d | 57b3a404959f356aa86768c11fa3ad32 |
We study the following techniques: (i) Undervolting, i.e., the supply voltage level underscaling below the nominal level, reduces the total power consumption of the underlying hardware. Thus, it directly leads to the improved energy-efficiency. (ii) Frequency underscaling is used to prevent the undervolting-related err... | i | 234523cedc11acd032fd1e2a5b567631 |
[leftmargin=*]
Dual Molecular Encoders to Capture Global and Local Molecule Patterns. SafeDrug model firstly learns patient representation, which is fed into dual molecule encoders to capture the global pharmacological properties {{cite:69b47b463e92ab2e7a2ea294927461b26d16ebf7}} and the local sub-structural patterns of... | i | d10d5d844db1f38e32ac9465b48cf989 |
A more recent, but rapidly expanding healthcare domain, in which the vehicular characteristic of AIs represents a powerful resource, is mobile health {{cite:46c091fc7e39f903757c1ca5b498aafe8fc1c5e8}}, {{cite:977f1a0a5384946b8d4d6a12391b7fe130744f8b}}, {{cite:5d0b6a830d480fb545c9b14a587c0563b9ee1b6d}}. mHealth refers to... | i | a3b8b28e4bf4d1624cb152240a2f7948 |
Pioneering papers {{cite:f26fd63d33b9069eb090664913d83a3ddee5a2d0}}, {{cite:1ac305bc63dc65b71915b782736123a1d58a142d}} are based on the above-outlined kinetic framework.
Their remarkable contribution (an adjustable element) was to apply phase shift outputs of the self-consistent procedure of auxiliary-orbital-based (Ko... | d | f4530f60b2a83afa36bde284fd2ec099 |
Table REF presents the comparative results among MCCNN, fastText {{cite:af3494b3d3e8eae32b06938060aff847b677e72c}} and SVM {{cite:863f70c749e31872c3e7660940a397686cc82cd3}} with different representations on the four datasets, where Word* means the word representation is initialized with a pre-trained Chinese word embe... | r | b87e42b57002b8b2e9cbc7caeecf5e58 |
Self-calibration estimates simultaneously interior and exterior orientations. It is generally solved with a bundle block adjustment (BBA) routine, taking feature correspondences and Ground Control Points (GCPs) as input observations. Extracting feature correspondences within a single epoch (also referred to as intra-ep... | i | 357ae5a764233a21e910be35f350ca39 |
The best known lower bound {{formula:8ff121f4-79f3-4d22-8e03-697200cdd248}}
was given
by Gilbert {{cite:c6ffd76c1c802fecdbee9feebdd0d5b1172bde05}} for general codes and
by Varshamov {{cite:539411ba74568cd2e4fb4fedaa8520ed117cfeeb}} for linear codes,
where {{formula:30a2b60f-a50b-4719-b9ce-6ea0ec8862c6}} is the binary... | i | 79624d4e94e1ac94dfc02bfc8124267d |
where {{formula:d88b9af5-596d-4564-9143-105a04aabae0}} is an isotropic vector-valued Wiener process
{{cite:ae220847ba60274685a4cdaee44cfb8aa26e149d}}, and {{formula:41210c5a-802b-439d-9586-dc5cb7934146}} , {{formula:3ffbe85a-e927-42a9-aae9-743a9c0e6ebe}} , and
{{formula:6dd499e5-2a6c-4d26-8446-2a18bc4765ee}} are coef... | r | 7793465e678a881531813056340a20f1 |
Defining the {{formula:acb8f937-ec92-4a15-9925-76d85cdf7df1}} value: The {{formula:9709c55c-da2b-4211-9f17-8574518f8eae}} value delimits the search bounds for related discussion candidates. The RD-Detector uses the {{formula:5a399b0d-34b8-47f2-a361-1ca404c1b6aa}} value to select the similarity values of the top-K m... | d | 6ffaae9c9446fb96aa9786fd6b8021d4 |
In this paper we obtained a class of higher dimensional black
holes from {{formula:91fa21e2-1ee1-40f4-8902-ff2e5b79edb8}} gravity with conformally invariant Maxwell
source. The two key assumptions in finding these solutions are:
(i) the constant scalar curvature {{formula:18da0f84-2713-43af-a475-0fcb3d2a0e60}} and (i... | d | a708aa7cecf3af986692cf856b807279 |
More specifically, we followed a heuristic approach in order to conduct a parametric analysis for the magnitude of {{formula:75aa1cd7-10e6-4bd9-bd67-e34302169ee9}} which is a priori unknown to us.
