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The involvement of the entorhinal-hippocampal complex – as being the most probable candidate structure underlying network-like cognitive maps and multi-scale navigation {{cite:09f5c5f37279194f59018400894875ca87987f17}}, {{cite:c870c59060ec72d17c6282f484a3b83071e5f538}}, {{cite:309964176b56eaaf3a9e95415bdffd0dcbaa27fc}}... | d | 309a12a32981abf8fc39518724ee1dd0 |
Imagenette Data Set: Imagenette is a subset of the well-known ImageNet ILSVRC 2012 data set {{cite:384d05b431a52165342db5abe71d9b136e8e03c7}} with 10 easily classified classes. Images were rescaled to the range {{formula:d0043e0c-1e6d-4490-8f95-33be9bf26855}} and normalized using the mean and standard deviation of ima... | r | fcb395eba56926ac0b009481d7f774d0 |
Inspired by the Contrastive Language-Image Pretraining (CLIP) {{cite:09f5e733c2101a639190269adc3d629bf35058e2}} framework, we propose
Contrastive Surface-Image Pretraining (CSIP) framework which utilizes dual encoders trained using a contrastive loss to encode imagery and above ground level (AGL) maps into similar late... | m | 3348d29b2463ee38f969ad1b54b02c85 |
Finally, we compared the Color Magnitude Diagrams (CMD)
of our disequilibrium and equilibrium models
against MKO observations of brown dwarfs by {{cite:a68b0c44567072dce91884472751462550065f4e}} and Spitzer data from {{cite:70ce6de98ad7a28b6d0423c4acc380acb7b383f5}} (Sect. REF ).
We noted that our disequilibrium chemis... | d | b6b9dfbc6eb2e05b511569d0ae417cf9 |
We provide the results from ImageNet {{cite:759127a7aebabd848c528e1abdeb04cb1753ec6a}} {{formula:bc5f5a03-72f4-4e17-9dab-6d9f82d812ad}} conditional model with our method in Fig. REF , and the results from LSUN Cat {{cite:83defd1b84ae7eac1f68f954415b298c70ca4cb2}} with our method in Fig. REF .
Also, in addition to the ... | r | 4e9e1ca4a794df323d8d1f9bbb6d443e |
In the counting of pairs, the weighting scheme is applied to both
the data and the random samples
(except that we use a 10x larger {{formula:6f579d92-b85b-4e56-bdc4-c9762088511f}} in the random).
This treatment is different from what adopted in {{cite:742c0faa4feed361506d3c4e26b581acf990e5b5}}
(where the authors fixed... | m | 49ce5f208122e98199f73be80723c42d |
As shown in our previous work {{cite:af31ec7f1caffb39cb83ca3406dd33dcb552516b}}, the simulations shown in Figure 2 where implemented as follows: Numerical parameters were set to the known properties of aqueous
glycerin, air, and sand ({{formula:164c4104-1b97-4aff-8524-87a86e9c75ba}} , {{formula:24d12279-1f04-4103-b8b5-... | m | 12ea47cd0b10e613156a07a6af18137c |
We use CuBERT, CodeBERT, and GraphCodeBERT as encoders of the input sequences. For all of them, checkpoints for input length 512 are available. GraphCodeBERT allows an additional 128 tokens for data-flow information. For CuBERT, a checkpoint for length of 1024 is also available. We experiment with all of these. For spa... | r | f0959d72ab534893d3ac92f5a6417713 |
In recent years, distributed optimization techniques over a multi-agent network have attracted considerable attention since they play an essential role in engineering problems in distributed control {{cite:56368a4e3a619fb2e707ced21eda384885b14b37}}, {{cite:d741e35956a54a5ae4c7552ae073d9f2a7e2e87f}}, signal processing {... | i | b3b4c03b3c18aa104fc9bcba12053352 |
In recent years, the convergences of the field of AI and neuroscience have brought new ideas to each other, which lead to interesting results. One of these ideas that used the concept of the TD learning gave new insights that interpret the results from recording place cells of a moving animal in spatial navigation task... | d | 28c663f305aeffff627da2bffbc9a805 |
GSD vs. Multi-Pass Models
Bayesian MCDO {{cite:2cba2c91645e63d70ea3864e31e511bbd1524c82}} and Deep Ensemble {{cite:bd4ed2603c36bbfc9aae5c9600d4a3f1d7902764}} are considered the current state-of-the-art methods for multi-pass calibration. Bayesian MCDO requires multiple passes with dropout during training and inference ... | d | 3358d6169747ba1fcd85ed7b9fe004d9 |
