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The dimensional effect in the investigated thin films is another issue worthy of discussion.
In principal, dimensional effect can emerges with the decrease of film thickness {{cite:2480ef252a39048fba162eed7055d79058901228}}. When the thickness is smaller than the penetration depth {{formula:2e09deca-fea5-4082-ba9f-25d5... | d | 7061fa79e7a39099fdb117bd617562f8 |
DanceTrack. In Table REF , we report a remarkable improvement of 6.8 IDF1 and 7.1 HOTA over state-of-the-art. Given the unique features of this dataset, these results highlight the versatility of our approach to utilize the right cues for different scenarios.
This is in contrast with methods like {{cite:4778afbd96c2074... | r | cf51a06e1cb8234c36df26defdf6bf8e |
There are several interesting future directions. An obvious one is a higher dimensional generalization, which we will come back soon {{cite:8f9415bc85e63bb6ee0a7bb7f85cc791d107b4bc}}.
Another important problem is to explore string theory embedding of the Island/BCFT correspondence and see how the coefficients of one-po... | d | fff682604e7c2306d317e0fe0310e85a |
Figure REF shows the spot positions and spectral profile of the single maser feature throughout all the epochs. This feature was in the bow shock CM2-W2. The spatial distributions of the pre-burst maser spots are compact with a size of 470 {{formula:38926bfb-340f-4757-9458-0c82b07da50b}} as corresponding to a linear ... | r | 810fa30e78b0d78c7000b1dbac67e7ad |
Contrary to prior-free monocular Simultaneous Localization And Mapping (SLAM), image-based localization can align the observed single/few images to the training images' coordinate and recover the pose globally. In monocular SLAM, the system can only recover the camera pose locally (relative to the starting pose) using ... | i | 309a2b85560fb7bbab277e817ac0afa3 |
The results obtained using different strategies (as explained in Section 3) for training the I3D {{cite:9640af66d9980355c62daf549ac1a6ab1ab657b7}} model are shown in TableREF and TableREF for the validation and test dataset respectively. To our surprise, the I3D model trained using only RGB images performed the best ... | r | e9af786ba10aa272e541ab5d6781dd1a |
In general, it is assumed that the binary merger rate has either the form of Eq. (REF ) up to an arbitrary redshift, or functions that are given by Eq. (REF ) up to a certain redshift and later decay. These two families of models roughly correspond to primordial and stellar black holes respectively. Here we will concen... | i | c88722cd68b58c4ef8a2aef3cf069470 |
In this work, we take inspiration from recent advances in modeling language {{cite:cebefe4f41952a73db3770528f12edbc9b45fdbf}}, {{cite:92ce36382bc094e1e353cbbdee8f24d285c077fa}}, {{cite:cc07fdcc9be59766bdaa7096d4bfc4eb90b7edbf}}, graphs {{cite:d9f62b141fdd345b4387c9312986bccf3e10106f}}, {{cite:81d1c52502940b437b2f9d6f29... | i | eb389e4d24078709f54ab04de6f87340 |
The works listed above focus on the canonical setting of a correctly specified, i.i.d. probabilistic model
in which the dimension of parameter is fixed and finite.
Going beyond this canonical setting, a number of authors have provided extensions of the theory.
For instance, BvM theorems have been established for non-i.... | r | 8b9a2a324917a66f4934c6ebac332af3 |
Integration of domain-discriminative information. The relationship between (REF ) and (REF ) provides us a theoretical insights that the problem of minimizing mutual information between the latent representation and the domain label is closely related to minimizing the {{formula:4afe53dd-4a0b-4438-875b-00489eeec88b}} -... | m | 6bdc6e770c0febb4f7b8fadc25eb2e55 |
Our results are summarised in Fig. 3c, which highlights the transition between isolated skyrmions and a disordered skyrmion lattice, determined by real-space imaging of the spin textures as well as magnetotransport. The critical parameter governing the transition {{formula:f735939a-10c8-48ef-9969-f18b64d9e8c4}} is ide... | d | 7702449d0d30bc016b7085c580081f65 |
We measure the difference between modelled and observed variant distributions at time {{formula:ef8e7bc5-a216-4075-b725-37957ffd1dd7}} using the total variation distance {{cite:110d0ee9e318ef697f9aa5ea446b9936b03e7882}} between the variant frequency distributions in each cell, averaged over all cells
{{formula:05e30da... | m | 2cf42a9d668cf5399648f0eb0236240f |
where the second equality holds because {{formula:d34477b7-151e-4172-b483-4291cfe1c03d}} and {{formula:931e4d59-e134-40cb-b0e4-830065b63acf}} denotes the adjoint. We note that in this context the loss function is invariant to {{formula:6e635dc2-73d2-4545-b6bf-b96026bd06c6}} transformations of the weights, i.e., {{fo... | r | 154de500b80c5eb6143869b9e2b27ec2 |
Greedy Clique Extension {{cite:2b4abacabc1be5f6ec4f05642cd2aa2dd32a8ea7}}.