The values of {{formula:c0583941-2279-4b7f-b1b9-cf88500b86b4}} were chosen with an “order–of–magnitude” based approach an... | d | 23e25d7a4df1e1d16fae3a77f7768c6b |
Classification of the solutions to the quantum Yang-Baxter equation for the
case of non-exceptional quantum affine Lie algebras was found in the pioneering paper {{cite:7706b333f9e7f7b0a8c809f04dbe4bfc25f9682a}}.
| i | cd37caeaff396e057dd3ab1288a92fd7 |
An interesting comparison can be made between the multiple peaks we observed in our analytically solvable models and the multiple peaks observed in random features models {{cite:a671d035673d1592ef0119cc812f5c6cc6fa9527}}, {{cite:32a2106b556df4b8c9957ae018a1d954a792d6b4}}. In these models, one of the peaks (termed “nonl... | d | afda714bc0452a1513953268f6ccf776 |
where {{formula:adb1ddad-b51c-4888-9095-4037ef0927d5}} and {{formula:79a86877-063d-41ad-9991-c3a993f54926}} represent the two- and one-dimensional Gaussian kernel functions {{cite:9272b0625f46c201611256a72c7e76c6d21e98c4}}. Consider once again a counting measure {{formula:038fc9eb-756e-4c6b-bd62-1da75f8232ab}} that ... | d | 981b970553a3332b9de4969e2ee1a9a0 |
Both {{cite:eb428fd6c03c3e815f32b678c987c21b08a49623}}, {{cite:a3cc3141dd39bb42d2b989c6435085e0b8ea04c3}} approaches deal with the {{formula:2b2f028e-e95c-450c-91b3-97c86edfa198}} term, while the temperature term {{formula:556d42c4-b706-4dc8-be7e-f1845713d86e}} and the coefficient {{formula:64d22c36-5e60-410d-b7c3-38... | d | c599e658bee6708e032f7e0b66b9a079 |
When a set of orthogonal states is not locally distinguishable, entanglement can be used as a resource for distinguishability of such states. This is called the entanglement-assisted discrimination, which was first proposed by Cohen. In {{cite:e5d4dba5a98e073063c9a08f0e28850079448648}}, Cohen showed that the tile UPB i... | i | ed8ee4dfa9a5a1c93c54518abdd0df30 |
There are several avenues for future research in this area. Here, we consider two-dimensional manifolds to facilitate convenient visualisation, however the inference and information geometry techniques are general, and can be readily applied to higher dimensional manifolds {{cite:3bdc688e08cdfcceebd0853906281d7877c35e1... | d | 10bdfe9864c3dfbe5f0aa127d906118c |
A fast but non-additive, as well as an additive and fair, but computationally demanding decomposition have been proposed. The approach relies on conditional samples that established methods like conditional SAGE {{cite:68b5ae5246ff597216b0570b048faaed6e6c6c2c}} require to compute anyway. Thus, the computational overhea... | d | 36d219abc5fbaa85ace17e02e7e31248 |
There is significant progress in understanding the Galaxy's spiral
structure in the past few years, which is heavily dependent on the
developments of astrometric observations by the VLBI in radio band and
the Gaia satellite in optical band.
The VLBI observations have the advantage to measure the spiral tracers
in dista... | d | 695c00752308a0b2e7e36c8115a4bc86 |
The PP method uses an individual time step for each particle.