We also note that system identification is significantly more challenging if greater numbers of candidate bases are retained. In this case, stepwise regression is roboust as it iteratively eliminates inactive operators instead of attempting to find a solution in “one shot", thus mollifying the problem to a degree. Giv... | d | ab42d03c87c1df4f37d3009b94e98697 |
Qualitative Comparison: The 4× and 8× qualitative results are depicted in Fig. 4. The top two rows show the 4× visual results. As for Bicubic interpolation, we observe that its results contain over-smooth visualization effects. SRGAN {{cite:e23d2e5ef0e8c4f0a0ee13d05796fd5da273aadf}} relatively enhances the SR results c... | m | 562a547475dc9f33c0244a3e80b177a8 |
Several previous studies {{cite:d17d768fe8f432877e87b0c687a08e5e66b8e455}}, {{cite:995456811e4f49bdb0e064f011762483c68b6716}}, {{cite:7bc148f679018cedc6715392cae12389d471cf86}}, {{cite:1743680d6b97722352de6626550452d292e0ba1d}}, {{cite:bdfe8931b1686a971a996c41179806f5103d71aa}} have achieved promising results on self-s... | i | c86218deb24c7303b13cb98bf0f1ff2b |
Several recent works have proposed data-driven path planning models {{cite:fe4b1952ab4c474b24158d1541be8881091b9fd5}}, {{cite:17be8f45ad422d1eea0c5223d0ada37751ce6a2f}}, {{cite:61f9bc4426e53e3c4646468eba6b9884d29875d0}}, {{cite:080ba423a401a0755cb6f535c3866dc6d3330dff}}. Similar to how classical algorithms, like {{cite... | i | ad35ebcb0f632c4aa1746d20f469db29 |
MIDeepSeg was also compared with several existing interactive segmentation methods. In 2D cases, in addition to traditional methods like Graph Cuts {{cite:f99cb775214a4cc42aff78c68957cd58be0a0593}}, Random Walks {{cite:548d3891b2cac70ae652653fa811f3bba1ffedf4}} and SlicSeg {{cite:c8fcf2316759d6cd6093c835cf65efeae61125f... | m | 8575f1ecb4106964e7b5ac062e9bed8e |
On the BraTS'20 unseen test dataset, our method achieved higher Dice values compared to the validation Dice values, obtaining an evaluation score that was equal 5th highest and 10th place in the overall ranking in the challenge. As an indirect comparison with existing methods using different validation datasets, our me... | d | 6750b12db55986cccd34b4fb83771519 |
We note that the lack of new detections is not unexpected. Firstly, signals from binaries with the inclinations targeted by our banks are significantly weaker than those from face-on binaries typically detected by existing searches. Secondly, in contrast to existing searches, we have performed a two-detector search usi... | r | 4cdab5625d39bd1c28794d2e1b1f11aa |
Task specifications (and non-Markovian reward functions) are naturally separable, so an automaton (or reward machine) allows an optimal policy to be found more efficiently by breaking down the complex task into a sequence of Markovian subtasks. Nevertheless, recent work has recognised that this automaton is usually a p... | i | 9f19cca831d238a34c6e6aa742526160 |
We provide a probabilistic interpretation of attention as a generative model for queries and values through a set of memory units. Using this formulation, traditional attention in transformers {{cite:4589594e0684fb5479af533c27dbbccb74130d9d}} reduces to the special case of maximum a posteriori (MAP)
inference of values... | m | 8751cb5734e6fbb6a7589114f00aefbb |
Denote {{formula:72782f45-d138-405c-9907-84a5c1689c6f}} the communication time need for each master-worker exchange. For simplification, we assume that {{formula:416c070f-4703-42e8-a9ca-8d9a994006b9}} is fixed and is the same for all nodes. If the time needed for computing one update {{formula:5fd5965f-e68c-4ad4-a6b9... | d | 2c69fce4fbd15a8d1304ef5031823abb |
(4) Applications. Currently, the only direct applications of FSS (I2S and S2I) are entertainment and law enforcement {{cite:dab2fc63e01dad1bd6a65798383debe9e3e12504}}, {{cite:4dabdd942ae811145568be5cd04ea8e49e0c4f52}}.
