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What might be driving the anomaly encoding patterns indicated by our results? Explicit syntactic training does not appear to be necessary.
GenSen is the only model that includes an explicit syntactic component in its training (constituency parsing), which could help to explain that model's comparatively strong performa... | d | 3748217eb63a43ead32feba72818cfd8 |
The Wronskian method presented here is similar to the well-known continued fraction method by Leaver {{cite:e5dd0ca7a4bc964baf83c20bf33c0f4bb0e8e08b}}. We do not explain Leaver's method in detail because it is discussed in many places.
An advantage of the Wronskian method is that it still works even when we do not have... | m | cb0dd3ca23a9ff7502babbc125224833 |
The main advantage of stabilizing training using rewinding {{cite:295034d90a64925642f968a310fb8d93fff6a90b}} is that it does not require changing any hyperparameters. Our large-batch experiments modified only the batch size and correspondingly are not competitive with rewinding. Although this might be fixable by an exp... | d | 9f75cef695e9756a746ac33e0cacb720 |
The efficacy of the proposed approach is verified on the three-phase unbalanced IEEE 37 bus {{cite:8ee5817f82f2cd00a115c6ceca4c9d0fe6e9d3a1}}, and IEEE 123 bus test system {{cite:0d2bae92fc54fa4100d9a35f8ae43275f952a805}}. An aggregated 24-hr load profile at the primary nodes consists of a mixture of load profiles, i.e... | r | 368ceed510506befd85966589b8b8f09 |
We propose a new conditional GAN architecture, which is capable of directly predicting a waveform from intermediate features. In particular, we adapt HiFi GAN {{cite:49f840f625c4080f39729c7d7710d726b3387ef0}} vocoder for a general decoding task.
We combine ideas from ASR-based content encoding with a GAN generation a... | i | a766b694edf9e8e9ec91c9af290af887 |
Typical Transformer. The typical transformer {{cite:dbc6230f454e9e643d886e3bd4e42ca26f133bf2}} here uses the ST-encoder and a typical decoder structure, with an additional future mask added to the attention function of encoding stage.
| m | e0cafd7b6b1f0444f2478e587a0e94c8 |
In so far as we establish low-barrier permuted interpolation for further scenarios, these results provide support for the conjecture of {{cite:2ec148bf93ae5b9336c266a40e3eb6e8f80f50cd}}. On the other hand, to practically do so we needed to rescale the preactivations of the interpolated network, moving us out of the rea... | d | e0915a06c21ee15098e85f9dd5e709b3 |
in the {{formula:699a0bd7-8347-467d-bc16-31383e63e5a3}} -smooth case. If {{formula:57a22a76-802e-4841-a32a-8f35b3bb0c7c}} is of class {{formula:d5fdc86e-a344-4fc3-9e18-e3b363915d60}} around {{formula:1386aaab-a60b-49ea-b50d-9a8738db09b7}} , then the computation of {{formula:abd02ca9-1715-4f2f-ba01-e2ee871540b7}} red... | d | 35746839a6d2e4f58f16fd308c4286f2 |
The fairness concept we use in this paper is the intensively studied and well-established maximin share fairness. The maximin fair share (MMS) of an agent is the best she can guarantee if she is allowed to partition items into {{formula:ce811df7-bb36-4fd4-9f3c-0b6d9b0e9dc5}} bundles but then receives the least preferr... | i | 5fb6176891784173e5de419c67b28a8d |
In the present study we identify interesting links between quantum groups {{cite:9377e2a917e4cb640f47e07d51bf765a14e7b0e3}}, {{cite:dd260d3ec10d5f82ca9a10cc568c5b1f1e7de234}}, {{cite:8be025cc045acae2cadc614ede0b71b4e705e417}}, and tridendriform {{cite:156e76db96c333c75e97c932fa56d9e3fdc8053b}}, {{cite:f3512df12141c8a68... | i | 212a3d9f07e624b402be2253a96db2bb |
In this paper, we will use a version of delta method due to Duke, Friedlander and Iwaniec. More specifically we will use the expansion {{formula:5e6b5b7f-c06f-4cdd-8f5d-a05e2606b6e6}} given in Chapter 20 of {{cite:25fa08545571e51879c66ff4599370ac89919b1d}}. Let {{formula:4080d848-9eab-4b93-9112-2f979277c5ae}} be defi... | m | 890930b20894781735113ad872dac5d3 |
where {{formula:56aaa51e-00ec-42ec-bf01-f88188952261}} . Since parton's suppression {{cite:6538761357d0340ff9a2331003b2bebfdeb7d0ed}} is defined as {{cite:73d90d792a06e74110b36d6c3ecb64c343553993}} {{formula:90ba20df-2117-4c5c-b8b0-c06b379a4daf}} , we finally obtain:
{{formula:2085c151-1c85-4219-a078-f1ee18f96c23}}
| r | 1d6b822273b3861b8f9bead8c26908b9 |
We also apply our method to a version of the well-known Berkeley child growth
study data {{cite:9ccc849a3d3744d4fa4c79729d789ebd5064bdd9}}.