The Greedy Clique Extension algorithm (GCE) can be considered a heuristic for the optimization problem of finding community structures according to the Lancichinetti community quality function {{cite:6d4795c1de5f931b05dfdeef7cef3971bbed49e3}} {... | m | f1fcaf3ef3024924ca523058ba9e8fe1 |
Besides {{formula:c51d0d88-2be8-4f11-a64f-aa97617ef837}} ,
we also computed the polytropic index {{formula:b3af5de3-c802-425c-af46-54520be81def}} , which has been
discussed as an indicator for the state of matter in the core of neutron
stars.
The results are shown in Figs. REF
and REF (right panels). At {{formula:397... | d | a68387d65474085f8499b3503d752dfa |
Accurate density modeling is crucial for this type of generative modeling. For instance uncalibrated {{formula:d73b1038-401e-44be-a7d3-436bf86b61f2}} can adversely affect the balance in the second (ELBO) term of {{formula:85f6aa15-082f-4421-a4f1-e792f51e17f8}} . To examine the potential of the proposed approach withou... | d | cfc13b37185ba8da66e060c4468a482c |
The overall procedure can be viewed as maximizing the evidence lower bound (ELB) {{cite:7e43d4cc2d51f6aa6a62e066984e4cea606c3d11}}, {{cite:6a0ad2cb6f436945938012521fcf80cee5522105}} on the joint likelihood of the model distribution over images {{formula:8e093273-b52a-48ed-b2f5-e700f403861e}} , captions {{formula:347766... | m | 36b4116e4e20bffd0f78958310476822 |
In contrast to the expensive projection or voxelization, point-based methods {{cite:60300ec034948001ed79a10ee33e950bd861338d}}, {{cite:02e729c381cd472f12baa3522d6e256f86caebed}}, {{cite:1627116272ca26ba6b09142bd611801ff5dde115}} process the input point cloud directly and efficiently. The pioneering work PointNet {{cite... | m | 0ccf42110da14ece89ee08ab46b4b6f5 |
We also present the results from various ablation studies of our method. In the main text we present average results (PSNR, SSIM {{cite:1f9d327fb85d3a6c3cc82b32e27baba405c77d88}}, and VGG LPIPS {{cite:be145d37f0cab6ff894466d8d03477f47d2ee499}}) over all scenes of each type; full results on each scene individually are i... | r | 54d3b8c1b5e6962835355900a8bf805c |
In our analysis, with the thermal annihilation cross section, the best-fit {{formula:eddf2329-e7d4-44db-9909-f3fdf8282187}} is {{formula:4ec71452-2f39-4eaa-89d6-674df1e7b9d6}} GeV for the two-component model (via {{formula:1648e8cc-4a10-4e5b-9bca-36e276131c1d}} channel). Surprisingly, this value and the annihilation... | d | 578bfcf12d6766276ed55424242dab9f |
The DialoGPT medium model the authors used has 345M parameters with 24 transformer layers.
It was chosen for this work, as it was reported to have the best performance (compared to its small and big versions) across a set of related tasks {{cite:6543d5d2b0b06f61c43b978fa66868563d6403c1}}.
The experiments were carried o... | m | ec97c2dee732c79425e5416b15599bae |
Our causal mediation analysis from Section REF found evidence of partial mediation of fatigue, and also evidence of full mediation of weakness. Baron and Kenny {{cite:f5b07a38515d9abea3d1812de5ee51469e745019}} argue that mediation is stronger when no direct effect of the treatment on the outcome is found, but there is... | d | 82d16e195f17604a22e53963bffcb23f |
Reinforcement Learning (RL) ({{cite:66d09db03b9e5b2c564ab6455f578ce9048ccb5d}}) is an Artificial Intelligence paradigm which aims to develop policies for arbitrary tasks using a reward function as a supervision signal. By trying different actions in some environment and observing the outcome, an agent should be able to... | i | c54cb124bc4efec9fe64a2d9de906921 |
In the next section, we briefly summarise the state-of-the-art methodology for image segmentation (SEG) and super-resolution (SR), which is based on convolutional neural networks (CNNs). We then present a novel methodology that extends these CNN models with a global training objective to constrain the output space by i... | m | aeb7a7848ee6e42dcf2962a4b480486e |
For the proof, we refer the reader to {{cite:4f2668a1d49478c59158b5610c3bd4fb6e50f26b}}.
| m | e11427e4f0d1109748b59b86eed22b68 |
The proof basically adapts {{cite:0d29ed059f1c0efac996292e38efbf48ac32a771}},
although it may be less obvious than others and has a different presentation for
revealing its information geometric nature.