With the development of FSS techniques, many other promising applications could also be implicitly or... | d | 44d5ffc0940a7a9ed6b567480551f18a |
First of all, we extend the analysis of the {{formula:bc191cde-bf3e-4a32-a8a4-8aa58de6440d}} , as already shown in Figure REF to the results reported in {{cite:2c8aa9cf9d9a216614e1b81d3d922b6680823a4b}} in Figure REF . Here, we can see that of all the countermeasures proposed (for CIFAR-10 data), only {{formula:026279... | d | dd06b06f94c2a3a94b64874f49aa679d |
Assumption REF (A4) gives the following analogue of Lemma 5.7.8 of {{cite:b27d91d5b59172726f0365ee10b0cb5f597c5017}}.
| r | 020b488615fb0bf14dbcaaa4c6016313 |
At SBC ({{formula:6262808e-f90f-423f-8a4d-bf3df86d6795}} ), the unstable modes that degenerate owing to spherical homogeneity emerge simultaneously from the static thermally conductive state over the critical Grashof number {{formula:8833a9bd-9f56-4c4a-8882-6be3062293f9}} .
Owing to the nonlinear interactions among t... | r | 6e2ff70e7e3f8dc5174f93209492f1d1 |
By an application of a concentration inequality for Lipschitz functions of Gaussian vectors, we have for any {{formula:a0d268fb-ab9c-458d-a43c-e7b82239a2d4}} (see Example 2.28 of {{cite:c6bf4948f6d3f52680b23bd905cc1012f1e61637}}):
{{formula:14f02842-4b19-4597-9423-976d19d8469c}}
| r | 6de0895c6957628bd8495e86be2b0324 |
There are two main branches of adjoint methods in use today: the
differentiate-then-discretize approach, which derives the continuous
adjoint equations from the state equations and discretizes them separately,
and the discretize-then-differentiate approach, which discretizes the state
equations and derives a discrete a... | m | 285534bc013b6f16ee857337c66b6469 |
Fast and numerically stable uncertainty propagation has numerous applications {{cite:7486664e7618dbd3e9aed820de6be8b63f1011dd}}. We could use it for selecting the next best view {{cite:32fc09b4cfed70b66a7c2c9148a3e86175f1d3bb}} from a large collection of images {{cite:e75f27cc35fd0b6074037e0425bc97d896747afa}},{{cite:2... | i | 75799104a495acbdb7d863b41cd0385e |
In this section we present the performance of our estimation and uncertainty quantification algorithms for images of single edge dislocations in single-crystal aluminum. As described in {{cite:5122a048de4b307140faf522cf40aa2b5fb81e69}}, the displacement gradient tensor field {{formula:2571db40-b843-47f4-8328-bc10df94f8... | r | f3ed34a7565252cfa279c0d356cb43b9 |
Theorem 2.1 (PPM, {{cite:ad8b9dc4cd0cde401ac935f2e4926bd296041cff}})
Assume {{formula:60c879c0-c20f-4225-88d0-85cc518565da}} is {{formula:1ca2b53c-ec14-46d7-ac1e-637b03f81c43}} -convex and continuously differentiable everywhere. The (PPM) converges at the following rates:
| m | 1f2b9b153783a1a05d5ff0c091d8afbb |
Unsupervised representation learning has been highly successful in the domain of natural language processing {{cite:4daa0bd54b0811d6851d1b9e80b9b5326d663e87}}, {{cite:fc09a861e8e130c946baaa45e5a73a17d9f0f17e}}, {{cite:6d0eacd6812e8e83502b8c0bd1ecf36e7016c1b7}}, {{cite:ebbf12bf6cc2d1a353813d0d90c39659edc81aaa}}, {{cite:... | i | ed3a9b0bdd7c31e52bbf5c2c6ce8cf11 |