The data contain annual measurements of the body heights of 39 boys and
54 girls from ages 1 to 18.
The focus lies on the first derivatives of the data,
i.e., the speed of growth i... | i | 23ded89a76d5889535faaf059e616ba4 |
We observed single-mode operation from both InP-NBs and InGaAsP-NBs. Figure REF shows the lasing spectra of the two InP-NBs at room temperature. Single-lasing peaks were observed at 1534 and 1594 nm. From the SEM image, the structural parameters were estimated to be: {{formula:05c0f928-842a-4d07-9e1d-5f7f93bae104}} =... | d | 390352614d77df79e63d5022663e0627 |
where {{formula:eddd1fd3-fceb-44e4-a1be-4381f60ee365}} is the cosine similarity to measure the similarity between two representation vectors, and {{formula:c5ba55a0-6053-4836-8e59-c4121de3092f}} denotes the temperature scaling factor as defined in {{cite:3770a7d659561ccc39b1f96367795b38383c8eea}}.
| m | e376e0072c25706be261f96cbe767b21 |
{{cite:49d48df0b88db4d195979d74c81286c5a0a15e98}}, where also rates of convergence have been established.
| i | de2e3306345f9a519036c61247ee0b80 |
Recently, there is a growing focus on hierarchical approaches in reinforcement learning employing the options framework {{cite:b44b4801fbc1ecc11f16b15d730d4535359c5719}}, {{cite:f100d993994e95691e28f2baca0482622ef7a808}}, {{cite:8c2bcffad75e189bb67c785c81c77dc097086d93}}.
Combining planning with RL has been studied bef... | i | 7ad55d261b3efd66fded36eeb87ce916 |
Quantum dots (QDs) in graphene
are very small particles with unique electronic and optical properties
{{cite:4bb09a162e086f2dc1be247c392fbe4cefc1e8c7}}, {{cite:c15b4b9baf718846cd1cf81a2b1ff276fd060753}}, {{cite:804a1fc050a924bcb243476a9a148e3fd6dc1fda}}, {{cite:979e64a6131cf4449fa124275f713a7b1e8f355e}}, {{cite:81b5123... | i | 20b0b331296ccb54e0887ba66dd4e97b |
NORB environment: We use the NORB data set {{cite:e97f03dd590b4828c1bc7395b08d9a4b888fccbe}} for our experiments. This data set consists of images of objects
from different viewpoints and we create viewpoint-matching tasks from the data set.
| m | 85a6d73b812a4ff8c79b6f71acc429f8 |
In the past decades, the gas-kinetic schemes (GKS) have been
developed systematically based on the Bhatnagar-Gross-Krook (BGK)
model {{cite:d6ce89a3af5251451a9894cbf3cfc222d19ece21}}, {{cite:01c3339b239076b953df546473f5b3c0135eb2d7}} in the finite volume framework, and applied
successfully from low speed flows to hyper... | i | 8ec4abcddea77be301697b60b91b2b15 |
When integrating machine learning into numerical algorithms, it can be beneficial to resort to simulation methods whose mathematical structure is easily compatible with neural networks. The present work shows that a highly suitable CFD approach for this purpose is the lattice Boltzmann method {{cite:b6ee4d3defd8c155b3d... | i | 8570c644e59d08df8bef8fe1c6d0955c |
The QAS resembles a simple positive mining strategy, which is intuitively reasonable that there should be more severe punishment for pseudo labels with higher confidence. Moreover, compared with semi-supervised and supervised tasks that focus on simple/hard negative samples {{cite:964636909b28e56e4170cbc79670afde0c96b0... | m | fa3184b105e162d4d63773081225570c |
We also quantitatively compare our DiFa with competing methods {{cite:71b00ef33dc13aa099bc076d63f958297082bf54}}, {{cite:fd9c12ec7cd158c94e6448e56b3a8f03091e121d}}, {{cite:28abd7896c9f13a01326d8a1592acd95650c4866}} under six settings, i.e., {{formula:935d8f03-3f62-4cd6-9dcc-82281d535e05}} and {{formula:a0847cde-1bc8-4... | r | 88a6478e9f71cdfceba196d835ed8deb |
In HAO, we expose a large design space in both hardware and algorithm configurations to accelerate DNNs.