The following formula of the next Newton direction by
parallel transports {{formula:6437f0d8-f210-436d-912c-4cc97d88d... | m | 6d94d966736d6cf22d5b2a9b5c76b49a |
where {{formula:5ea510c0-3a03-4ec2-a29a-0f82c435086d}} is the disc specific flux per unit frequency range at observation wavelength {{formula:25730c55-7077-40c1-97d3-1c5d9cf8e20c}} , {{formula:4801a9b4-19e2-4224-aab7-21c2c73a4092}} is the assumed dust opacity (noting that this involves various factors and is subject ... | m | a3c1276c833b59ecb19432b686414bce |
We perform experiments on one of the most popular databases for semi-supervised learning, the STL-10 {{cite:2699cb1e1d03f3614cff44e5ef82753df7da3811}} database and follow up with an extensive set of analysis for the same.
| i | e1d1f4c061573057afc640b48e0993ce |
Algorithm REF describes ARock. We assume that the write operation on line 5 is atomic, in the sense that the updated result will successfully appear in the shared memory by the end of the execution. In practice, this assumption can be enforced through compare-and-swap operations {{cite:a635cbb03643f8fbb41cc08f6054509e... | m | 2a4be71d024fcdf793a4be4c99340fb4 |
All the experiments are performed on a computer with Intel i7-6700K CPU at 3.40 GHz, 16 GB of memory, and a Nvidia GTX-1080Ti GPU of 11GB graphics card memory, and implemented with the PyTorch toolbox {{cite:e62a681d4b245a176cfa508a4b8d035ea8675b02}} in Python.
The initial {{formula:ae3a70dc-e2e7-40c5-a942-dcf4b0267222... | r | 5a1587c7e793450fad53f529e746702d |
iii) Disparity Map Prediction and the Loss Function.
To predict the final disparity map {{formula:be51da3d-971b-4713-92fd-dc1f39825e7f}} , the output of each stack in the Hourglass module of the cost aggregation is first up-sampled to the original size {{formula:3abc2ffa-b9c5-4494-b993-6ecd3190084a}} , denoted as {{for... | m | c4cb84d5a1c3f19ebfefece59a96eb46 |
In this section, we provide a self-contained description of Crandall-Lions (continuous) and Barron-Jensen (lsc) viscosity solutions. The results of this section are classical and can be found in various references such as {{cite:9bea52927619983aaf1242aba322bd6ac8e9587a}}, {{cite:306e838afe74ba913acf241cd3bce2996345fdc8... | i | 79772e005683358e155a4b16b69a68dd |
For pretrained embeddings, we used OpenL3 {{cite:3a5780a085911c05ce63cd40629d43d2bd8df4a6}} and YAMNet {{cite:93e912c2c2a1222dc99667f14d164dd0055da43b}}, both of which were trained on AudioSet {{cite:ea1fceb8f34ff283b46c81a86590d653fd02e193}}, a large corpus of YouTube videos, and are designed for sound event classific... | m | 5c1c8a5784ea6b068d0939543be9e280 |
Looking forward, although Paulihedral is designed from an algorithmic perspective, it can incorporate those technology-driven optimizations.
For example, our technology-dependent passes can be further optimized with more comprehensive models of the target devices. Paulihedral can be extended to other technologies
(e.g.... | d | c32efa046edf15a402ce30d63dbec545 |
For photo-based datasets, we executed the structure from motion pipeline to estimate depth maps with SGM {{cite:7508761427d423b9f62b47a8b992d1fe5d803c5d}}
method and evaluated our algorithm by using these depth maps as input. Note that to speed up the estimation of depthmaps, we downscaled original photos for some data... | r | 9f2560529283c5dad226f697d3e75ab9 |
In this paper the almost sure convergence of the AWH algorithm is proved by identifying it as a stochastic approximation algorithm. Free energy differences are considered jointly with ergodic averages for fixed parameters. The stability issue is circumvented by assuming that the iterates take values in a compact set an... | i | b0bf481016c20d5307cf29d3577c03f4 |
Is it merely because of larger transfer length? One hypothesis on why long sequence can help is that the sequence length for downstream transfers (4096 for COCO; 1024 for ADE20K) is significantly larger than MAE ({{formula:d0692bfd-3508-4f92-8d5c-eaca36d1c5b7}} ), and long-sequence MAE is just closing the gap. To see i... | r | 310b594f1fa79a07f8a71aace2a24bd6 |
see e.g. {{cite:a47b9e63e00172011dc8574c821199c0e1ed44b7}}, {{cite:06f24a5e903f175b98ecee378a68e24cd57d7759}}, {{cite:a00b4ad687a789a6be86f98988554a2a4c5c0f12}}. If {{formula:aef6f2e9-b597-4147-b98c-64d9e9e7c228}} in (REF ) is replaced by {{formula:777a2402-5f4b-4bfb-902c-715fd5cc7fdd}} for some {{formula:2c78484a-0c... | i | ba62b8b754663d84b2e74b8b076ca933 |
In the regime of scarce data or when new classes emerge constantly, such as in face recognition, few-shot learning is required. Modern computer vision methods of learning from a few images are based on deep neural networks. These neural networks are intriguingly vulnerable to adversarial perturbations {{cite:4625645013... | i | 380c0cc06280f6c83d829d2ab833cfea |