Recently, end-to-end approaches that directly predict trajectories from raw sensor data have gained traction {{cite:e3919a60aeae019bfab28266099da193968ad371}}, {{cite:c6633f90a6fe38630531cf3a5f3df2d87baeacdb}}, {{cite:831f050136bc6532117f1da8e3473ba6343ce16c}}, {{cite:fba906261cbe607fc7ed8ebdff208d4019a04adb}}, {{cite:... | i | 1eeba84fa1a64909995a95bf099488bb |
For practitioners we present a simple cost-benefits analysis using our work as an example. To achieve a roughly 2% improvement in downstream tasks over general BERT required 48 hours of in-domain pre-training. Another 48 hours of training led to a further 1-1.5% jump in performance. At a run-rate of $22.33/hour (for 8 ... | r | 89601c6bdcfe91aafa32072903978a74 |
In recent years, security has been becoming an attractive issue in the control and estimation of cyber-physical systems, such
as chemical processes, power grids and transportation networks {{cite:c7446f224816df41710f8a220c245bc3be1acced}}, {{cite:647c7ae3ec72d2056d18e7068b18b94a47445f45}}, {{cite:98f0e86d8b6b49eb741f44... | i | 89d2d01f31e0c3553293e0539e9b369f |
Parameters in different model scenarios are presented in the Table REF . The vacuum effective potential {{formula:e941347f-8ae6-4e78-878e-b3f5759fb644}} is a function of the two variables {{formula:3deadd7c-f808-4595-bd20-9f903783b099}} and {{formula:b6f563d8-33ad-4402-8749-a7fc2a24fe6c}} . Its minimum is located at ... | r | 56d597382b6479bdd95362ef71a953d7 |
Coupled waveguides play an important role in numerous optical devices such as multicore fibers, optical directional couplers, polarization splitters, ring resonators, and interferometers {{cite:90249d00794d8a24d02302b2927d2d6ec86dad5c}}, {{cite:a39a465f7e28f37b74a030ca184db4ae3681b6a4}}, {{cite:e5507acc9a66010ff36323c9... | i | 7ffe2174e7a014b9fc78a7a1579c2fde |
Bursting behavior is known to be extremely variable and violent, and the bursts influence the accretion process.
To date, a significant soft excess is detected in most bright bursts which is caused by the interaction between the burst and the corona or/and disk.
However, in this work, this phenomenon is absent, or cons... | d | 6067631949aca3f5ebc8b6ff38f79f3c |
Furthermore, such a smooth translation between different modalities can allow us to gather, for instance, additional annotated data with no extra effort. The top row in Fig REF shows an original camera image from the Semantic-KITTI dataset. By using the corresponding LiDAR scan, our framework trained on this dataset c... | d | 5c58e5d57d2a51c104df16685bd2b1de |
In all three stages of training, we use an effective batch size of 16K. We utilize the Adam optimizer {{cite:e1bce8fdebcf9b5b0fca995fc4da49a57642b1c0}} with weight decay and set peak learning rates as [5e-4, 3e-4, 1e-4] for three stages respectively.
