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Notice that the term {{formula:2af4c627-3b31-44e1-90de-da8918258e87}} is the extremal length of the annulus (REF ), see e.g. {{cite:e073eeed68b83f543315b664691d75e4b7949767}}.
It is worth pointing out that a characteristic difference between the expansions (REF ) and (REF ) is the appearance of the logarithmic potenti... | r | 31b77c7bdb5d7c7963c0f92806c40580 |
More recently, Prompt Tuning {{cite:b318bee75a2a7ebc0bbe58a51e154ecc562761f7}} has received much attention. With large frozen language models (say, {{formula:385b7f15-801a-4055-b1dc-85b4d6f20534}} 10 billion parameters), Prompt Tuning simply adds a tunable soft prompt to the input of the encoder, achieving results that... | i | f57b20ce04eeae7c7a609fe53019d62b |
The earliest known spatiotemporal gait recognition techniques started in the late 80s. {{cite:eefc19939b219717d90edf4c8ba18b08bf850870}} proposed to recognize gait at a sagittal angle with the subject walking frontoparallel. It modelled the human gait in the form of a set of spatiotemporal snakes {{cite:1231893ffcf6dd5... | m | dc4cbe48ef8e6914e150a45aba7f41ad |
Monte Carlo (MC) simulations are performed with Metropolis algorithm for
Heisenberg model {{cite:cdd5a03c2b847fffd1eaafa7aa518917a8c45b9f}}, {{cite:58bb2eb5ce327bee71e6d21ce88efd94fe6d571f}}, {{cite:64d0d011fcb68ca5b80a6c2f8d77c687ae51d490}}. The size of the cell
in the MC simulation are 16{{formula:aec085c4-dd56-4698-... | m | 891d33daf92e42340ff6297dca8854f3 |
In simulations, we consider the geometric channel model with {{formula:649ab144-488b-4ae1-bbc0-73917b3e8548}} propagation paths between the transmitter and each receiver {{cite:ec7bb4f949f89eb7894892899539dfdc575e6951}}, {{cite:33d512bc6b156d5573f7a56f3ec693e15301c8fa}}, considering the high density of reflecting surf... | r | 5be3907291731a12a5dc9dd49fa3e108 |
In this section, we visualize additional visual results of our method in Fig. REF , Fig. REF , Fig. REF , Fig. REF , Fig. REF , Fig. REF , Fig. REF on Animal Faces-10, Food-10, CelebA-HQ, MetFace, Anime, LSUN-car and LSUN-church datasets {{cite:fb9b3cb3198e5aa453340358b1563f8a54d07b09}}, {{cite:3f252982d8cecb98a71f302... | r | 7c85f28993801544793a5b23294775ca |
In the framework of theoretical works, we will start with the iconic papers in Refs. {{cite:30049bae6638b2ab7b720adab8f40fd894d69596}}, {{cite:e768d46d986bcb3a420d291184d111e62f87ded4}}, Brodsky-Farrar and Matveev-Muradian-Tavkhelidze, respectively, predicted a scaling law at large transverse momentum meaning for large... | r | be14c91a38b4701dc9bc66494a30b17c |
As shown before, for least square regression the primal distributed ADMM uses {{formula:891ced09-4ee4-401d-930e-c820745e594a}} for linear updating. Since we directly obtain the linear convergence, we define the convergence rate of primal distributed ADMM as the spectral radius of linear mapping matrix {{formula:f7941c... | m | 1f07a62d1b8b040228a318d832441a33 |
All criteria are computed based on the implementation in {{cite:01b5ae16fc8954e892757eea4f6a0548eaaf922e}}, available at the publisher website.
https://www.crcpress.com/downloads/K14513/K14513_CD_Files.zip
We quantify the performance of the enhancement front-end that is trained independently, trained jointly with and w... | d | 5225c3c84a09be9ccc8d7915e557dd1a |
Concerning {{formula:e26c52e0-c2c9-44bc-bc92-e38cec87051e}} , defined in {{cite:ff29039797f9e89a6c9b79885e2d34f84497c019}} and here at equation (REF ), another simple identity for (convex) tangential polygons is
{{formula:0a177b64-dfe5-4894-8aa7-5105939cef3c}}
| r | 38f6ba23d969ff1d5dbb1cf4a42f9d6c |
As shown in fig:pipeline, the proposed system consists of two stages, i.e., the initial estimation stage, and the refinement stage. In the initial estimation stage, similar to {{cite:1763fe6f515e1f50eca3f5bb7c2b110d9b6f8ec7}}, {{cite:33e378ca6ce348a4c63a443e5809bf4cca0b61d6}}, a 2D detector is first adopted to extract ... | m | 492b8ec142327c48574101b066e2d9cf |
Given the above three different possible modifications of standard physics, we will organize this paper as follows. In a recent paper {{cite:5f1f7a6e449d514152cb6ee860e96663adbec931}}, we showed that there is an upper bound on the cutoff scale of {{formula:ecc07eb4-650c-45a1-ab95-41eb5d7d4a11}} in order to satisfy the... | i | 08fb319f57eadb11d51aadf908de3397 |
High-order discrete methods for hyperbolic conservative equations comprise an
important research area in computational fluid dynamics (CFD). The rapid growth in development of
high-order methods has been to a great extent driven by a radical change in the balance between
computation and memory resources in modern high-... | i | 70a098a16e65306603bc2a324d1153b6 |
The next lemma is the analogue of {{cite:b27d91d5b59172726f0365ee10b0cb5f597c5017}} Lemma 5.7.23.