To efficiently navigate the search space, we first apply integer programming to prune the hardware configuration space by minimizing the latency subject to a set of hardware resource constraints. We then narrow the ... | m | e6256654fdfc9c0872437d6354ad5268 |
The {{formula:a5906644-d253-4e6a-abc5-76152af21684}} decay distributions depend upon hadronic form-factors. The determination of these form-factors can be calculated with the HQET techniques which are presently known at {{formula:279856b5-9808-4ee6-afe2-0e11c8d4f065}} . In this work we use the HQET form factors in the... | m | 5f556efc14eddc0f5aedb8dff0beb52c |
Although significant progress has been made in one-step {{cite:2ce632fe83282e1b22d6c975984a9c91fa80f32d}}, {{cite:c57bb6336116f8cc6444312c3fe0c518145fc4f3}}, {{cite:3a5bf8f03b5d5b675288d95137071f7fcd1de1d3}}, {{cite:989ddebb7a42fa1b867ffd4da10fc33e5b855fa9}}, {{cite:a4c3f57e1326ce9faa4a49037b99c5b769ed8b29}}, {{cite:77... | i | e7c2ba9d0756dffb5f711aba2f14f74b |
We first trained selected models with ASWL on MNIST, which contains 10 different handwriting digits with 60,000 training images and 10,000 testing images. Each of the models was trained with various pruning factors of 1, 1.5, and 2 for 100 epochs by the Adam optimizer at a learning rate of 0.001 and 0.98 decay for each... | r | cc1f79792c388f157739e23d16ac0d12 |
The average number of semantic categories per scene in the UC Merced and DeepGlobe Landuse Classification datasets used in this paper is 3.39 and 2.51 respectively as depicted by figure REF . This implies that a given scene from the UCM dataset with a given image-level label will have 3 or more different pixel-level la... | m | e4333ed5c54724b551072cc4c324477a |
Now {{formula:fbee1da2-b4f3-4ab6-ad30-6650bf759cf0}} is a Chevalley group of Lie type {{formula:00513773-b034-4dd1-b2b6-d932e7f49b95}} . By {{cite:e40bae7a653afd4292bcb333f8b8ecf928a04723}}, {{cite:e288e554d75fd8a9131afd22351c805d7212edbc}}, {{cite:028309a9197c010fce139b38d4e1895a68c13141}} it has a natural extension,... | i | 096a7fb271f0aba2cab87001f3d6a082 |
From the perspective of theoretical computer science,
there has been a lot of progress towards understanding the information theoretic and computational limits of solving the SBM problem since the 1980s {{cite:9d70c0ba8b3f00ecfd38c98f32dc4105206bd6e0}}, {{cite:bff25f17e052abf4b48cd7a4b69dfa70e12208c7}}, {{cite:8619debb... | i | f9c405386ca082b852ec6443daf6e037 |
Our input data for training our RNN were aggregated financial transactions of approximately 26,000 customers, while the output data were
their personality traits {{cite:6b7acd743b0c69866db8fbd9b4be0dd304079acd}}. The transactions were aggregated annually across 97 transaction classes, such as groceries, transport, leis... | m | 80491f271ae6b69dd8d29941c28f1286 |
As discussed in Fig. REF , we believe that the implicit relational knowledge of the edge is related to the low and high-frequency information of the features of the paired nodes. In other words, the representation of the edge strongly depends on different frequency information between the connected nodes. GCN-based agg... | d | 161ed500b1dce0a0f8c6b4f3c3398f92 |
In this section, extensive experiments are conducted to verify the effectiveness of the proposed approach. We first design comparison experiments with state-of-the-art (SOTA) KD methods (e.g., AT {{cite:78d9dfad25eb8409375f47698536df7aa359cfb8}}, SP {{cite:ed951438b91fb5d07a208ee133f2c4465e07aa69}} and RKD {{cite:b3067... | r | 149e96d0fd457e797c27079b543618e3 |
BQCD is a Hybrid Monte Carlo program for simulating lattice QCD with
dynamical Wilson fermions. It was first published at Lattice 2010
{{cite:e29e8686cc8b71d0c2d1bc01bf8bdcd4d3469386}} and has been used by several groups: the
QCDSF-UKQCD collaboration {{cite:2fda2e4cb6e5228b39c92403cc3fa8eb0ca02a08}}, {{cite:b076bf1a81... | i | 5a96a58613774d701595fb9ce6df48cd |
In this paper, we extend this result to include the full Kerr-NUT-(A)dS class of spacetimes {{cite:74ed039a339991f3941f0f2cb177ef70c0807f62}}. Our extension is possible because these spacetimes all possess a series of Killing objects (vectors and higher tensors), all related to the `principal tensor' of the spacetime (... | i | dea644cfe1ea35759ca94ef48f0a9c6a |
If {{formula:98184e34-5a65-48bb-99d8-990b6523bfde}} , the Hankel matrix {{formula:94609687-ae52-49f0-8f1a-a216b8168263}} is of rank {{formula:b9c8b6da-8b62-4a94-828e-33abb6f8fb98}} regardless of {{formula:7e147c59-4868-4905-bbed-f92b4e4e10cd}} {{cite:363259c4dbe42408b37b718e3ab2056166c06cc6}}. Specifically, we will... | i | 13c704082b3dd680cb9625541cb6589d |
In addition to the detection performance, two essential considerations are involved in implementing a hate speech detection model–bias and explainability. Hate speech should not be judged by any specific word but by the context in which the word is used. Even if any word generally considered vicious does not exist in a... | i | fb34f4d74731166814d22234995379fc |
It is interesting to note that a linear VAE also reduces into a Gaussian, as it is equivalent to probabilistic PCA{{cite:f3c89cb92545b17f1add2b409f7e118833702eb1}}.