To construct a 3-dimensional oriented TQFT (REF ) one needs some initial data, with very general initial data being that of a spherical fusion category {{formula:3417c947-ec8f-47e1-b1fb-6d14b1e385ff}} {{cite:09d76ce62fb867dbc1ae531c75d1b3c99d0a4c7d}}. The most well-known way to construct this TQFT from {{formula:411f1... | i | 052b7fc6202da6d951de9c02332e3dc3 |
In our SIFN model, we use BERT encoding from Hugging Face https://huggingface.co/transformers/. User and item embedding size is set to 16. The batch size is 100, learning rate is 0.001, dropout rate is 0.2, and {{formula:95b3ce22-270e-4b80-bbae-d34802792dbe}} is tuned amongst [0.1,1,10]. For baseline methods, we follo... | m | 8f04f4379dfee96c007d936eb8b2499f |
{{formula:4fb1b80a-2677-4a0e-a37d-6bc368f14241}} Weakness
Although our HAG-Net has achieved good affordance grounding performance, there are still some limitations that should be addressed in the future work. Firstly, it is not an end-to-end solution, where a separate pre-processing stage is needed. In the future,... | d | 7c4be710af9feca726dd3746b7e10b7b |
Req. 2 (Ablation Study)
It is necessary to always consider a `vanilla' model {{formula:97fec7df-33b2-4c4a-a114-44bde5d7546c}} that uses {{formula:ab32db81-c0f4-4463-b600-cc9224a9d630}} in a trivial way together with an {{formula:38fa881a-9826-40fc-8798-75d2ea5c3eab}} randomly sampled from {{formula:b6a25f53-bfb8-48... | m | 7e738ac6c2a88802eb7cbb782daa4039 |
A prior on {{formula:68c3e8c2-66d3-4080-a858-93f9ec4bc4f1}} is induced by setting {{formula:b68ff941-a814-4d7b-954f-e5b7b12eb970}} , for a Gaussian process {{formula:761bd5b9-e2c5-4706-bca6-d6e108df9064}} . Any Gaussian element in a separable Banach space can be expanded as an infinite series {{formula:5b5a779b-d2a8-4... | d | af90de6d2978e03b0415ad417f0064ae |
We compare the DnCNN trained using the meta-optimizer with several state-of-the-art denoising algorithms. These algorithms are the NLM {{cite:d9eb33a2fc19d66b7d0599455cd0bdc91e1da4ff}}, KSVD {{cite:b2a1193cded8bf115f81df28f8bfd636004ec3a9}}, and the BM3D {{cite:ddc5e5d67bbcdd388c67f797404321714be93119}} algorithms. We ... | m | ef3c9d30516a821ad24610a75a954223 |
These methods aim to isolate input patterns that simulate neuron activity in the higher layers of CNN i.e. analysing the components of the input data that causes the output {{cite:d9b86876013b02b5c31235dd9351b21b3546920d}}{{cite:8116aa31672b486d9d3679740fc0e697f74d4910}}. Again to understand this consider a linear mode... | m | 6249face0af1808f746d904aa0c2e120 |
Standard Attentive Reader (SAR): This is an altered version of Attentive Reader, where attention weights are computed using a bilinear matrix {{cite:e8fefd38984c27c6708880d76c989c4f56b41d82}}.
| m | 2ae6586c1ce4849fd9bde7f7a38dd37f |
The introduction of the new variable {{formula:a0d0c889-792d-41ab-8c59-15982c457735}} and additional constraints preserves the equivalence between (REF ) and (REF ): from a solution of one problem, a solution of the other is readily found, and vice versa {{cite:fe6971969eb5fb4349547bad6d6f344720c2831b}}. Similar to (R... | m | 09c0d8fd6d003698d89f5d63dd79ba01 |
To explore the impact of using different attribution methods, we train our model with Grad and Grad*Input. We see on Table REF that Grad*Input works better whichever channel strategy we use. This could be explained by the fact that the former outputs sharper attribution maps ({{cite:969422e7b4d91c446c5367793f41ee68b7d... | m | cd2a1c173526d677ea47fa635fd662c9 |
For communication with static, or quasi-static channel conditions, the problem of acquiring CSI is only necessary initially for a given coherence time in order to establish beam alignment. Many innovative solutions have been proposed to obtain robust beamforming for communication, even at a low SNR regime ({{formula:99... | i | 73eccc16d7d08e4c1921ef43ea8a2e2f |
Our results also allow to confirm that SWD can be applied on different datasets and networks or even pruning structures and yet stay ahead of the reference method. That means that the properties of SWD are not task or network-specific and can be transposed in various contexts, which is an important issue, as shown by G... | d | 0ee8f9b9c3a6834b4e84ee5ef90668ec |
Another point that worthy to be discussed is the semi-classical gravity itself. Semi-classical gravity is usually criticized since it has contradictions to the many-worlds interpretations {{cite:73acb392fa21d86e114a5a8122a9c8b3444d40bd}}, and some inconsistencies when combining a quantum world with a classical space-ti... | d | 4d6f58f341d04c0d945c74d04fd9e12d |
In addition to the simulations, we have also shown that in real life applications RF kernel is competitive to RF.