| r | 0a3a71de292d2374134ba0801f885064 |
At the long wavelength limit {{formula:4ebd8d84-4f72-4f69-86da-1723fbaf93d7}} , we can find an analytical expression for both optical and acoustic collective modes of the system. In this region and for {{formula:88661dc8-6e84-4c39-af98-9967e273c233}} (since for all acceptable density values {{formula:bb6ec972-062d-4a9... | r | 707d12054136816d158209d8c0c47d67 |
We do not present comparisons of our approach to more traditional OMT algorithms such as the Sinkhorn algorithm {{cite:233d0eaabdf058b5246e86451706bf2e4eda0479}} or the linear programming approaches {{cite:edde745b8d1239d3cee8319b7831ac0d7deada99}}, as these frameworks, although they approximate the OMT distances, do n... | m | e94b14dfbc058d5d585a135756e6d8a3 |
until a solution to the original problem (REF ) is found {{cite:597fc15d5239153727911ca5eb95ab415938ad6f}}. This typically requires ten or fewer outer updates and a simple update, {{formula:267e0e52-cd45-4e34-812a-bcffa63392e5}} , that scales the penalty by a constant value, works well in practice.
Throughout, subscrip... | m | 63851fd360b2788d8d53ca420bd3588c |
Another way ahead is in developing robust uncertainty quantification framework via Bayesian inference for Bayesian Matrix NTMs. We note that the copy tasks by NTMs are computationally expensive and Bayesian inference via Markov Chain Monte Carlo Methods (MCMC) require thousands of samples or model realisations for samp... | d | 95d12c63e13c8249bdc5167deed788d2 |
Can we avoid inverse correlations with a larger training set?
Scaling alone without data curation seems unlikely to prevent inverse correlations.
{{cite:83ab4edfddadd635f1b9a4c541e1fb14a2a97d92}} examined a more general question and determined that the impressive robustness of the large vision-and-language model CLIP i... | d | f80572ddccde542410c28e8b9dce3902 |
which would give coordinates much like the momentum and winding mode coordinates. Showing that this preserves the {{formula:20e5a298-dd18-4ec8-a7c9-107ff0761314}} -structures the usual transition to Generalised Geometry {{cite:e7957e9a275ec604b5ef0012f286b3e49c9880cb}} could follow. So it would lead to a Generalised Ge... | d | 2b5b5ab0f7dfe5dd15dc7a5c563308de |
We study notions of entanglement for such non-spatially organized degrees of freedom in the case of thermal states dual to the BTZ black hole and thermal AdS{{formula:e22ed0ee-b48e-4007-afcf-81c574029236}} .
Concretely, we work in the setting of the D1/D5 system (see {{cite:b95ce2a2cf7d55a52ba31932961770ca67e94451}} fo... | i | d6751df94d90f44aef346a7db35d3455 |
The thermodynamics of black holes was established in Refs. {{cite:c6b1725f347e4bcebf5e2c23c4f483e2731abb02}}, {{cite:3cc0deb08ef03fd7e2beeabc1a2a397d11a8b319}}, {{cite:a3113fb02d91873ed305b32fd0884485664415d4}}, and in this section we will present our numerical results for the thermodynamic observables, from a BTZ blac... | r | 54a4bb7da6be10040f1fa3a6fb133101 |
Notice that terms equivalent to {{formula:e89146e7-0620-47cc-b80f-02adb999d3db}} and {{formula:1a57ce47-20c5-4171-8b8c-2806bf53f342}} do not appear in {{cite:d3e701db0a73650f8c0c2afd5ad1863602688ef2}} because they assume they are {{formula:7bd91733-cbba-45ff-8578-4c6843310158}} . The {{formula:af0b499b-1f7c-4f71-96ab... | r | a630dcc077ecf25b9a3603355776cc28 |
Observe that the processing techniques described in the previous sections can be also used to implement SCL decoder for polar codes with the considered kernels, using a straightforward generalization of the algorithm and data structures presented in {{cite:d3970af4d93fecd06f5e202baa30b7b090d99369}}.
The SCL algorithm w... | r | 52dd8cc6146b4933d883eaeda1623660 |
Firstly, it must be taken into account that the virial theorem of
Section REF does not include the effects of
turbulence on the dynamics of the core, namely, that many gas
collisions take place simultaneously across the core in the initial
stage of the simulation, so that the gas is simultaneously
compressed in many p... | d | b2a3e77b6fc29adcff51ca282df1407f |
Also, we have provided a new example of Type II (Example 2).