On the other hand, a linear autoencoder is equivalent to PCA {{cite:ebacef4fdce24e29a7c9766ddd82cea614e80be3}}, which is not a generative model.
| d | 1cf710239aabe60840494f5103449e51 |
Beyond representation learning, masked image modeling is a classical computer vision problem, named image inpainting. This problem has been extensively studied in computer vision for a long time {{cite:8baecdf48375ff5d29a8c9ccae979ceb4d6d10e6}}, {{cite:1091b6ba0f4008ab39bc0ce0a2f44188cd9927cd}}, {{cite:57f61d29a28217dc... | m | c0b25ffffd6d1ec9608e7f235ab7b8f5 |
The wide application of deep learning makes the design of the neural network structure an important factor affecting the model performance. Neural architecture search (NAS){{cite:08abfcbd101c273ed36882796f4dfa7698de5092}} is a technology for automatically designing efficient neural network structures. In 2020, An et at... | m | 9152029f685eb3a5c38f62fe148a166d |
The limits from the monojet analysis (blue curves) depend mainly on the overall magnitude of the interaction, which have been kept fixed in the two BM scenarios in order to be directly comparable with the experimental benchmark {{cite:e6e17512d6e5f0cfc5fdfde2e6a80ca3427d1828}}, {{cite:032144b164e34ba77747542ba7cc8388c5... | r | 3030de74ba27fde1a882763f7f836e57 |
In this paper, we introduced several expected generalization error bounds based on the Wasserstein distance.
In particular, we introduced full-dataset, single-letter, and random-subset bounds on both the standard and the randomized-subsample settings.
We showed how, when the loss function is bounded, the presented boun... | d | 202db1e17403cce47b9a91e648b25626 |
We demonstrated a new approach for denoising CT projections that supports training the model in the self-supervised mode and allows to denoise sequences of images depending only on the features extracted from these sequences. We compared our Noise2NoiseTD model to the state-of-the-art self-supervised denoising model {{... | d | 89037ba1100cdec564b8f96d2c033654 |
Consequently, GUG reduces to a version of Nesterov's accelerated gradient method (see, e.g., {{cite:18a2b03a1384328e0133b6d6553cebf03d2fad82}}).
For our study, we will focus on a projection-free implementation of the Approx-Subproblem procedure, namely the conditional gradient method (CGM) procedure, described in Algor... | m | cbce5bb8a46e55f8e99519beb2d32467 |
Our results demonstrate how the rich phenomenology at the interface between topological order and Anderson localisation in disorder-free systems arises also in higher dimensional systems. Whereas the localisation properties are different, the notable effects on the relaxation and transport properties underpinned by the... | d | efe4f3cd3209401f9ef6ed6339975c8f |
Although the Q-function with motivation (equation REF ) is similar to the one in goal-conditioned RL {{cite:9f8f5547101f5c52d0ab990e26c2e6c221d5d753}}, {{cite:a49fe4ff78d546faf0ede532df34a8aa18720885}}, the underlying learning dynamics is different. Motivated behavior pursues multiple distributed sources of dynamic rew... | r | fc61049fbed7f67d7c39b73655809968 |
To understand the rerankers better, we investigate the effect of different proposal models, different language models, and various numbers of candidates in the {{formula:43d0c559-55ad-4260-a78b-311c0e51c43a}} -best list. Table REF and Figure REF show that better proposal models and bigger {{formula:296f1045-add3-44ed... | r | 11ee9f0a8ea0bdd0ffe77a96b6731be2 |
Deep neural networks are models with huge amounts of parameters, works like the lottery ticket hypothesis {{cite:f3b30523d06f9bd87c3fa16b1dd723737f9b5b7c}} suggest that these DNNs are heavily overparameterized. Thus it is possible to kill up to 80% of the neurons without losing too much performance. MIMO builds on this... | d | 2dac918d92dcf99385f13184b7cebf7a |
In this work, we proposed an end-to-end pipeline that recovers the scene geometry from an input stereopair using a fixed number of semi-transparent layers.
Despite using fewer layers (4 against 32 for the baseline StereoMag model), our approach demonstrated superior quality in terms of commonly used metrics for the nov... | d | d288337b419796c6230975e21d05738d |
The results with ResNets trained on CIFAR-10 and CIFAR-100 are displayed on Figure REF . The results for the other two experiments are postponed to Section REF of the Supplementary.