It is calculated by {{cite:ff6b14ae2f6b9e58f630937674071fe429ddb9f8}}, {{cite:ba5acdea781bff542ede6de4e40ef9452642053c}},
{{formula:361bd730-b6ee-4b4d-8a05-ef690b23cc6c}}
| m | bcb9ed183b72c6ce993bf02f7c49727e |
In this work, we compare our RealBasicVSR with seven state of the arts, including four image models: RealSR {{cite:e009f01f31e753e14f7cde4a1d38f01f469e92f5}}, DAN {{cite:0bf865fefb83b66f206433d9ed5b318069279018}}, Real-ESRGAN {{cite:978d8fc95ff8aed92a80e901a58f055052467352}}, BSRGAN {{cite:97953dc0c9f10e7642a175efde23d... | d | 73592499c19973774c544276139f45c0 |
The block diagonal plus low-rank approximation we use is well known in the
literature as the partially independent conditional {{cite:a5b0441e7b9998127822c68814864d719c3972e8}} or
block full-scale approximation {{cite:fb79f0fcd29de498cc4b5c06d45b2c131e79fe9b}}. It is also a special
case of the Vecchia approximation {{c... | d | a2d76d35fea7b881b82600f679f920a2 |
Another objection is that, if the optimization is performed relative to the re-projection error, then re-projection – if performed causally in time – can be understood as a form of prediction, and many approaches have used prediction as a generic task for pre-training models. This is true, but with two caveats: First, ... | d | 4f749a43922e616deea55b00237b7898 |
However, using length normalized log-likelihoods {{cite:57f797b78a940dfcc2899aac1b35bb2504fa97dc}} has become standard due to its superior performance, and is also commonly used in generation {{cite:c20a8d877e1d7789155cfe1a854684c95c06dbd8}}, {{cite:8155f96301bdd4e5d848abb28132a9f8e79f6743}}. For causal language models... | m | 9ae205009fdfd57a8c3d2847479a8a5c |
In learning theory, we assume that the target function {{formula:de71aa08-8c8b-46e9-907b-648bc6b60d56}} exists throughout the paper.
This is a standard assumption in approximation analysis {{cite:58e884059a9929d3346bd20d085ffb2c41264b36}}, but existence of {{formula:3a08e272-5429-4db0-895d-a281817822a9}} is not ensur... | r | 715a3a8f707c4274a698da531ba5981e |
We have shown that a non-trivial inner product measure for the states of four-dimensional quantum gravity follows from a simple reality condition on the quantum Ashetkar connection, and a closure assumption about its integration contour, leaving room for the possibility that the CSK state might be normalizable when the... | d | e0585e9dfc1d518dccfcd2fe00708ea5 |
The study of Quantum walks (QWs) has been paid much attention in the past two decades because of its applications{{cite:16c995f617f39b47fd2d03fecb551c3630ba37c3}}, {{cite:feba9ecf32ca74c7fd6473ff7cebe20df2a72a46}}, {{cite:86b0d3ad663d5a3a068ec9559e67f3120cee4a05}}, {{cite:3bcbfd78717da40851fb398c6ed983db138f667b}}, {{c... | i | df79f429e008f2d7c6eced7ab9aed4c5 |
In a second phase, we generate feature point clouds to detect areas of interest. Since it has been demonstrated that objects in images can be expressed as a large set of smaller visual features{{cite:a996d729defc54aa6d4cb5d34a23004775425b68}}, we can reasonably expect dense feature clusters to appear around objects, al... | m | 4bfc732342b3e3ef60cbe6b1e8f73fb7 |
Experiments with network architectures in {{cite:3cdc1451dc5e355fc4482585784510f09aa046ec}}: We used the training settings in {{cite:6151adf0380ddd87a7b10ac1cad0fd4132001c95}} by default. For simplicity we set {{formula:bde6eb04-21a3-4e14-ae66-faa82d82e12f}} and batch size 16. The augmentation probability {{formula:43... | d | da35bc8730eb7769f03b07915d3cf392 |
The variational approximation replaces the original posterior distribution, {{formula:4fc5c9f9-f64a-4294-a9ad-295a1aad3df4}} with an approximate distribution, {{formula:81b4ef78-6512-4c03-afea-41a4e1b0ccf4}} , where the parameters of {{formula:fbbc4de5-5c6c-4f96-bf50-6a52da70fc35}} follow the gradient of the resultin... | m | cc12bd2ad8c0a031992d535858d3be0e |
It was believed that inflow generates the redward shifted broad emission lines with the blueward asymmetric velocity-resolved lag maps obtained in RM observations, and outflow generates the blueward shifted broad emission lines
with the redward asymmetric lag maps. However, the asymmetric lag maps and shifts of broad e... | d | c5f3fd5baedeb1201d4aa2af0c926b96 |