Interestingly, this example is different from the domino states{{cite:9658ed380b9509638d8a26ea1b0e1165a5dd0a92}},
which can be unambiguously discriminated using LOCC.
Moreover, this example is also different from the indistinguishable product basis{{cite:87ae... | d | ee50431f7f8661889927cb25bb18f9bd |
To state our results we need to introduce some concepts. Let {{formula:f17b8368-6973-4ef1-8a0b-3289dacb4c20}} be a standard graded algebra over a Artin local ring {{formula:019687d1-e28f-42be-9ebb-5bcbdc088cc1}} .
Let {{formula:248743c1-126d-4daf-aaca-f16a590301f7}} be a finitely generated graded {{formula:5e6d6ce0-0... | i | 55c6ebdde4285695d3529c7810c3ede0 |
According to these fragmentation criteria, the values for {{formula:bc4de676-1d5d-4ab6-bfb3-e6205394ec05}} and {{formula:c1a8b620-b553-4322-95e3-39d919d1dd8f}}
that we use in this paper favor the
collapse of the core and the formation of the embryonic binary system
according to {{cite:2612844806a0f810b8301c097413e8c1... | i | a44b13b26b8ac7c03606ddc1805671e0 |
We use the Hapke models for predicting grain sizes of a 1 µm radii even though the approximation model assumes that the particle size should be much larger than the wavelengths {{cite:2a2b430c59d2b0ba7294b4675ccd6ff94a795109}}. This study also uses wavelengths close to or even larger than a grain size of 1 µm. Indeed, ... | d | ded048708f6025fdd05c486de8d429b6 |
The proof of thm: localization convex will consist of three main steps: i) we bound the empirical error of the noisy clipped subgradient subroutine (Lemma REF ); ii) we prove that if an algorithm is on-average model stable (see Definition REF ), then it generalizes (Proposition REF ), extending results from {{cite:f5ff... | m | 3394221c3f3275ec07f5161d1747ed46 |
Measurement of the vertical distribution of optical turbulence in the
terrestrial atmosphere (OTP – optical turbulence profile) serves to
support operation of modern astronomical observatories equipped with
adaptive optics (AO) instruments and to characterize new astronomical
sites. A classical instrument to measure th... | i | a87436efa711c5210d5b0189f9aa2c31 |
However, the proposed method is limited in several ways. First, the neighborhood is generated based on the reconstructed data point. We lack a quantitative measure of the fidelity of the generated neighborhood to the original samples. Though the generated samples are derived from the VAE that was directly trained on th... | d | 73e26328ab6ec4bc5e59e6cf4fb15500 |
Although several methods of this type have been developed in the literature, most focus has been on the polyp class with many datasets being publicly released and deep learning methods applied {{cite:c5f1c0eff0488cacd8bea8189978b0efbe0c0c7b}}, {{cite:2c2141e5801711a7b5ac118914bcb2f6c74125cd}}. However, in reality, thes... | i | 8222b1e39ec9768b5def234956985a54 |
The requirement for very large datasets of manually labeled instances may seem counter-intuitive since this is not how humans learn to recognize new objects.
Humans are constantly fed with images through their eyes, and are able to learn an object's appearance and to distinguish it from other objects without knowing wh... | i | 4f122af8cf92c81300c2716aff472186 |
Confidence calibration with DocTTA. For reliable VDU deployments, confidence calibration can be very important, as it is desired to identify when the trained model can be trusted so that when it is not confident, a human can be consulted.
In this section, we focus on model calibration and analyze how DocTTA affects it.... | r | 49a1cfa4a688f603acca5e25817237fc |
There are many interesting areas for future work. One would be the development of flexible machine learning methods for estimating the confounding bridge functions. An advantage of doubly/multiply robust methods combined with cross-fitting is that data-adaptive methods can be used to estimate nuisance parameters, yet t... | d | 6f6f223ba1c987fb58cb9984565ca345 |
Some other researchers {{cite:0641f11a79e89281d9b0f5a4d7cd1bffea463865}}, {{cite:3edfdfced32121f6bc917954a8f1d81248a73328}} also proposed density-based regression methods to take full advantage of annotation. However, variations in the crowd scale and cluttered backgrounds are still the main obstacles.