In Figure REF , we display the evolution of the values of the loss function and of the test accuracy during the training phase. We observ... | m | 90b0af4b7a1cfd07ff8c59a9c04b54ea |
Recent studies have repeatedly shown similarities between brain activity and pretrained supervised neural networks (e.g. {{cite:89640019a98895fe4ba6792e85fab0ee1dd160f0}}, {{cite:f243949806155a6b4fbb77149cbdc1147d4a3288}}, {{cite:dd12f0fee6333777af55d40a44c23d3fa5defe3b}}, {{cite:df46d5de5f45e66d55f4f018cc815081e4be31d... | d | 433f2b7901bcf60ca6ae88f29ef72c98 |
Figure REF shows the comparison of forward back-to-back di-{{formula:5cf491d9-68c7-4ee6-90db-0de0628f385b}} correlation function in MinBias {{formula:4ea1ac2b-ec98-4fdc-901f-4dfec06f3b16}} +{{formula:ff8c6578-a1c4-4153-95d0-8f3c75ce0952}} , {{formula:47f269f8-e700-4514-a035-c72bd19876ad}} Al and {{formula:90b8b023-ef... | r | a2e45b5fdded8cf85f4e1230fe5145cb |
This subsection reports all experimental results in a graphical way. Figure REF compares the test accuracy achieved by both SGD{{cite:4e44c3aae3627ccf7f063c610dfb269165d17f9d}} and LARS{{cite:1c67ca0ae5286716e5892df9ccb8a4e9e9de107a}} optimizers. From the figure, it is clear that both approaches perform extremely good... | r | 741057db2df388955523ce878dbe9bab |
Cosmic Chronometers: We use the 31 data points on {{formula:f0bb5741-2e74-421d-bb34-ed82302759d7}} , at different redshifts, from {{cite:262dd31e09471952efcb134fbe0d3a0fe8d495ba}}, {{cite:88b15843be8b0759c1e10259cb8414cd2f11b81b}}, {{cite:c63eecc48dc3c29ac3358dae21c34355529bd051}}, {{cite:f3f98644eb65db21091d73a5ee4ea1... | m | e9ea79613b17df0fdb5b491657cd2a8c |
We carry out the integration of the orbits using a high order
Runge-Kutta-Nyström method, discovered by {{cite:087d1f77b16edf7c5ec0e52331bf155e3bb71ece}}. We split the Hamiltonian
into Keplerian and perturbation parts {{cite:be145fd3ebc838e0e930d65a61461014a0318cc7}}
to increase efficiency and avoid spurious precession... | m | 361cc215a730cf298bfbb1b447b7b4d0 |
In this paper, we use the phenomenological (3+1+2) neutrino model with
three active and three sterile neutrinos for description of the excess of electronic
recoil events in the 1 – 7 keV energy region found in the data of the XENON1T experiment {{cite:fb5c57981c5f316917b567931cee342418039124}}. This excess can be natur... | d | 7fc718495053df69ebbada13db4ac5a1 |
Given the flux densities for G2.4{{formula:0bc35e02-9326-4893-a3c4-4a211ced0444}} 1.4 that are available from various
surveys, it has a flat, optically thin thermal radio spectrum at
gigahertz frequencies. The steep, non-thermal integrated radio spectrum
reported by {{cite:7470045944947c13d0de7e0e8f43a3c34ce064af}} for... | d | 0442be0ca113d3ba8b73e693ec0f393b |
We observe that excess risk bounds of the same order for DP-SCO based on noisy SGD and the uniform stability of differential privacy have
been established {{cite:f6f3f10ea8b100fb48c71cb04d29807d1dae3ef3}}. Improving these bounds in DP-SCO required substantial efforts, which was only achieved recently
{{cite:3b84846525d... | m | 8510e5636c3ea63283203cd1dceebaf9 |
For the simulations of the polymer, we employed the replica-exchange (parallel
tempering) Monte Carlo
method {{cite:ad7884c995c2802ece02a05168922045190d3a99}}, {{cite:4006bb9c756cfba0c822dc8b02b75af329ebe986}}, {{cite:c24c1ce2edbaa88ca0a16e7357ebea2cee24e9f1}}, {{cite:f67bc67c46a588c4faa50a40f0a81e4cb4b663dc}}, {{cite:... | m | 1af43740c85452bd876efba98eae85b3 |
We carry out a set of experiments to evaluate our proposed framework ADF. We compare the performance of ADF with eight state-of-the-art machine learning and incremental learning techniques, comprising two non-incremental forest algorithms (namely RF {{cite:78ad9556901b1dc54be26be5f1985ea26c9a957c}} and SysFor {{cite:c8... | r | 2d91463f34b7c348af53e76fadaa91ca |
On the other hand, in nature, the scattering lengths are {{formula:c4bb81a6-3585-4a0c-8e5f-0a0f053a90fe}} fm and
{{formula:0247001e-4809-40f7-99d6-ee6779a1c534}} fm for proton-neutron system {{cite:6f9cfda19fc1c53cb58c3ce09074e837ae78d860}}, {{cite:351213df9fc6edabc1028331efafcad1c920149e}}. Therefore, {{formula:396a... | r | bd3c030e1e4efaaee49d19c17ebba0a3 |
The starting point of the proof is to convert this theorem into a result about moduli spaces of parabolic {{formula:7f5eb736-7379-4da8-acba-e73bde9fa101}} Higgs bundles. The general theory of Higgs bundles corresponding to representations into real Lie groups started with Hitchin's paper {{cite:6bfa3541628006efec67707... | i | 3d597197502e4b6c7546e2e40f0015e1 |
We tested different convolutional neural networks, one of them was the VGG Net, a very deep CNN often used for image recognition {{cite:81ac16774fc8e938746cd609a72cc2c9e3b217ad}}. To execute the VGG Net more efficiently, we used a hybrid approach, in which the ML model was executed on the GPU while the physics model wa... | d | c00e57e4c96a9d49111d9509b298f34e |
Consider the {{formula:f0b8ecb0-e499-413a-abbc-77f1f00c6971}} OPE in the {{formula:2e14242e-b615-412b-b38b-9620f8cf303b}} representation of {{formula:afeb7bcc-fc1e-45bd-b3b8-ba9ff3154dba}} with some spin {{formula:cd9ea0ac-3822-40f6-b43d-7401af782df3}} of the appropriate parity. By definition, only {{formula:fa009f... | m | 54fb566088080fc2b1a3e4b1798284bd |
Our first observation is that in the case that the local variance of the density field is non zero, the expectation value of the luminosity distance becomes dressed by local fluctuations. In the case that the density depends quadratically on the field {{formula:be6ac155-7306-426c-9f80-21d10eeb2f81}} , this variance is ... | d | 2dc8bd836005b535e3c509b4904669f9 |
In this section, we discuss two alternative ways to construct
estimators to be used with the bandit algorithms.