Scene Graph Generation (SGG) aims to provide a graphical representation of objects and their relationships in an image. Recently, SGG has emerged as a promising approach that bridges the gap between vision and natural language domains. It has been found to be useful for many vision tasks, including 3D scene understandi... | i | a679187746c3f5323e0020ceea45dda5 |
For robotic applications, occupancy maps are a long established form of environment representation {{cite:9a1f00b9c99e510623a786811a8f2596ae848bf0}}, {{cite:ec453260b9333db98550299bdf7ddc1912bdffca}}, {{cite:6e2ddf66772b6464919a2182f5b8dd62d59979fb}}. Because of their robustness towards environmental conditions, radars... | i | a42e0faae520b25bf8c8cac32dd020f6 |
Figure REF a shows the measured {{formula:0db7bb31-ceda-4600-b57e-311845a9fb90}} -band visibilities, which
slightly increase from 0.8 at 8 {{formula:ccafd8d9-88c9-45db-afe6-8c3cee8dc82a}} m to 0.9 at 13 {{formula:17a9695b-d7ff-4488-8a75-acb12ab7482c}} m.
Figure REF b shows the full width at half maximum (FWHM) obtained... | r | 94f1b52bf4eb49c4b8d5a71d58a3163e |
We look at the type of shift incurred in classwise-DG compared to classical DG setting following the study of datashift by Moreno-Torres et al. {{cite:46ffd14d45d672a0811f2b900bd9963cfdb33a51}}. They study various data shift and broadly classify them into four different categories, namely:
| m | 8b32258506948c8a020b0f32ec1542a1 |
Fig. REF shows visual comparisons of the proposed method and other SOTA methods, demonstrating that our method can achieve more accurate results.
The precision–recall curve (PR) in Fig. REF shows that our method outperforms other methods on most datasets while only performing slightly worse than ITSD {{cite:ca47d7dfe... | m | dbe69c698cf0aee78b24b8f7a51e3e14 |
One approach to solve the cryo-EM problem is to estimate the missing rotations from the observations
and then recover the 3-D structure as a linear problem.
This methodology is used to constitute ab initio models {{cite:b4affcf8be4f6079bd2f6573d78e4451507db066}}.
In {{cite:c2fa4a069f02c1f156e82c85528e549ab252473b}}, {{... | d | 01cd9aa1dd579881ee550d062ed94ee9 |
In our methodology, we aim at being able to measure the similarity from words, phrases to sentences and paragraphs. So, we use the same embedding mechanisms that can encode various types of text pieces to the same multi-dimensional space. Specifically, we experiment with SIF {{cite:5ae977a796a5958638c74f53191bfe229112d... | m | 43c70e288768048a622eb062e11f4d07 |
In this work we pinpointed one direct cause of the performance drop and instability of DRO: the sensitivity of DRO to outliers in the dataset. We proposed DORO as an outlier robust refinement of DRO, and implemented DORO for the Cressie-Read family of Rényi divergence. We made a positive response to the open question r... | d | 37b4e32e1e3d3e898fc66e87a4db7e35 |
In order to further systematically analyze the relative performance of each comparing algorithm, we use the popular statistical test - Friedman test {{cite:40cd810cff80a9900712b913072d59569f30e73a}} for the comparison studies of multiple algorithms on a number of data sets, with respect to each evaluation metric. Speci... | r | 6e7beac68af528b669b5487c1d2f643e |
In Table REF , we present the results of two trained generative models using the full data set of a randomly selected occupant in the Fall semester. We trained both a conventional auto-encoder and a recurrent based auto-encoder. In Table REF , we present several selected features, either from the interior of a dorm roo... | r | 8b8882112d68aa1ddac996fcfafeb1b0 |
Similarly to the power of the FDP-controlling procedures, the power of the FDR-controlling procedures applied on partial conjunction {{formula:b541667a-5c9b-47fe-998d-14958bf8e393}} -values strongly depends on the choice of partial conjunction tests. In this work we developed sufficient conditions for FDR control on pa... | d | ed2cea55d1074e21c819e09dd3940dcd |
Using the same experiment given by Section 5 of {{cite:7d9e1586f5f1a7cab7747249cee01c085898998b}} and also {{cite:4452e49bc9ad6233a394dff643b2373e90cb1439}}, {{cite:84598abae5aefa5fe463db98309f62abd485cb61}}, we
compare our bounds with previous works. We reproduce
the experimental results of our method by directly runn... | r | efa457b98007998f932018c03a5e3656 |
We evaluate the accuracy of MultiScaleGNN with {{formula:a26b7d0e-aad0-454f-adc9-bcbc71195a69}} and 4; the architectural details of each model are included in Appendix .