We train up to 30 epochs from which the best model is selected based ... | r | c0b4f2c5f1d73ab3d581c9b657a9641a |
The mid-infrared wavelength region (MIR; 3-20 {{formula:5d126efc-0c2e-4747-b830-674e47b6582e}} m) provides an excellent opportunity for the search for life, as it contains features for multiple biosignature gases, as well for gaseous species that could provide evidence for or against biological O{{formula:6b7f4670-223d... | i | d1f1f51ef1b6211a0db9097993e20e93 |
This fact can be seen by adapting the classical reasoning for the finite element method to the spectral setting (see for ex. {{cite:b53cfc63268c1bba9ae3e587f92e1fa26c329f4b}}). Moreover, using inverse inequalities (REF ) and the above accuracy estimates we can show that for {{formula:8a1047f2-7cfd-4edf-905e-13ebd137a44... | m | d07e444dc603824e9de0956172d21122 |
According to the idea proposed by Leon Chua {{cite:8d0b714033d48e212c279e0e28a3b9903bfdb11f}} the memristor relates the transferred electrical charge, {{formula:58e89e97-f09a-47e0-a409-2bcd47cf5e03}} , and the magnetic flux linkage, {{formula:9291fbcd-73f4-4c60-9d75-256bd3f2c3f6}} , by means of the linear relationship ... | m | 91233b9cd30ba7140ad15bd9e283949d |
We have proposed herein a model to describe spin-half quantum particles in curved space-
time in the framework of quantum field theory. Its novelty consists again in assuming that the
Einstein equivalence principle and general covariance hold for spin-half quantum particles. It
is not a self-evident assumption, because... | d | a73a42215a8584b44015e7de3e47709c |
We refer to {{cite:c5664d037ce88e22c75638d423a2a641eb12c99a}}, {{cite:4e668ca4284af48e814c717435b0b1c0cd15b79e}}, {{cite:6c38a7e2ccdb6367f223a682ab2358dfb3106c96}}, {{cite:e544211af5a16f53dc723e09a777463304d0e592}}, {{cite:3fb3a2ec779d606623ef9eb11e1a0ecd83683828}} for several sufficient conditions of then comparison p... | m | ed0733f39b8fc2f5f28a72ccc14956df |
Before synthesizing pseudo-bursts, it is essential to align the input burst frames (having arbitrary displacements) so that the relevant pixel-level cues are aggregated in the later stages.
Existing works {{cite:ea183df28cb67b8557db6f5d6735699a439aeb20}}, {{cite:5d4b53a008b5201a1609b4c7cb509c91ab495356}} generally use ... | i | eddb40d0f1712f078c09d334fdbccbba |
where the equalities are understood for the expansions of the rational functions in {{formula:90dcb752-7406-4fba-8fdb-28b4c65afd39}}
as series in {{formula:dff10573-a151-44e8-a71c-ab724d3a9248}} and {{formula:b7a0c51d-f85a-4a24-8f39-b0ea8911aff9}} , respectively.The roles of {{formula:01d81e98-7a21-4305-81da-1ff97f47... | r | ace9ca4f5fa681ec5f38f96a510a0d33 |
We use PAWS {{cite:dffbd0556e4aef5888084b212298a9ab6f641d1b}} as a canonical example for this family.
A key difference from threshold-mediated methods is the lack of the parametric classifier {{formula:8a4c9d4e-2450-4635-a2e7-00a7fadc29f1}} , which is replaced by a non-parametric soft-nearest neighbour classifier ({{fo... | m | 4a26763b953155a56de7d9c7e19cdbbd |
Since Assumptions REF and REF ensure uniform injectivity of {{formula:d2fd31f7-b5ab-4776-a68e-773c4512a80f}} , and {{formula:cb522143-f3ff-4c08-98dc-87039cc4552d}} satisfies the PDE (REF ), the inverse {{formula:67060a15-ada2-4136-8797-9c35240406b1}} exists and is unique. Thus, the data samples used in the training... | d | 24e0beb8e2682df6210da8d87a6bd929 |
In this section, we recall some known results on the simultaneous dimension of graph families and the fractional dimension of graphs. We begin with some useful observations. The open neighborhood of a vertex {{formula:4e002920-47f5-471e-bf0e-f01777f5d29a}} is {{formula:9a5d1baa-304e-4aa7-9464-a5b90a1650f4}} . Two vert... | r | 05487865961a9ea745c7b684197f4792 |
Ground truth for data-driven approaches.