{{figure:5fd34be... | m | 3faa5205b73e5c80e23437cc51bf3e2a |
Our work also has limitations. First, we evaluated only a single diagnostic use case of ICH detection on CT scans of the head, albeit with multiple datasets from different clinical populations. However, our approach is applicable to any other medical imaging use case that utilizes cross-sectional imaging, including dia... | d | 71539b7b95ce78ada08490b2e967ce3f |
In this section, we show that our network can outperform state-of-the-art deep shape matching architectures on standard datasets like FAUST (F_r) {{cite:b49e1a7b9080cb2698a8fbf8c5378071b05969f6}} and SCAPE (S_r) {{cite:6576a37f92d974c21f5635cb20ded39c0db4e120}} as-well-as non-isometric datasets like SHREC'19 {{cite:db4... | r | 5b8989c43689062ca0016e3506bf9e66 |
The domain {{formula:74dada1c-ac92-40a6-a356-fb024bb763ef}} has however been studied in a different, non-logical setting : it appears in the graph homomorphism literature {{cite:f28b674cd001608daa0de160a579d738fbf40f67}}. The object ({{formula:c05551d5-2682-43ca-afd8-f84cbbc413e5}} ) where {{formula:373af687-1480-463d... | i | f8e54a5b2b49ce82a946bfe0c5dac823 |
Leverage the {{formula:3f41c559-0a49-4a76-8284-06e2a9b0faad}} symmetry in {{formula:aa966927-d43a-49b8-9f11-b3b776141a3b}} and {{formula:460a7603-b0b5-409a-835c-8329dfc2ec70}} , which divides the cost by half. This can be done preparing initially
{{formula:dfcf2b65-c527-4a8e-b029-1d09e394590a}}
Then, one can use th... | m | 9916b26f8043abf671f26087b11ada39 |
We use the Kohn-Sham DFT based electronic structure analysis
implemented in the SIESTA {{cite:1925386d9c80cacee884a48935629abb653eee16}} software package to
study properties of the GNFs discussed above. When performing DFT
calculations for these GNFs, we include 20 Å vacuum space in
each of the {{formula:d75d9890-2a64-... | m | 557ec98dc4f34a9198c5433078ae10a7 |
In a LPAI, sequences of laser pulses are used to split, deflect and recombine matter-waves to create atom interference. In inertial sensors, these sequences of light pulses commonly use counterpropagating two-photon Raman transitions with large one-photon detuning {{cite:0e489501e42c84c5f0845d3908a8bf663faaa680}} betwe... | i | 771de387150e600b8ce3ae6246075c17 |
Another takeaway is that the design of multi-modal detectors should take sensor degradation into account. In order to avoid that the entire detector becomes overly reliant on a specific input modality. As our results with AVOD {{cite:c50e0505e3b8bd55a23ffc3c5599b2bf444bb4d0}} show, such a biased-design can lead to poor... | d | 87df11b9e04a067bbf2cd680fdca282e |
Above the percolation threshold, {{formula:e3a601dd-3511-43c1-ac0d-cafa186162f5}} , the giant cluster contains a finite fraction of sites, {{formula:38f83c14-d3cc-42cf-97a6-e0799a5cb79b}} , which behaves in the vicinity of the transition point as {{formula:9173332e-2602-40a6-99e6-5a4ee42743ab}} , with a critical expone... | r | e2717afdfc7dc2c5ced8cde2ba2b75c2 |
Chaotic scattering is an important area of research in nonlinear dynamics
due to its fundamental applications to a wide variety of fields such as physics {{cite:f8c2c8d7a4cc4042252ec55abdc8fbf3396d9bdc}}, chemistry {{cite:f3c18161af2676d248b8737e34acb73b02391c36}}, {{cite:782217419b930b55f59c4ec99cc8b2a706b6d544}}, med... | i | d2a0ca49e7a635cc679fda2408863af4 |
Before we focus on how the treewidth behaves under the bridge operation, let us recall the following fact, whose proof may be found in Diestel {{cite:959e202238be113cbc80a3b4357dfdb16dd75da8}}.
| r | efdd4c5c43f5ae7ef20730affc1d1228 |
where {{formula:d407841e-07d8-49f6-bf13-f7537c33a004}} , {{formula:9dd58e5c-9f2f-4f97-827f-dcf7ac350c85}} , {{formula:02db61a1-3ad3-48a2-93d8-c4501d0e518e}} are: the estimated speech signals, the clean speech targets, the estimated noise signals, the noise targets and the parameters of the model, respectively. In this... | m | 65fa899540e97b3b6af6a430c614e18f |
This is the Euclidean analogue of MuRP {{cite:852b4389e290c941097f80ac9e290efd9fef13ca}}.
As shown above, SemE models have achieved state-of-the-art performance in all nearly evaluation metrics on the two standard benchmark dataset.