Tables REF and REF collect the MAE for the last time-point and the mean of all the time-points on the testing datasets.
Incompressible fluids have ... | r | 88a3a3ec336bf19822903d3ae16172c4 |
Overview. An overview of UDAVT is shown in Fig.REF . We propose a two-phase training pipeline where the model is first trained with source data and subsequently adapted using source and target data. Our model is defined as {{formula:22b4ce06-81f6-4280-933d-1025b7b800d5}} , where {{formula:bc8c5ad0-e44a-4822-8f7b-a5603e... | m | e0fb9f06b814f2b53078622f09f9a584 |
The fractal dimension values (ranging between {{formula:4576c32c-3a25-4dfa-bd58-095fe169972a}} and {{formula:eb2992ad-20a6-4a54-9dfd-57bbd46dc965}} ) used for the scaling law estimates were taken from third party sources in {{cite:9450e3ac3cdb3189b4ccc0280b2c2639e5277999}}, {{cite:f3a9e41ee5a01d2e21fecf1e98c9c1a6081c4... | d | d414355dd5c9d051797c87536ac8dd68 |
To test the statistical significance of the assortativity between individuals' properties and their neighbours' properties, we use the directed configuration model {{cite:41799942191ba622d041379d2aff3ac75a390ce6}} to randomise the network structure. The model randomly rewires the edges while preserving the given degree... | m | 28e065c016e1560cdc9b7214b5e977b2 |
We evaluate the action anticipation model using Breakfast {{cite:cb36f34527be558e04dba41b93febd50da069e75}} and Epic-Kitchen55 {{cite:27a384c5656f6e0769d13391c3c98ef71c1ee144}} datasets.
We follow the protocol of {{cite:afd506d4b7f07db01e63bafa831f8ad785ced1b0}}, {{cite:ab8afaa4a50e07968aa0dfe2f88cf29141d849dc}} for ac... | r | a5d6578bd6099513c2ad058d01070153 |
In this paper, we consider a single cell where a BS with massive number of antennas communicates with a UE in FDD mode. Using the fact that the channel matrix over each subcarrier is a function of a smaller set of parameters, namely, the number of propagation paths, the path gains, phases, delays, as well as AoAs and A... | m | 475e9f1faf2e0c7f98f9994ab4bf598f |
In order to perform backpropagation through the Poisson equation solver, gradients can be evaluated with the adjoint sensitivity method ({{cite:f71bf303c8494d378e5ab8d2f9725dd1d1e75949}}), which is often applied in constrained optimization problems. Recent application in machine learning includes the Neural ODE ({{cite... | m | 0116903a82521ca81fe160e3bb306e7e |
Theorem 6 ({{cite:383e6e7db9b9e70e73240c8ba81be0db217a52a1}})
Let {{formula:c6cbd090-1ab4-47bc-bd4a-9bdae040cbb8}} be a bipartite graph.
The graph {{formula:eb91163b-45e2-4911-8704-15e5b17d8e86}} is a difference graph if and only if one of the following equivalent conditions is true.
| r | 430b5246187db3b12facff32255e1db9 |
The Oxford Dataset {{cite:93f0d88c75c2554a067954129d48ab2c45a5671b}} consists of 5,062 images of 11 Oxford landmarks, collected from Flickr. We utilize all the images (including images in which the buildings are not present, heavily occluded, or distorted).