When applying data-driven approaches to map light-field images to high-resolution 3D volumes, the ground truth can be given by other high-resolution imaging modalities including but not limited to confocal, light-sheet and multi-photon microscopy images {{cite:2bc6c99fd37f387836... | d | 9edf90fbd41fba480b1e1bb5c4af41e3 |
In this work, we learn a task-agnostic head (or joint head) and propose a new evaluation protocol for class-iNCD (see Fig. REF (b)). In details, we first use the new head to estimate the predictions of unlabeled data from the new classes. We utilize the HA {{cite:36f0372e630be02e3800754679f65e063817f142}} to re-assign ... | m | 18f24791b77ae9cbc03cb3f77181ccc3 |
Our proposed Point2Seq shares a similar intuition with the concurrent work Pix2Seq {{cite:a08e2c5718dbc35c8943ebffce33e63dffc63222}}, which is proposed for image-based object detection, in terms of leveraging objects as words that can be read out from a feature map. However, our method is intrinsically different from {... | d | 3f55fed90567a32ae54a8abaf3a553dd |
PSO with AAR fitness: The PSO method is performed by using the objective of problem (P3), i.e., AAR, as the fitness function, where each iteration requires all generated channel samples to evaluate the fitness and update the particle positions. This method serves as an upper bound for the proposed mbs-PSO method.
Sum... | r | f74bc3b2d2c461e99ab30b3f2ace06a9 |
In order to prove Theorem REF , we need the following two lemmas. The first one is an immediate consequence of Dedekind's theorem. The second one follows from {{cite:8422a01d8afe35696e0ff70726293853c9a87f93}}.
| r | 312c7074674c6b444a8a9d7a8e40959f |
Another possibility is that our dataset (410 students) was simply too small for complex deep models to find success, compared to the 1000s or even 100,000s of learners in other datasets where DKT has been evaluated. However, model complexity alone does not explain the difference, since the simpler BKT model did even wo... | d | 5afc65648229a2d0c82c25c38784a9dd |
As with Furstenberg's conjecture, Theorem REF
has also valuable consequences about expansions of natural numbers.
Let us first recall that a set {{formula:bee4bcae-a5d2-4128-80bd-67987c7b77ac}} is {{formula:6e45766b-0fb3-46d6-a6e7-e5435bf67899}} -automatic if its elements,
when written in base {{formula:6fd7038b-9942... | m | 5146e7201503dab9f8a1f408bbdc33b6 |
To address the aforementioned issues, we propose Contrastive Learning for Local and Global Learning MRI Reconstruction network (CLGNet), which is composed of spatial branch and wavelet branch, as illustrated in Fig. REF . Firstly, according to the Fourier theory, point-wise update in the Fourier domain globally affects... | i | 071ecb55f977853b63883ef556321473 |
We note that WideResNet {{cite:62035234a9ca38350c165b79c1ec3e146da81a4b}} used in prior works is much deeper and computationally more expensive (with {{formula:588ce0f0-40a0-4bfc-8ced-772be750d2df}} million parameters), whereas MSDNet with {{formula:51e08174-0623-4898-9207-4a47e8aefd5c}} million parameters achieves s... | m | 7cd9198df58ab96468404e5352ba1231 |
These methods attribute importance to input features/signal dimensions for the output i.e. how much the signal dimensions/features of the input contribute to the output across the neural network {{cite:8116aa31672b486d9d3679740fc0e697f74d4910}}{{cite:d9b86876013b02b5c31235dd9351b21b3546920d}}. For a linear model y =w*x... | m | 9f89efb73620a9c7dd1381f5f8eacdc8 |
We use the notation
{{formula:b2374f23-78ef-45c4-9098-1b9dde7cb5fd}}
By Markov's inequality, Theorem REF implies that there exists a set of the form
{{formula:92058e83-9f29-417e-9a25-05bc07896982}}
with
{{formula:43806c67-ca17-4774-8ac6-7b4a53936048}}
Since the distribution {{formula:003a2521-e0d2-49d3-bc95-c434401... | r | 67c4793a228ffea172cb8c677c684b6e |
Astronomical observations over many length scales
support the existence of a number of novel phenomena, which are usually attributed
to dark matter (DM) and dark energy (DE).
Dark matter was introduced to explain a range of observed phenomena
at a galactic scale, such as flat rotation curves,
while
dark energy
is expec... | i | 9134f92847d3a0399fe9cfbedfe9e142 |
First-order: Some first-order methods such as extragradient {{cite:766aa58bbd25f64c4013866ca4dd96a391d6ba84}} require a second, costly, gradient evaluation per step.