However, the usefulness of RF kernel lies not only in a potential improvement of performance in certain high-dimensional setups. Availability of the RF kernel for regression, classification and survival exp... | d | 2c227148eb1427c1cc6f169295978425 |
Using a BEC {{cite:fec91aa7629e3d26c2e68194ff75ff08b3bb25b4}} as a source of ultra-cold atoms brings several advantages to atom deposition as it can significantly reduce the linewidth of longitudinal and transverse velocity distributions providing excellent coherence and collimation for the atomic beam as well as offer... | m | f2f00552b8fda610180d60ffee65f063 |
We run two single-view methods to serve as baseline comparisons: K-means (KM) {{cite:eafb27b63242d997b74bb6447f4651675e710146}} and Deep Embedded Clustering (DEC) {{cite:88ed8a675e3b20ac76dc12555e91ab1c3d518cea}}.
| m | e927d90e214985c43bccf3f7fd678b60 |
In the pre-training stage, we use the Barlow Twins loss function to learn graph representations from crystals. This loss is based on the redundancy reduction principle proposed by neuroscientist H. Barlow {{cite:3fb4ae8aa5ad64e62b59f63718caff25aa7dc2b8}}, {{cite:592d724f4c63df1316e087dac3e7e64ae540477e}} and was introd... | m | 715468d41ef490d5a774141fcba8ae32 |
The set of measurements defines the topology of the FG and for almost all placements of measurement devices of interest, the corresponding FG will have loopsNote that, even if the physical power network has the radial structure (tree structure), the FG will be loopy. An exception occurs, for example, for the scenario o... | r | b74e27e021c2efd959ae1b434e677b26 |
The physical manifestations of vacuum electromagnetic nonlinearities have been a fascinating topic of research since the discovery by Euler and Heisenberg {{cite:2e812a72c7b069267c1d306597f9b79b4e481517}} of a striking prediction of quantum electrodynamics (QED), that is, the light-by-light scattering arising from the ... | i | aea6ffbe2c10229f0d08cb72b20e8785 |
We conduct experiments with both the existing popular protocol and USB to provide comprehensive results and comparisons, where error rate is used as the evaluation metric for all tasks in USB.
For CV tasks, we follow {{cite:fcfb40bc9da1f863b437e34bafa65afb664f8818}} to report the best number of all checkpoints to avoid... | r | c9af9da761f7e28cf2d4657f0d5d276a |
We will consider homogeneous periodic integrable spin chains (lattice integrability) with the Nearest-Neighbour interaction (NN). In magnon propagating systems by definition one can obtain an analogue of momentum operator (shifts) {{formula:bfb5e1f7-8e0e-48f1-8966-0008ca173e5d}} and 2-site NN charge – Hamiltonian {{fo... | m | caa069f7da070b77e58aaa9022ca0c60 |
An application of attention-based pointing to generate solutions to another classic combinatorial optimization challenge, the Vehicle Routing Problem, was proposed by {{cite:4059a23dfb239a0652cfb3cbb9925e3827832777}} {{cite:4059a23dfb239a0652cfb3cbb9925e3827832777}}. The resulting architecture is an encoder-decoder Gra... | m | 526aa2f2b6b462682608cbb6f2b1f4c6 |
Visual Genome
(http://visualgenome.org/, {{cite:fd8975993ca4551e680bc76f118956e28b4f55ba}}) is a large set of real-world images, each
equipped with annotations of various regions in the image. The annotations
include a plain text description of the region (usually sentence parts or short sentences, e.g. “a red ball in ... | i | f1ff684cab60a3dff35babd4147140e3 |
+ ShaekDrop + AA {{cite:4b8c05e352a902340570b9cc24173c03dc076e21}} {{formula:e1e1d661-25ab-40ea-a91f-bbbc08b012ca}} {{formula:72ffcdc5-257a-4824-a404-67a31ee67e1e}} - -
| r | 6167080376f61d7b2b5a96b46d21df67 |
However, neural networks have an inherent/natural vulnerability to adversarial attacks {{cite:0ed2140d4e0bf3b5c3eceee2162733f39a5c5e9e}}.