Babenko's Landmark Dataset {{cite:500c9e33b3a01e63a704b6450d... | r | b79636a6fd5d1d8936294b8a80836bdb |
The main database used to evaluate our algorithm contained 130,463,526 SIFT
local feature vectors {{cite:3ee0b2acdbfe8789e370773daff97cd319cffb0c}}, with 128 dimensions
each. Those feature vectors have been computed from 233,852 background images
from the Web, and 225 foreground images from our personal collections. Th... | r | 2a31a3dadfd5fdd192900f78b9fa51dd |
In this paper we focus on the Poisson resetting process, with resetting events happening at a constant rate {{cite:196c73dae08a149518ff768bc761085126d9c106}}, {{cite:c1b7f01091505c6585abdbc11951dc75f7ca1273}}, {{cite:ef311da2849981f5ff3ea49262747312f6b3a66f}}, {{cite:6618ddf783d5b3b286b2f2a6796e075636d9aad5}}. For suc... | i | c88104fef2016854a0ef086e225e2b37 |
Proof. Define {{formula:8792e01e-b5b5-4c3f-8f17-d17964dda0b8}} by {{formula:4703e3ba-8c80-4550-b24c-bba6c9461bed}} . Clearly, {{formula:d393600c-686d-40e9-be5f-9abeac005360}} is locally Lipschitz continuous, and {{formula:a71caf5a-526c-44f7-862c-5ca17cdb13e8}} . Moreover, it follows from the assumption that {{formula... | m | 49120af2ac55db9d0cf5dab0eb211a6a |
This result is a strengthening of the original SLS theorem (Theorem 2.1 part 2 in {{cite:30fd88e8d9b78f33732b8d183863454fb1956dd8}}) where we characterize the closed loop behaviour of SLS controllers constructed from any closed-loop operators (not necessarily satisfying characterization in thrm:SLS). Therefore, lem:clo... | r | d399481746ad53f2eddd7ce7b1eba228 |
Assumption REF of Corollary REF implies that the process under consideration is non-stationary and we subsequently find that we can recover the root. We further show in example REF that in the stationary, non-reversible case it is not possible to recover the root. This seems to fit with the collective empirical evid... | d | b702b6598241ddebc8c868407a0466c4 |
Table REF reports performance when all methods are trained to optimize the imitation loss alone. Behavioral cloning yields a high number of trajectory errors and collisions. This is expected, as this approach is known to suffer from the issue of covariate shift {{cite:e209eaab309ca2e6e5506b346e15e6eb5e4e5252}}. Includ... | r | 43eb2d0998aa82106f428feea52b4205 |
Lexical metrics, such as Bleu {{cite:9e57ecb27a2ac75f81df9ff4b13af4fac75a92aa}} and chrF {{cite:38c600eafc48d0614bbdf92f12494a7fee1d7b1a}}, have long and widely been used for translation evaluation.
Both metrics compute strict matching between translation output and reference at the surface level.