Similarly, methods alternating player updates are bottlenecked by waiting until after the first player's gradient is used to evaluate the second player's ... | m | 4cf86362d9e8ca9ac4fe648b5f0cda31 |
where {{formula:fc3ecb1d-a712-4e67-b970-f5ded2212036}} and {{formula:29ada17e-f95e-451b-ada3-3e96807cceb8}} are borrowed from {{cite:11066c6bb9090ef60c42551622151c8f1852e6f5}} and {{formula:e933fd7e-f0e2-4504-a887-7eb06614e672}} is the adversarial loss discussed above. {{formula:7a382052-35ed-48d1-b31e-1c676f2e5dc9}... | m | 80bebd2331fb35bbf33959d2e16b35d8 |
We carried out Monte Carlo simulations in order to understand if these {{formula:67cca974-8fe8-4125-9121-a27695f71d15}} corrections can explain the amount of reduced scintillation photon collection in the solid phase. Events from the decay of {{formula:b87ff9ae-4409-4545-a30e-ab059a715aea}} Co are simulated using a de... | d | 5f6be16ef67256edc669bea8343403f8 |
In order to test our proposal, we use 7 video sequences in UVG dataset {{cite:e0efd8ba4b56b9830e831ada83bfacb9f2b3b7d7}} at 1080p resolution. We use 10 set of consecutive 16 frames (total 160 frames) of each sequence and compress them by compressai {{cite:577ba868010dcc550edbd77eb3928c6dd068ed41}} implementation of SSF... | r | faffca047494807a426c5b5ba34553a8 |
In general, the approaches to image super-resolution can be classified into:
single image super-resolution (SISR) and multi-image super-resolution (MISR).
Single image super-resolution has recently attracted considerable attention in
the image processing community {{cite:8c75c3b4c0573f223e6fb70eb9c1054c7aec68bc}}, {{ci... | i | c4f1d5cedb4cd75050ab34a0030bf927 |
Approach 2 - Fair meta-learning for segmentation: This strategy aims to add a meta-fair classifier to the segmentation network to train a model that not only segments cardiac MR images but also performs classification of the protected attribute(s). As the classifier we used a DenseNet network {{cite:28d12106c97f48bec4c... | m | 8e8ee15f204eeb99505e831b86f8fe2d |
In this section, we verify that Open-sampling can boost the standard training and several state-of-the-art techniques by integrating Open-sampling with the following methods:
1) Standard: all the examples have the same weights; by default, we use standard cross-entropy loss.
2) SMOTE {{cite:6a46e47700b85bc1d47c629a9bb8... | m | 8e123e11aed15cbc55f33f046f6219da |
The sensitivity of our observations towards TMC-1, between 0.19-0.35 mK (1{{formula:308573aa-6062-406a-832f-b6e832e77d07}} ), is much larger
than
previously published line surveys of this source at the same frequencies {{cite:c3274b5e794d203144ac1f2a3d9d662dc27ca5d4}}.
In fact, it has been possible to detect many indiv... | r | ecda31470bd2884cbd93022b005376a2 |
The RL algorithms which have achieved state-of-the-art results on various robots rely on deep neural networks because of their good approximation capabilities. With advancements in computing and auto differentiation frameworks, there is a surge in the adoption of neural networks in the robotics community. They have eve... | i | c55a69da3150bc187a66c6cc15d3477f |
In this paper we have generalized the factorization proposal introduced in {{cite:e3c88cee83d65ed0c65b523852263494a85ab425}}. The main idea is to decompose the observables into the self-averaging sector and non-self-averaging sectors. We find that the contributions from different sectors have interesting statistics in ... | d | 7aba51ff13702a14a6a9b1479ba6e6fc |
(2) A Hom-alternative algebra {{cite:b71c39ba690fb5a4e64bf881888e8093adf97121}} is a multiplicative Hom-algebra {{formula:6a4944c8-246e-4b27-ba96-0a77b3026b68}} that satisfies both
{{formula:93402e50-16fe-42b5-bbff-056cdcd9fd1e}}
| r | 9e799f886cbc218b99c47868dd02f1c1 |
We next investigate the performance of our joint graphical horseshoe estimator, jointGHS, through comparison with the Bayesian spike-and-slab joint graphical lasso of {{cite:ba10c30db8945d6de15ec91dc23b06a94df6f129}} and the joint graphical lasso of {{cite:4dc72530cbf68b64a670d9dfbe3b3f8518aa56a9}}. We restrict ourselv... | m | 872e68ea7f4d8211631ff66f36157245 |
A naive way to utilize these labels is to design a multi-task learning process {{cite:d28b8f09d7e91f6d74dc1789bfad1ceb725cf1fc}} that directly combines vanilla unsupervised pre-training losses such as MLM {{cite:e898fb4a9217608dc0b9684d2bd1a95cda6391d0}} with a supervised DA classification loss.