That is, a neural network model can easily lead to a false output by adding a small perturbation into the input of a neural network.
Such perturbation, called adversarial perturbatio... | i | 68991f36c34ebab9a7b89d772db6f990 |
At the long wavelength limit {{formula:68bfbf19-4ce4-4e57-a2a1-b78f0c3a0b8a}} we would have an undamped optical collective mode of the form {{formula:ff5e50f5-fe31-47e7-90b0-e08bf3003ce2}} . To derive an analytic expression for the long wavelength limit of the optical plasmon dispersion of the system, we make use of t... | r | c21948651356a72dff46bff3852cf995 |
We are interested in learning a parameterized function, {{formula:eee86955-f79a-49b3-94d6-cfdcef4d043c}} , for approximating the posterior distribution. We now describe our proposed deep learning architecture for amortizing over designs, allowing practitioners to train a single model that is capable of evaluating the E... | m | 2d0ddab40afc1909159c75641ee6e25a |
On the other hand, the ELM generalises the single hidden layer network structure by enabling the incorporation of sigmoids, sinusoids, hyperplanes, RBFs, hard-limit functions, and other nonlinear functions as nonlinearities {{cite:e5b53c805dd2b94b5d5a9fb7b8cb5bb3423487ba}}. The design of an ELM offers a significantly l... | m | fc627d99ad3f3c333e0b783c1dfe694e |
Consider the trivial family
{{formula:0646b356-354b-400b-ab50-dddab4a4bcfd}}
defined by projection on the second factor.
Let {{formula:4e1a338a-559e-4ae3-954c-91da77d0d691}} be the diagonal,
and let {{formula:cf117018-d2da-416c-aed2-ecff7492267a}} be the section of {{formula:e5b49718-e5cd-43ea-b734-5fff26aaee75}} d... | r | 2142bf8fec191c9759916ced744a21a3 |
Our temporal network embedding method has arbitrary parameters and allows methodological choices. The tie-decay rate, {{formula:ad8fd8e1-22a0-4a79-b983-568ce5a9ac89}} , is defined by the user. The embedding results were qualitatively the same between {{formula:91f281ea-011a-4e48-97ba-d2396f7ac1b9}} and {{formula:c8ec8... | d | 75d0573a958c4726214d255813eff74a |
Second, the attacker should also keep the generated adversarial examples to be seemingly benign. In other words, adversarial examples should be close to the distribution of original clean data samples. It is hard to achieve this goal since features in mixed-type data are usually highly correlated. For example, in Home ... | i | 66aee6c58c1c4b7d2b58f291ac8120f8 |
One of the major challenges with enlarging vocabulary via image-level supervision (ILS) or pretrained models learned using ILS is the inherent mis-match between region and image-level cues. For instance, pretrained CLIP embeddings used in the existing OVD models {{cite:b30fe4d3fb291e11ea43f0816e7da0cda50b4484}}, {{cite... | i | 81c5ea61361ee8c0dca96d4665074f9d |
Forward MT. As discussed in Appendix , neither of the variants {{formula:fe7127bf-b88d-4e3a-ba5d-0a796dfca612}} and {{formula:2ace535a-1fe2-430b-8839-68edaf983f31}} is incompatible across the asymmetric GB of this sample as {{formula:cd763f6a-4de1-4b7d-8d6a-52f3166c4d53}} . We note that the nucleation temperature ... | r | 1ba9b4a4be8de8957bfe4f1e63883d74 |
Recent works propose to leverage large-scale image-text pre-trained models to alleviate the above limitations. These works involve zero-shot or weakly supervised semantic segmentation because the large image-text pairs are class-agnostic. Due to the target being to segment an image with arbitrary categories instead of ... | i | 0e68841b85ef20c5ea6dcd8fa139e493 |
Code-switching (CS) is usually referred to as the situation where a speaker alternates between different languages within a single conversation, e.g., {{cite:cad10220d69fb0367a1a3ad2df5964318cde27d1}}, {{cite:27ce30edbf152b4172c4db55fe0364542f58d205}}, {{cite:4cb1565342194697d0277acff566e133f759cbe0}}, {{cite:1d873c72d... | i | e53e3032b09a77827a4fa5445deb6779 |
Recommender systems are playing an important role in many online applications. They provide personalized suggestions to help user select the most relevant items based on their preferences. Collaborative Filtering (CF) has been one of the most successful approaches to generate recommendations based on historical user be... | i | 7de5f9f720daa4e7f5b0e5103ac953e8 |
While a recommender systems can serve various purposes and create value in different ways {{cite:06ba876a6a408e0ed22fae543a153e29c09e6831}}, the predominant (implicit) objective of recommender systems in literature today can be described as “guide users to relevant items in situations of information overload”, or simpl... | i | b32aaddb6b3c8d2faa82ab78755912fc |
As we have shown, the first sub-problem is NP-complete. The second sub-problem is reduced to checking that no critical configuration is reachable from {{formula:6e582aca-a4b3-4099-837d-b1120beffe79}} by a trace using the lazy time sampling with less or equal to {{formula:a3d4d862-c6e5-405c-97f9-6768c1dd739f}} ticks. ... | r | a3a78d346391761d8f68f57e465dad80 |
We also note that Multi-Krum {{cite:879c7578e509a10e4a91230406a30589ca5b5bf6}} is also effective at preventing backdoors from being created when less than 10% of adversary clients are present, although it has a detrimental effect on the clean accuracy ({{formula:3a8c9578-1e7e-4e11-94b7-5af68bce2b86}} 7% absolute) even ... | m | 4ca74a70414be04be80d3166a6c90e29 |
The tools discussed in Sections and can be used to generate fault-tolerant graph states starting from finite-energy approximate GKP qubit {{formula:7b416c1b-a1e9-46e7-a270-c63d132fff4c}} states.