Remarkably, in the task of WN18RR, the low-dimensional model SemE-{{formula:9f9dcf99-5e... | r | b6cbae913b3fb2592b0a0ba715b3e200 |
By the convexity of {{formula:40ee4520-9ef2-4113-bceb-c0856381604a}} ,
{{formula:7bf09f23-f710-49de-b7bf-2c50c6d13020}} is a convex function whose gradient, namely {{formula:2136e711-c78c-4cfb-b0b7-7d995bbd436a}} , is 1-Lipschitz
continuous (see {{cite:6982da160a3ee7bedf65ca0e527919da57c0490e}}). On the other hand, we... | m | 27d473d3271c925e8652eac9cf2f9e84 |
Employing VLBI data sets at 15, 43 and 86 GHz, a consistent description for the temporal evolution of OJ 287 radio jet was provided in {{cite:f62e0a909a685856e783a3bcaeeb2ef09f71565c}} making use of a helicity parameter that allows for outward jet motion that is not exactly in a straight line, as noted in {{cite:7d0791... | d | dc0ecabc94d8b548bf410b986915bc1a |
The spectacular variation observed in the DLCs of SDSS J163401.94{{formula:389bb28a-c926-4089-953c-1ce1c324aa9d}} 480940.2 is remarkable, as such a variation is genuinely unexpected for the non-jetted-RLNLSy1s. However, exceptional variation, and deduced minutes like flux doubling time support the jet based origin and ... | r | b5d651d785a1486d6e342cb2cbc4739a |
First let us compare with {{cite:09713f46bfdc8da67226658474d840992accd7ad}}. For simplicity let us assume there are no additional controls {{formula:832cd7eb-efe9-4a16-ba32-5b3d4b24afd6}} . {{cite:09713f46bfdc8da67226658474d840992accd7ad}} assume the existence of a function called a `confounding bridge' which then play... | d | cd3918f60e825ffae8ad20322d483a0c |
Local alignment score based on Smith-Waterman algorithm {{cite:0e313ddaf7e6792a14bc01464de425b5b629a890}}.
| m | 5828d3fbf8e4d0808eabcfa2ce09eeff |
The coupon collector's problem is an old problem of probability theory which in its simplest form dates back to de Moivre, Laplace and Euler, see {{cite:4b6ede7cd9d5a2d69f35436fa27f679f4784bc6a}}, {{cite:d16207c01682738d9eadaf90498af07d518f3649}} and {{cite:cf40acf2e9a16cb1ff7685f5d646f3716126a9ed}}. Whereas de Moivre ... | i | 224e7b6108306b3174618bb01141e4c7 |
Performance on some detection datasets (e.g., rfcx, gibbons) was lower than others. These datasets are challenging due to the sparsity of the vocalizations in the training data. AVES was the only model that performed competitively across all datasets. We expect modern regularization and data augmentation techniques, su... | r | 5e9cc98528f7b4fd0ec624b17bd596dc |
Our results provide a base for further research in many directions. First, all of our analysis is in the limit of large sample sizes. With a small number of simulations, higher order terms ignored with the delta method as well as the difference between a {{formula:3c565334-f26e-497c-82ce-9e0a99b1853f}} -divergence and ... | d | 57f058843616f88481f3d27dc2909753 |
From Andrzejak et al. {{cite:679219f58a4afe9b829b1e90079bfe77e3a88236}}, 10 Participants (5 Healthy and 5 Epileptic Patients)
| d | 2738538329bb83c72ba3845b34fc442f |
The forward selection process does not necessarily evaluate the same models when fit multiple times. This is because the selection processes explored different combinations of change points and thus there arose inconsistencies in the chosen models from each repetition. This highlights the need for the change point sele... | d | 095c5cd16f4ee2d4f1b43c35f2e77e66 |
The intrinsic magnitudes of nearby Type Ia supernovae (SNe Ia) to which distances are known independently are characterised by a large scatter. However by exploiting the empirical (wavelength-dependent) correlation between the intrinsic supernova magnitude and the timescale of the luminosity decline {{cite:85668e904a8b... | i | 273daa5c899644fa3cbde70d48d2faab |
To demonstrate the effectiveness of the training, AuthNet model is benchmarked against a two-level system - several state-of-the-art models in face recognition combined with lip reading. Since FaceNet {{cite:360e915a07bd6cb9d31084a957960acbc3bbcb05}} is the current best-performing face recognition model reporting an ac... | r | 9c230002ae589e8d10b0c54f2fe813e8 |
We have presented a theoretical study of diffusion mediated reactions in an evanescent CTRW on a one dimensional lattice. A finite trapping rate is assumed when a walker reaches the trap position (imperfect trap model in Refs. {{cite:3cc870db936fdbfbe6db8772f47587511ebe2248}}, {{cite:bde6684f7ac61230abf7c3c833776d7b497... | d | c6884e7b591218c0cad5d3cfba2f476d |
We have studied the properties of collection of polar self-propelled particles moving on a two dimensional rectangular channel along an order-disorder-order interface with periodic boundary condition in both directions. The interaction among the particles is taken as Vicsek type viz; particles move with constant speed ... | d | bc5c8dc067139e3db432fc5e56290cc5 |
In this paper, we propose a lightweight CNN for HAR using Lego filters. To the best of our knowledge, building resource constrained deep networks suitable for HAR has never been explored, and this paper is the first try to develop lightweight CNN for HAR on ubiquitous and wearable computing area. Compared with standard... | i | 0cb84748870c5487597cda2f6a85a086 |
where {{formula:1f2eccf3-c2bf-45aa-953b-b1f48476702d}} GeV is the Planck mass.