Bleu counts the n-gra... | m | 2ff4438490623ead4d3ba0678e90c1b6 |
To evaluate our proposed method, we have made a comparison with the DenseNet-169 {{cite:a558e08c575d5f595616006944050f72948d2b7b}}, which has been trained under different pretraining methods according to {{cite:321a278c34ddd5ad94dc0a6efe15184a242a2842}}, named random initialization, Transfer learning (TL), and TL with ... | d | 1d4c2047f9fef34944d0b6c2c4f6a235 |
Clustering has emerged as another important class of unsupervised methods {{cite:9b0a3565f6b7440d45658bc96dbeb40abdd34353}}, {{cite:59fb87faf23788c35e9cb5587ee75b74cc6d3721}}, {{cite:4d31954523000c60692d10983b856a7ad03523c4}}, {{cite:a20c6798b0bbf4dd7954d44a45dd2f945992d618}}. Popular methods such as DeepCluster {{cite... | m | 667f4857e581628b4278ff7b581cfce1 |
In order to evaluate the proposed method quantitatively, we compare the proposed method with the state-of-the-art deep learning based methods {{cite:68927bcb7daf42e76dc0e86ecfda0aa2c5114fc0}}, {{cite:3c543694b970b25969e5e66d5e3ddb06feacd7f2}} on validation set in term of the number of depth, learned parameters and segm... | m | 3a6acebc189c7d8f9f7ab8991270030d |
The total computational cost {{formula:64f56099-3cfe-436d-b9e1-2efc89e9bace}} of algorithm FSS is the sum of the cost {{formula:f436d968-c5a4-4ea4-a445-837066060911}}
of computing the approximation {{formula:989a8c1b-3590-4147-a81d-373a25648b66}} of the affinity matrix {{formula:4f6eb2c4-4b26-4b15-ae6c-73e579227220}... | m | fa644986846c02fa5f9765f97e5a3796 |
It is not often clear whether the interpretability methods really highlight features relevant to the algorithm they interpret. This way, Adebayo et al. {{cite:2f294968abb65bf57ad62001c9f893aad16eaa5c}} showed that the attribution maps produced by some interpretability methods (guided back-propagation and guided Grad-CA... | m | 61546747519b404a15609c68ac164415 |
The inhomogeneous complex nature of the quiet Sun (QS) is predominantly due to the presence of a large variety of small-scale magnetic features. It is believed that these small-scale magnetic features are essentially the sources of magnetic energy, which can be dissipated to heat the upper atmosphere {{cite:4f9cac2362a... | i | ad3d6bdd9ef68495cebb9f5bd2840edc |
When there is a mirror-like mechanism that makes the amplified wave be scattered back and forth between the mirror and the black hole, the background black hole geometry will become superradiantly unstable. This is dubbed the black hole bomb mechanism {{cite:c93549a29c5d4670f23dac2e327d14c8343d5973}}, {{cite:c7d087eace... | i | 9a6e6724bd14deef5c9a3fbdb9b4fab6 |
We compare our approach with previous monocular state-of-the-art methods on the tasks of 3D localization and 3D detection, using the Bird's Eye View (BEV) and 3D AP metrics, on the KITTI validation splits in Tab. REF and Tab. REF , respectively. The results of {{cite:0209e077356df8a301c075ae268575c1abfc4421}}, {{cite:... | m | cb0257c0803d178dac74ee479bd40e91 |
Supervised training of neural networks for computer vision tasks requires large amounts of training data. Currently, manual annotations or additional sensor data are commonly used as ground truth for visual recognition tasks. However, manual annotation for low-level tasks like semantic segmentation is time-consuming an... | m | b28a570067558756963f302a6ba832aa |
We consider the problem of distilling a PreAct ResNet-50 teacher model trained on the ImageNet-1k dataset into a PreAct ResNet-18 student. Similar to the previous experiments, we replace the transfer function of the penultimate layer with a leaky ReLU with a negative slope of {{formula:93beca84-1b70-4052-8cee-f62c12757... | r | 2193d8984643d8556643cca1195500e2 |
Although the success rate obtained for the {{formula:2752ebe9-6747-4627-9844-641556cb8b9f}} -SCA when applied to solve the TSP turned out to be smaller compared to the obtained when applied to the other problems, this value is still greater than those obtained when SCA and Glauber dynamics were applied instead. The {{f... | d | 7b35be1d83c25a1b4c730445c8a8f045 |
the last operators are known as fractional integrals on a whole real axis (see {{cite:768cc469cdc43ac7ff9f7f2cbe55e733f13b71ca}}).
We need the following auxiliary estimate which follows from the Hardy-Littlewood theorem with the limiting exponent, see (40) {{cite:39bfc8acde602c87784052b2dad67cd97b49f5e6}}
{{formula:6e5... | r | 6734d4469c6b8e736216d7d22c0950b1 |
We test a state-of-the-art TextVQA {{cite:c6197bea0ba4d66f02948de43426f78ea6ed85f9}} model, M4C {{cite:62629027736550445f1015884ed477c06eddfe68}} on AdVQA. We evaluate two versions: (i) trained on VQA 2.0 dataset, and (ii) trained on TextVQA {{cite:c6197bea0ba4d66f02948de43426f78ea6ed85f9}} and STVQA {{cite:576ecb9e29c... | m | 0c0b74f2ae013d42d416ed1a40e67a4f |
Despite their simplicity, the ability of the two classifiers proposed in this paper to correctly identify a BBB is highly satisfactory. The results are particularly good for the CLBBB rule, which confirms the potential of omeR in diagnosis. Specifically, the sensitivity and specificity values obtained for the CLBBB cla... | d | ee61c626f76f955acdad63379ba11887 |
We perform experiments on two regression datasets, Boston Housing Dataset {{cite:fddad7a8102e2a869972c65dad676edd1e2790d0}} and Condition Based Maintenance of Naval Propulsion Plants Data Set (CBM) {{cite:c7a2cf1c6db29794c68e1ae4fded53b0bbbcf76e}}.