Ref. {{cite:e34a47d7c9082e9cc512cec10f3117fa6604617e}} provided a protocol to generate graph states starting from mixed st... | d | d8fd1cbc1ecbf491a1fb663388ea4d46 |
A phenomenological theory of dynamically aligned turbulence at weak nonlinearities that can explain these spectra and the transition to the tearing-mediated regime is provided.
In particular, it is shown that, depending on the nonlinearity parameter at injection and on the large-scale Alfvénic-Mach and Lundquist number... | d | 902569b1adc48f0f6cd2ec18d1117a69 |
Recent work has interpreted several 2D biological systems including bacteria biofilms {{cite:21ac11ac8287867c2fda34d2445d5db6fb32458f}}, {{cite:639a46c101a9ef85290cd48dc0f5c21d2b301c3d}}, epithelial tissue {{cite:0de47a7b64706d6801d741582766219a972d4742}}, spindle-shaped cell monolayers {{cite:20fd51e20f27b5437f79e11c9... | d | bb4fac6f716b2a78217045e785c5a2b6 |
In fact, there are two major challenges, statistical heterogeneity and resource heterogeneity, when deploying FEEL.
For the statistical heterogeneity, the data across the network is massively distributed in non-i.i.d and imbalanced fashion{{cite:e73f51acda3f346b3dc6b97e5a37be3e63778d87}}, {{cite:79866b40331e4b3a797eb15... | i | 7ba77d56c76354cf9f6378687b4e1bed |
Remark 1.10
Finally, we make some brief comments on the analysis of the proof of Theorem REF , the details are given
in the next subsection. Our analysis is strongly motivated by the geometric approach initiated by D. Christodoulou
in order to study the formation of shocks for multi-dimensional hyperbolic systems and ... | r | 500c384c2972849e654848241149fbee |
The proof consists of three steps.
First we show existence of a {{formula:df09d3ae-c86f-43a7-9c17-d3deb47079cf}} solution to a Neumann–Laplace
problem with boundary condition {{formula:9dcc3435-e824-4c6e-80fb-768fa0028876}} .
Second, we improve the regularity to {{formula:6a8eee96-0153-451a-89ca-040b39299484}} .
Final... | r | 978531ac20004258c38d98a77bfc99a6 |
We compare our unsupervised learning approach against several baselines, including random initialization, ImageNet supervised pre-training, and self-supervised pre-training. For the latter, we provide results for MoCo-v2 pre-training on our unsupervised dataset without exploiting the temporal information. In this case,... | m | 57c2a35bbc4ca9a572c9008c8ad67383 |
(ii). Parameter generation. This method uses one network to generate the model parameters of another network. Since the generation can be conditioned on the task-id, it helps mitigate CF. Hypernet {{cite:8f52cf30673e96d8ecaeda85713311f6338fb2f6}} in CV takes this approach. It builds an generated network {{formula:f195b... | m | 772c974fd0a8addc7121ca685137e0fb |
The performance of the proposed ALGAN-VC model is assessed with subjective evaluations and objective evaluations. Our results are compared with CycleGAN-VC {{cite:da624e860896f9351d4b9f845a83a9ce8f62ed09}}, CycleGAN-VC2 {{cite:1c25475a22dbc870fbc85dbd848c7c7da6d16abd}}, and Spectrum-Prosody-CycleGAN (SP-CycleGAN) {{cit... | r | 37dfbbbd48e95b9d9a1ff2fead89e86b |
The proof of Theorem REF (Appendix ) resembles that of the data masking algorithms for FDR point null testing {{cite:2d545c0100fd50283dd37ddaeeba1cfc718a8ba4}}, {{cite:360e065af534eedd8f934c78ce39983ba6b64533}}, but also combines an argument similar to that of {{cite:cebb26a618436160c833464433d0906727351752}}, which e... | m | 5ca0bbd818eea80bfe6f99e3ee0799b2 |
To address RQ2, we perform manual analysis of our data to determine the nature and purpose of the discussions. As the entirety of our dataset is naturally too large to manually examine, we take a statistically significant sample (confidence level 90% +/-10%) {{cite:380617729310a79d5222f607200e02c42f8ee80f}} of 68 posts... | d | df89e389c79f9a813fdd5f9c6e380b8a |
Deep ensemble of neural networks (dNNe). Ensemble methods combine different regressors into a meta-regressor and we consider an ensemble of deep neural networks as proposed in {{cite:184c5514f0d016df321c06c7cde892c0f65f7d7a}}. Each network in the ensemble incorporates 2 hidden layers with an output of two layers one fo... | m | 8381d6297e02885a7fe8d35ea9f134b4 |
Hence, we formulate {{formula:c2910930-8e9f-414c-9c65-41162ff21b91}}: a novel simple framework to part-semantic aware manipulation or generation of objects through 3D generative Latent space Navigation.