The density perturbations from which PBHs formed would have arisen only after the
end of inflation {{cite:067f5f0f6f94bc9d40eda3873f57002d1fb3c04e}}, {{cite:2cd310bfb1cf9ba434b3b9ccd32a1efce6ea7edd}}, {{cite:f87290c44df75c99025fa262299f6483... | i | ae76b205aa1b9a2e410e9a4fc5e334d3 |
The same argument works for both case (i) and case (ii): we will assume we are in case (ii).
Clearly, {{formula:2cd26c8a-fb2f-4066-ba5e-fc95278f1124}} is a closed subgroup of
{{formula:a9ccabd5-4307-4b6f-b375-1f2fbd1dc1c8}} .
Therefore, it is sufficient to show that if {{formula:600760a6-164a-41bd-8ad2-d5ec0001c829}} ... | r | 74a0bd54020e5d8adda19ec0e64f985d |
The existence of minimal length {{cite:02b5b7a13d1361f41ca1c41b73b32bb626f5be82}} has been predicted by various theoretical models such as string theory {{cite:7456e16e862d28412936ffd2076469e942827a8e}} and Doubly Special Relativity (DSR) {{cite:a99ab481cc3679d05e814571e4241c851c83e1bc}}. The presence of this minimal l... | i | 4629c645d412d6cf28a2a735517ae288 |
Previous studies using gamma-ray data of our Galaxy have performed similar analyses. Some studies claim that the gamma-ray flux profile traces dark matter distribution while other studies claim that the gamma-ray flux profile traces stellar distribution. Therefore, it is still a controversial issue. However, the resolu... | d | e0a63a473ec449c48ccafae64affa403 |
The Bound in Context.
Figure REF plots the competitive ratio bounds, for a fixed online cache size {{formula:135ec236-a9d3-4cbe-8ffa-8b52860e0062}} and block size {{formula:0b0216d4-a900-4f05-8770-1fca3e1e2b7d}} . Our resulting lower bound is much greater than
the Sleator-Tarjan {{cite:1fd0bdbaa0fd4e80857a1bb13c929eb... | d | dc735778f30352374c86c4b7d02243cf |
In IE and sequence labelling tasks, the model will receive a text sequence, and label each token to one of the various classes. A sequence model can be simply added on top of BERT by connecting BERT's hidden-states output with a token classifier (Figure REF ).
It is trainable by feeding the output representation of eac... | m | ff9e7f866f7acb20c2435b4215d1cfd3 |
Note that there exists a corresponding classical protocol in the SMP model with shared randomness, with a similar complexity. One way to see this is that the quantum protocol is ultimately based on the use of the swap test to approximately compute the inner product between unit vectors, for which there is an efficient ... | r | 8a546c03a5c0497a5c82fd98fbcc1800 |
Experimental details. For our experiments, we compare the icsn to several baselines including the unsupervisedly-trained {{formula:86350b08-642f-4ab7-a4f0-89f6031a6c1c}} -VAE {{cite:d9976589d95be1506e939bf357267ed7e258d8f9}} and Ada-VAE by Locatello et al.. {{cite:71830a330ec2f9f2d1245595b135882647acd78b}}, using the a... | r | 6156522cfca3873a59247fb77bd93742 |
Under the setting of transfer learning, FixMatch {{cite:2ca536c63aca4a3c41e22a045ea7f8647c7e9292}} tends to be the dominant technique with in-distribution data. However, it applies pseudo-labeling as the key component and is doomed to suffer performance degradation when faced with out-of-distribution unlabeled data. In... | m | 95474c167d03399f6ba678173d1587c5 |
The proposed TCL is evaluated on five image datasets and two text datasets. For image clustering, we take 21 representative state-of-the-art approaches for comparisons, including k-means {{cite:d2cf61f6ca1269eff9f18ee70f05a9b5bc5627e7}}, SC {{cite:fe4262a91141da7e037d8126800c81f9bb9cecbf}}, AC {{cite:be9bbddb7c66d17cde... | m | 9c70cb09611bf2bedfa380888a4ee39d |
Reinforcement Learning (RL) {{cite:fd27b0ccfba521475da21d8cadd93b0b885f1b92}} has demonstrated great potential in solving complex decision-making tasks {{cite:3085bca6c725341e1bbff798479a79f5e9f55886}}, including but not limited to video games {{cite:1be1faa1b0f6de29513c6c86ebf8cc3ebe72d39f}}, chess {{cite:72a9586e0a2c... | i | cb1d29091fb091a432f3b87d759ee5ce |
Given the SNE {{formula:6352cef2-a0fe-4d63-a80b-e2b4e5566d3b}} , select an initial point {{formula:536773c8-0313-436e-b67c-8dc594c0e828}} and a maximum number of iterations {{formula:06fb6b15-2ef8-4389-aa51-44dec1a47fc2}} .