We first consider row averages as an estimator for the bandit
algorithms, and then discuss the connection of bounds we
use on our spectral estimators with those of {{cite:acb6793cfaacc26cda6ec908f613aa17323ef... | m | eb66be313c7f375216f6e2c78efa070f |
A new approach, motivated from cosmology and quantum field
theories on a curved space-time, has been proposed to study the
gravitational interaction: the Extended Theories of Gravity
({{cite:7dca545ee066989189cab6fd768ab581bc1808d5}}; {{cite:f9a02d322495278d8cdecda74d70bb0554150fc3}}).
In particular, the so called {{fo... | i | 281242af0e2b37085f841194b3d99dc3 |
The data set collected in 2018 is divided into two subsets, S1 and S2,
which correspond to the periods before (20{{formula:28a3cbac-d6ab-4c99-aa60-5e960ef37d3d}} of the data set) and
after (80{{formula:2c15ab39-74a1-4126-bfaf-80cad506a5ec}} of the data set) the installation of the new final
collimator COL. The subset... | m | be51996152ec5cca96d8573bd6158541 |
Adversarial training can be seen as a type of data augmentation where the inputs are augmented with adversarial examples [{{cite:a862f342c9ef663dc872ef4fc08a633dea53190a}}] to increase robustness to adversarial attacks. Here we test the commonly accepted hypothesis that adversarially trained models need low frequency f... | r | 39252e540dd222c8e6a51c924805db2e |
In the previous section we showed two important results with practical implications for the forecasting community.
First and foremost, it is possible to compress a dynamic forecasting ensemble into a compact individual model and retain a competitive predictive performance. Students show a better average rank relative t... | r | 03272fcb61130a1977636570d0301657 |
The baryon-to-meson yield ratio {{formula:12aff9a9-bded-400c-bf75-cf668dbcffde}} is presented down to {{formula:f47ee95b-f043-4bb0-a80a-e70ef12de9ce}} in p–Pb collisions at {{formula:a9831b82-848d-4603-a5a9-c81c97a24a78}} , and for the first time in pp collisions at {{formula:5618e848-92a1-4655-9bd2-42977d3252d6}} , ... | r | 2a21b2ecdfbb227098b41ef2a850241e |
We have implemented the proposed hierarchical attention using Jax, an open source library https://github.com/google/jax for automatic gradient computation and linear algebra operations on GPUs and TPUs. All numerical operations in our algorithm use the Numpy native linear algebra functions supported by Jax.
In all our ... | r | 169cc4409315936bd79fe9a005e8ef9f |
We remark that, an alternative way to estimate {{formula:c37e1035-5c33-4b19-b9f4-07c8ec7786cb}} for each {{formula:8a98c623-fcf0-4421-bb11-716588276307}} is by using the method of classical shadows to obtain `classical snapshots' of {{formula:753a910f-a8bf-4d1b-aa63-fd37bb5280f3}} that can be linearly combined to ob... | m | ac35a055106f9140b2346b962e120a18 |
In this section, we outline the potential of our proposed scheme.
We test our proposed AUQ-ADMM on a series of machine learning tasks, including elastic net regression {{cite:e3d7e8d4f5dc10c5e666594a61a65157410ce74b}}, multinomial logistic regression {{cite:e3d7e8d4f5dc10c5e666594a61a65157410ce74b}}, and support vector... | r | c69e854918893341485649e67f58c65c |
As noted previously, the three models {{formula:22aacdc6-ed52-4c70-9057-3c56da7efd0b}} CDM, XCDM and RVM have the same number of parameters, namely 5, one more than the {{formula:17617008-1af1-445f-afd9-bd24d8b657b9}} CDM. The CPL, however, has 6 parameters. Cosmological models having a larger number of parameters have... | d | a63e655736a625822cf89fd9f029aa8a |
where {{formula:c91a03b3-c377-4095-b877-a67ad158c1e4}} and
{{formula:0b81ccd8-1dec-497b-882e-f63cd188b1e5}} .