The datasets contain {{formula:44cbd8d7-b501-4db6-ae5f-872c407511b9}} ... | d | e67cf9202ddc7da1db2f4f7606f8ceaf |
Future work can focus on the implementation in a 3-D environment, to have a system of complexity in vertical levels for the agent to interact with objects in the grid world. AI2-THOR framework {{cite:a3d22faa1e9765a4c15cd624741a4e8c27f60dc1}} provides free online environments for various house plans that can be met to ... | d | 04ec56a7435d5754f3fb16104baee466 |
Hyper-parameter tuning: Almost all participants applied hyper-parameter tuning which resulted in significant improvements in the performance. The methods require a lot of tuning in order to get good performance, and this consumes a great deal of time and is a dull task. A more critical evaluation of improving hyper-pa... | d | ae4e8149a409ebe246ac1edc1a88a5c7 |
In Fig. REF , we present the obtained WSR results of the MLBF and WMMSE algorithms averaged over 1000 channel realizations. The red solid line is the WSR results over iterations when using the MLBF algorithm and the dash blue line is from the WMMSE algorithm that is considered as the baseline (the WMMSE usually converg... | r | c45ff2992051cee6acfe21336684b0ea |
Remark 3 (Static optimization without gradient errors)
When the optimization problem (REF ) is time-invariant and one has access to perfect gradient information, then the result of Theorem REF boils down to {{formula:59cf81cd-2cc7-4a2f-99e3-24477a98bcfd}} , which coincides with {{cite:20e4bb75c787d7605f72b7724b1dba6... | r | 50c22e12579b2a5323c8a3c3f87653f3 |
Research Objectives. In this paper, we adopt a different approach to address the research gaps. We propose a novel multitask learning-based model, AngryBERT code implementation: https://gitlab.com/bottle_shop/safe/angrybert, which jointly learns hate speech detection with secondary relevant tasks. Multitask learning (M... | i | ed8d20fcb62774e3f198abb6ba42e1cc |
Situation changes with additional predictors when the AB approach becomes infeasible and machine learning approach offers possibility to explore how informative the predictors are for forecasts. When additional five realized measures (RM) are used as predictors, performance increases with respect to all measures. With ... | d | d5028c97aab63ed6ec7de4db3ee08396 |
The method of ANN for solving differential and eigenvalue equations include a trial function {{cite:5aa05975b2e23385b3aaffc14cdba2105b2bc67e}}. A trial function can be written as a feed forward neural network which includes adjustable parameters (weights and biases) and eigenvalue is refined to the existing solutions b... | r | e181e297b973fd70b29f59a58d41e037 |
Model V is the most complicated one. In addition to the fact that there are two interacting parameters, there is also an important dependency on {{formula:384fa276-a77a-4320-ab62-54146003e14c}} (and therefore redshift {{formula:d9ec838e-fdca-48ac-90d7-c30906c8a37d}} ), which means that {{formula:dd8efde7-6a49-4e84-b7e... | r | d1b19786ffba276ec6d7c6462a315163 |
GGL20ing the same Taylor expansion as in (REF ),
we get, on the one hand,
{{formula:8d771ae6-5574-48e1-af51-15e72ae5134e}}
and on the other hand
{{formula:52613ff6-22e0-4319-90a0-7d466f786cc3}}
We then get the result by subtracting these two expressions.
Proof of Lemma REF
By definition of local time, {{formula:a0... | r | 243ade9e8ba08aaabdf1b6acf449af00 |
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