For {{formula:9fd430cc-272b-44dd-b996-7f755039264b}} , do:
Select a forcing term tolerance {... | m | e27958750ae3accf0efdc872bba35f7e |
For the circumstellar disk mass we considered an average value of 0.01 {{formula:01b4ef6c-2f94-4e30-a052-f3a5f943664d}} , and the radial extent was between 20-120 AU, similar to a transitional disk with an inner cavity. While the circumplanetary disk mass linearly scales with the circumstellar disk mass {{cite:4b7e2c35... | d | 6fefd2ac4db7ad52b0179bc0f4974daf |
Computation Time Scaling: Fig.REF shows how the computation time of our MPC scales with the number of obstacles. The linear trend observed validates the remarks made in Section REF . To recall, with an increase in the number of obstacles, only the computation complexity of constructing the cost function in (REF ) chan... | r | bf1d1c4e41b61c62b28903093dd52366 |
Figure REF illustrates the workflow of our OntoGCN neural model. Each gene contributes a node to a knowledge graph
where edges represent the similarity between the genes.
We create edges that connect each gene to its K-nearest neighbours in the ontology embedding space according to the cosine distance. We use DL2vec o... | m | d3035afd361728cd01d8e5d21ae74282 |
In particular, the formula (see {{cite:0ec15f58139c225faeb54a1d3d96d7fa4c3fb552}}):
{{formula:257f24c5-874f-49bc-8214-23b878f5861a}} can be written in the following form
{{formula:d7002a33-74f2-4e61-b649-ea210fc1fa0c}}
| r | cb97fb6faeb76493c082055fce5b8806 |
The statistical analysis of large, complex, and high-dimensional data has become a significant challenging problem. Due to the rapid development of complex, performant technologies, data can now be collected on a large scale, resulting in high-dimensional and high-frequency data, sometimes necessitating high-performanc... | i | 735ae22fb85dc853de87afda055d5961 |
Now we must show that {{formula:82b60912-c4ee-440d-ae3a-9f43ae4fb16e}} . By {{cite:f8d843e14c51201f8c2ea14e6c59ccf7aa829e1d}}, since {{formula:7e4accee-39fc-4690-951d-ed62c87a5d4b}} is a topologically graded {{formula:4ea50e6c-4394-445b-83d9-0bea335925d6}} -algebra, with grading {{formula:14d640f7-0aed-4cc7-b2db-16a58... | r | 58030674772ce7354709cf572d4daf40 |
Dynamic mode decomposition (DMD) is a more recent ROM approach {{cite:3dbc6cab2553f7fbeef2bc19df2e205c50c4ab45}}, {{cite:81528aec73d46b16de143c90bd61f4eb8d170193}}. DMD is non-intrusive (equation-free) and provides a simple linear dynamical system model ({{formula:5f786c14-ed3b-4bce-8109-590c8e444aa2}} ), which could b... | i | f2eefa7dfe7c814a3ec30ad0646df732 |
Quantum process tomography
We fully characterize the process of W.-Z. phase acquisition using quantum process tomography {{cite:d87ce72e2a70514bfb80b6fafd6fce42b4037a0e}}, {{cite:7b9aebf7ea722fb64a2c0cd5a84f52daf9de10ff}} within the ground DS.
An arbitrary transformation (operation) on a quantum system with initial den... | r | 050abe0704d210d22dbcbabe8ddfc365 |
Previous works that employ autoencoders for style transfer tasks {{cite:1a37744e3b3eb6f9eacb8feeb1298e0c9dd67df0}}, {{cite:c7f4452cd9a6069caada31742f90c39862472c7c}}, {{cite:6a99a1839fc1b8c3be2c38ab094230fdebc0b6eb}} often suggested adding adversarial losses {{cite:f63010298665d5ba9ffe42196274ff99445696bd}} on the late... | m | 84570bbc6f8c2a84bbeb2e3611e2a005 |
In COVID-19 detection, the networks' performance decreased similarly for both weight decay and spectral decoupling (Figure REF ), when training the networks on the combined BIMCV{{formula:169ff7f6-97a6-419b-bedb-288aca7cdabc}} and PadChest dataset. Radiographs contain systematic differences between data repositories a... | d | 887e453b8387827f7af9bf68e7099aae |
Implementation Details. We build our method on top of the GFLV1 {{cite:f377b1f33464fa3445b40c87d55ffcbc7438d9b9}} detector using their official implementations. The teacher and student detectors defined in our experiments are standard GFLV1 architectures. For the GFLV1 detector, ResNet-50 is used as its backbone, FPN {... | d | 0c50ed8903bfb2edd3a111ff37763c5d |
Since few existing methods currently have the same capability and the same experimental setting as ours, we modify the existing competing methods to enable a fair comparison.