Here, {{formula:068182cc-29e3-4997-a8e0-712566b72eb1}} is Fermi's constant, {{formula:e2b1133e-e0ad-4da1-abe7-fe0fa5edbef0}} is the {{formula:dfba4bea-5c9e-4e90-b1b2-d12d75dc175c}} element of the
Cabibbo-Ko... | r | 69046137cb1d4da10ebad364a467c5f8 |
This solution corresponds to the class VIII Heun polynomial {{cite:10674d66d78c8b53f1a035687b40ba41794bfcaf}}. Since the above mentioned procedure is valid for all of the polynomials {{formula:e68691cf-2c7f-4e75-abdd-446e79969398}} given in Eq.(-) in the eigenvalue and eigenfunction solutions, one can directly write d... | m | a475b71b76cf27e1218dacbf10cc0b31 |
Efficiency: While DCRNN takes on average 271 seconds for one epoch, GA-DCRNN requires 401 seconds. The discrepancy between the two models is due to the attention mechanism, which requires more computation. Note that when implementing the attention we followed {{cite:7d9654f9a3e3c02497e1147d56fc02271160f810}}, but a mor... | r | d38973c15a90b00c47770bf25ab59ae2 |
Proof of Theorem 4.
To analyze the large-{{formula:5b354a1d-e15d-495f-b7b0-46be1cf1dbb0}} behavior of the rogue wave {{formula:8afcc6ba-4a09-4efe-bff6-3e2d9b9859d5}} in the neighborhood of the origin, where {{formula:34d77b17-14f0-4479-921e-37705413e1cf}} , we first rewrite the {{formula:d64838b3-e082-42b7-9610-66b4... | r | 5efa6f70c8a27525434632ac20b7454a |
Quantifying the rate of semantic change for a word requires records of its meaning from two distinct time periods and a quantitative metric that compares these records. One type of methods that constructs word meanings and enables comparisons over time is based on word embeddings {{cite:97adc2febe6de7f495c99757cadf4ad0... | m | 7a593c73000fdbc3a41d64ff3b8a6c87 |
We did not compare with FedHealth {{cite:227b8e9b287af571f66d76547b63546817c0a7d9}} since our method focuses on more general setting where clients do not share large volume of datasets. We compare three extensions of our method with five methods including common federated learning methods and some federated learning me... | m | d1948397e2af027f9008102f2fa059d6 |
In terms of the closed string moduli, the finite difference equations are most naturally interpreted as being related to the integrable hierarchies underlying Gromov-Witten theory, see e. .g .{{cite:ad323cbec0fa425d1868c843285cc578a85dbb8e}}. Finite difference equations of the type put forward in our work are shown in ... | d | 27aa03d6a09572657f102f1a3551de51 |
We compare our method with MonoPerfCap {{cite:25397202653763653b4d4fe0ee0ff27688dfe8a0}}, a representative tracking-based method. This method captures the human performance from a monocular video, but requires a pre-built subject-specific template model. We thus conduct the evaluation on their own dataset, which provid... | m | 8c91e3a992c7f20f3984c15c6665564b |
When {{formula:2d0413ad-6bd4-400b-8623-8ae714a02a1f}} has good ordinary reduction at {{formula:7d9eed1b-3556-4615-9608-2f91d441525f}} , this follows from Mazur's control theorem, see {{cite:ae1dbbfa235f5d01c8f6da6e7a310bd00fccca63}} or {{cite:5bae87910821c16eda815dea381ad824e32bab3e}}.
In the good supersingular reduct... | r | b14fb5d42cf202c407acbe2253fe19d1 |
We use the pre-trained weight realized by {{cite:a0e77fd3d89398b1964d6493075c8ee5f30b7c1c}}. For all of our models, we use the AdamW optimizer {{cite:44339f3ef2c1f11eb94564ce8d5deffa0f4f6f98}} to optimize our model for 20 epochs, the learning rate is set to be 5e-5, the batch size is 48 and the warm-up ratio is set to ... | r | f7d2f73f8527cd3db82b6573ea6759d6 |
The impact of deep learning methods in computer vision is growing rapidly, owing to the recent increase in processing power thanks to GPU's and CPU's, sometimes solely dedicated to machine learning.
The current access to huge labelled image datasets such as ImageNet{{cite:735ade7bbbf6f764cbfcd749599c13c39d0e8620}}, COC... | m | 787df738142a59dba5f35b957ce1fdaf |
We presented a system that uses diverse prior data for general-purpose offline RL pretraining, followed by fine-tuning to downstream tasks. The prior data, sourced from a publicly available dataset, consists of over a hundred tasks across ten scenes and our policies can be fine-tuned with as few as 10 demonstrations. W... | d | 778f765b625bb60b3c98669031bbd3da |
Thereupon we provide preliminary analysis on the face image dataset FairFace {{cite:e2e915e7bf61b39d9a403c8d0a0a8a1c17cf0e68}}, FairFace is a face attribute dataset for balanced race, gender, and age. It categorizes gender into 2 groups, race into 7 groups, age into 9 groups.