{{formula:de998169-3f8c-480c-94b4-7376eb561451}} can independently manipulate part-semantics of an object. In contra... | i | 7b6269d9fbeb1b7e96210dcba72991bc |
Distributionally Robust Optimization (DRO): DRO {{cite:1899de090e51851bbdf2cdafcfea38ff6b65a98e}} minimizes the worst-case expected loss over potential test distributions. Often, such distributions are approximated by sampling from a uniform divergence ball around the train distribution {{cite:45bed8c105a86ed31b08563b3... | m | 9f1308431fe9e7c380200c6ae7564e72 |
Based on the localization principle, the block particle filter (BPF) proposed by Rebeschini and Van Handel {{cite:02811968a4ecfe7e736d2e4d5a84779e005dd46d}} approximates the filtering distribution by the product of marginals on each subspace, called block. The framework of the bootstrap PF algorithm is kept. The predic... | i | 48fc0455bd7921c387b5c8db2e277e74 |
The second advantage is that if we know that the optimal policy is monotone,
we can search for them efficiently using monotone value iteration and monotone
policy iteration {{cite:990738f23584060e85c3ccc37e5201beefd655a9}}. Our counterexamples show that
these more efficient algorithms cannot be used in energy-harvestin... | r | 49db1ab2b783e63ad5569f130e5342c0 |
The main interest in exploring the supersymmetric extension of theories with a
potential with an odd number of fields was to ascertain whether the {{formula:e37c55e1-0e74-4be4-8334-eacc29b31cdc}}
emergence of the non-supersymmetric case, {{cite:1d88388ed3723715d50e49f51ac1478a2a60f9a6}}, was maintained. It was
not sur... | d | cdd3ba4a1014ad82e1eee9c83fbca207 |
Furthermore, a better experimental and theoretical understanding is needed of the basic settings of neural networks to support computation in time-frequency domain. For traditional real-value neural networks, researchers have good intuitions about the basic configurations of initialization, activation functions, dropou... | d | 6cc76d3a6b612a17618c2195851c28ec |
As comparable methods to classify graph sequence data, we adopted four methods.
The first is a method using vectorised basic DMD{{cite:570f0be088259badd7d7c581282967d069e62519}} modes (denoted as DMD spectrum) as a baseline of DMD approaches (the selection of the elements was the same as GDMD spectrum).
The second is t... | m | debd2e7afe07fc125856d1ae7240206b |
Second, the estimated values of the transfer gap {{formula:ffe42bec-be96-4b52-8751-8dda0a9010c2}} suggest that adding complexity to data is usually beneficial to decrease {{formula:d5455a9d-1457-4508-a8e4-4b41b574b3c9}} , but not always. Figure REF (right) shows that increasing complexities in terms of appearance, li... | r | 73743e7d92c42c7c6d6a64adbca5d116 |
The proof of Theorem 1.1 and Theorem 1.2 is based on Theorem A, the
mountain-pass theorem without the Palais-Smale condition
{{cite:343b546bb0b97a82bc837c245f3ca8254d871041}} and the Ekeland's variational principle {{cite:46c778b464052aab6081043482412bc5ce2f5488}},
which were also used in {{cite:713e88cdf000014d59902b6... | i | 32e320be8ef89c6e0948f97e5ba679f3 |
As a key observation, we pointed out that a multi-cluster pattern emerges if we leave the global constraint of maximum number of living individuals and replace it by a locally applied restriction. Despite of the fact that we used a numerically demanding off-lattice simulation, the resulting pattern can be described by ... | d | 2a8162887cd0acf64b6ba75c6cf8835b |
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