We choose state-of-the-art deep learning models for 3D point clouds as benchmark methods, including PointNet {{cite:11105f11876338fd3ce3bcbd88c79... | m | ea487bc8bdf02dd8b891a094dbfddf91 |
We also tested the impact of our network augmentations against adversarial attacks; here again, we showed that PC helps to improve the robustness of the networks. So far, the most promising strategy for achieving robustness has been adversarial training, whereby adversarial datapoints are added to the training dataset.... | d | ce2b1cd5bc45cf2d8c4d8462f489d350 |
We refer the reader to Refs. {{cite:b0f0846037c3ea52da39a001ded77d9ad99b463e}}, {{cite:56e6517f063cc5b11f0c4d5a14f4a61ffa505497}} for details on the interpolating operators,
the parity and spin projections, the measurements, and exploratory studies of the spectrum.
One observation we immediately made from our prelimina... | r | 2e41b644947f376d2aaffe75e0107e68 |
Figure 3 shows the space-time evolutions of the atomic density integrated along the transverse direction at relatively small
repulsive potential strengthes, and other parameters are the same as the experimental parameters.
Fig. 3(a) shows the case of a very small perturbation {{formula:3e804e11-13ab-43e1-a7af-e899044ac... | r | c1d62726b9b391ac15b3c11e6e685ebb |
The availability of vast amounts of data and the advent of deep neural network models have accelerated the adoption of AI systems in the real world, owing to their significant success in natural language processing, computer vision, and other data-intensive tasks. However, despite the advances in performance across the... | m | 4960c62c0f76d672a41b4f79b47fd55d |
Scene-specific APRs are not designed to solve domain invariance by nature, thus they lack the ability to generalize well to different domains of the same scene. Despite being trained with multiple scene data, the MSPN and MS-Transformer also do not encourage domain invariance to different domains. Unlike these approach... | i | c5b7a685829ae6db681712e24da1c7e0 |
The run-times of our techniques are broadly similar to those described by Anders and Briegel {{cite:bb284b5cc3c86436dc38121ecf8959940dd12442}}, who describe stabiliser states as the image of graph states {{formula:a7ab34f2-59f6-450c-9e0b-f49e0ec5e5f4}} {{cite:f8922fcad2cf25375ebd4c56376c7e96d432ceeb}}, {{cite:2c7af1e9... | d | 9394ce8f1e8673f96d64b00251c7e748 |
We show that estimating the state-action value ({{formula:e9822df8-347d-4809-b4d5-d6bbabc29a59}} ) by minimizing the mean squared Bellman error leads to a regression problem with confounding, the inputs and output noise being correlated. We provide a re-intepretation of the popular strategy consisting of fixing the ta... | i | 8203e23c1397fdc064ada98a28452a80 |
Motivated by this, we extend the theory of noncommutative differential forms by Connes {{cite:4547a729cb4b2e76ed4956b85a7ec5091a901554}}, Cuntz-Quillen {{cite:6e427be1dba0def520f7f9a17002199b33f65dd3}}, Ginzburg {{cite:0cba4930ae8b1e6d706b321f6d31a7ecfebc2d9c}} to the context of near-rings. The main idea is that, every... | i | ed8c68bb2886c95e04a6def66af41ba4 |
The straightforward solution to reduce the GPU memory is building fewer 3D cost volume. Fast-MVSNet {{cite:d4ed859d69d50e6b0204c68aae664b579f44ff8e}} only calculates the 3D cost volume on a sparse depth map and propagates the sparse depth map into a dense depth map. CasMVSNet {{cite:d9dee1c5be67a850c925990d3ce7bffcefec... | m | 5c8cdc132b35bc7fdffaac531f933b90 |
Sun et al.{{cite:f4508fe0877aaf9eb80b2a050c2621344eadd6a5}} propose an end-to-end integral regression model to extract 3D poses from 2D heat maps. Madadi et al.{{cite:8450a641d00bd9b3e23ad464cf316de7e9220678}} use CNN-based 3D joint predictions as an intermediate representation to regress SMPL pose and shape parameters... | m | 9eab3aa568dc548e5cf0398d5498bf14 |
We summarize some basic facts about {{formula:e06666d0-b41b-4bd5-a1a6-636cd1cda3bb}} -adic analysis that will be used in
this paper. For a complete exposition, we refer the reader to {{cite:6f855327629fd3a1e19bbb2e3bf6882e07a66d56}},
{{cite:172b4c9febc1fd47beafb9adb77bcb3e05ae6c1b}}.
| r | 81e7127b09021215a04aa1205af951c0 |
where {{formula:68d36313-a14a-444e-a0c0-a233c85293e5}} denotes the Euler number of {{formula:3ad2f5ef-4131-429c-9c7f-f115d81ec0d8}} and {{formula:11167e5d-368a-4bf2-9073-c038ea981666}} the degree of the divisor {{formula:957e531d-9503-439e-b050-898aa9fdf08e}} . If {{formula:5d5e6282-e252-4d21-93a9-ce39cbeaff18}} , t... | i | 9f376b515b8968759f5915ec7d2470fb |
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