We freeze the pretrained backbones and only... | d | ad223762193fdf5a2aaca61bb7212c10 |
Finally, we comment on phenomenological applications of this work. If we assume that a Georgi-Glashow GUT exists in nature and is spontaneously broken to the SM gauge group, one can use measurements of the gauge couplings at low energy to predict the GUT scale {{formula:1e159db9-0a7c-41f3-b854-94d22c455cdf}} and the c... | d | 5619dc4fc0e9ad265f7c542c0ff7bee3 |
While our experiments only explore pretraining of ResNet backbones, we note that investigation of pretraining Vision Transformers (ViT) {{cite:fbee052a2c3809b260c1fd24d1c7a8709fb3d684}} would be a direct next step in this research. ViTs have shown improved performance in segmentation {{cite:dca71af23394d70e0deeccb9380f... | d | cd517e776ceed167d2ed3d59328bcf39 |
When {{formula:1b12e3c1-9f02-4c4f-a8a7-90cb2312d847}} , the existence and uniqueness is a consequence of the Lax-Milligram lemma on {{formula:1e9f2896-9880-4e35-95b3-711c31945f8e}} . The classical way to deal with non-zero Dirichlet boundary condition {{formula:cc28cd7c-9220-4c36-9cbe-fb704c31dba8}} is to find a lifti... | i | 79f535b6ef6d77a93e052ea7aa5bd0d3 |
Substantial quantum computations in a system of real utility will involve long sequences of one- and two-qubit gate operations {{cite:de27b773e05a91980afd5dae5421181abbb287f7}}, so a thorough characterization of specific gates is important for predicting how they will behave in such sequences and how experimental error... | i | 0a32a72691111b1197a86efe5ed1c771 |
(6). Relaying pseudo features (latent representations) in CV. Generating high-quality samples can be very challenging. Some CV papers propose to generate features. Example systems are {{cite:ccd7c2b1ec28c6b5287b1898b069c492a3f7cd4d}} and {{cite:0497fa36977184c53d3d0546da2f60178946534a}}.
| m | bf2c69a307edf39a48a71a52569ad9cb |
In this section we describe how multiple instance learning can be used
to address some of the drawbacks seen in previous approaches, namely
the need for expert knowledge in lexicon-based sentiment analysis
{{cite:4d8cc06e4f3563d4cf0cc5c453bff04b1c13fa05}}, expensive fine-grained annotation on the
segment level {{cite:5... | m | 614eafb7180a0991fc3c2362007a5953 |
This part compares Dual Teaching (DT) with previous semi-supervised wrapper methods, Self-Training (ST) and Co-Training (CT). To show the generalization of the proposed method, Logistical Regression (lr) {{cite:fa11ee696eefd3a3fc0d61e697a4ffe8f4a0540e}}, Support Vector Machine with linear kernel (svm) {{cite:9915d79c40... | m | 3e4f8b374312be38bcc2baed5d7587d3 |
Next, in case of the similarity {{formula:5778d688-cec7-4d7b-9023-cec0483774a5}} , as hinted in
sec:gsimlearn, when {{formula:af65276a-1264-4b0a-b0e4-aa68b40085dc}} , {{formula:6a023d29-c08f-41fa-a57d-9a1cfb984160}} is a non-unitary CPTP map.
This thus enriches the family of feature maps at our disposal compared to th... | d | 56eb72e8cf3390b9c5b66db3d1d2b217 |
The advantage of Theorem REF is that it works for general branching mechanisms without criticality restriction. In particular, it applies to all the stable branching mechanisms given by (REF ). Furthermore, the proof of the theorem actually provides a way of finding explicitly the ergodicity rate {{formula:238d577e-51... | r | 828439fda3e9012fd2b7a3adf12c40a9 |
The details for the result of analysis with operator theory can be found in the appendix B.
The mobility edge of the model (REF ) with {{formula:36065c4f-4657-47d0-989d-8055f0ae85f6}} can also be obtained by looking for the self-dual points of system,
which was originally given in Ref.{{cite:b0f29753d72a3c2f2993cad96f... | r | 4bdeefe6af89e20f155736859bdd2561 |
The here presented approach was developed in parallel with the super-resolution method proposed by {{cite:2bd0c77ca7530e9ad5ca2f334817325e9f75011b}}. In the current work quantitative results are presented in terms of SSIM, PSNR and VIF for all cardiac MRI slices of test patients from the ACDC dataset and 45 subjects fr... | d | f32c223bac903bb16ea8fa4818cfb200 |
Ablations on Part Representations.
We further compare 3D reconstruction methods that use different part shape constraints.
A majority of existing methods represent objects with a single mesh {{cite:9112f7f5f9e9e7d50b8cef1e5615c96b09a802c4}}, {{cite:979045aa810b4c46a042dfae5a355808110ccd05}}, {{cite:2fe0f501761dac48a147... | r | c4b42e9e36e2c0e2375e34ec257bfb3c |
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