However, this approach ... | i | 2c711b9c077313a49a7dba7a41558499 |
where {{formula:89890577-2b99-4bdf-81c7-337a06d132ad}} such that {{formula:51eaf7f9-86c2-4dd7-a7b3-d4297ecfc682}} for each {{formula:d985bcb7-12a7-4a7d-8ec9-fb7e7ad014ae}} . Note
that the model in {{cite:8d769ff5da6fde1aa96df669f68dc85f83fe9f70}} also contains a bias
vector, so that {{formula:189d2e8e-0d53-48ec-b3ae-... | i | 5659b203b257dde0050eada0dac21275 |
In this paper, steering wheel angle prediction is performed using a two-stream deep Convolutional Neural Network (CNN) {{cite:f5778c82cbb1ad89668e800f152f72e91c8535a7}} {{cite:d633e987c9539b590579d44cacc6db08e0aaa2ba}}. The architecture is described in detail in Figure REF . Each stream in our implementation has two co... | m | 61958a8f901b719ba6fb1369181eec01 |
We are interested in extending this research in multiple future directions.
Despite using a decentralized trajectory optimization framework, our experiments in this paper were performed on a single desktop PC, and commands were then transmitted to each UAV individually in real time.
We are currently working on a new mu... | d | 7306f3ed630b9ab55c5ea0aa73520b2e |
In CivilComments-WILDS, we divide the data into training, validation, and test datasets and maximize worst-group accuracy in the validation data (and by association, maximize the average accuracy over all domains). Then, we perform model selection and evaluate the OOD accuracy on the test data.
For Amazon-WILDS dataset... | m | 29b50355757caaf7917559c5de255070 |
The formalism of the {{formula:057315a2-8609-4ce5-9ab1-35da429902dc}} -modes {{formula:5273ab8f-3c0d-480e-8d75-409b5b5395be}} or the “reparametrization modes”, discussed in the last section to obtain the {{formula:e04d6d8a-a3e8-4f32-a776-98494d42cf1b}} corrections to the 4pt functions in the JT theory have since been ... | d | f670375817cbb1fbcde87beeeacd094b |
Principal Component Analysis (PCA) is a well-known data dimensionality reduction technique {{cite:b390639706ceb056407e79fe0db527fb8c436c36}}. It works by projecting a dataset of {{formula:56d942d6-04a7-46de-9ab3-1f098ce52ae6}} vectors {{formula:925d197f-bbbd-4a47-a478-abc57e11553c}} , {{formula:e7817ea7-a867-4ef4-90d8... | i | 9a0f4c4c1ad0d4d3586368f98edeede6 |
Since industrial machines are operated by humans, learning control strategies by imitation learning {{cite:5202a36248e0f64be6060f12ea52ddd04ede901c}} may be useful for a wide range of applications.
If the applied task has multimodal state transitions, its policy model can be extended by multimodality or robustness {{ci... | d | 4827cf4d3f590f746d077a99d29ba431 |
Motivated to generalize Artin's Approximation Theorem (cf. {{cite:7963fde16935a0972829e43201ed64610d256beb}}) to excellent Henselian
rings the third author developed a powerful tool, the General Néron Desingularization (cf.
{{cite:ad19ed1e7f9b5b134925e610a2d05449def909a3}}). This result was discussed and used later by ... | i | e6809012ccdaff53577e796392c471a4 |
One important point shown in section is that also the irreversible islands-in-the-stream formalism can be captured by a remarkably simple quantum statistical mechanics model involving a temporal sequence of unitary averages. One can view this as a nested generalization of Page's theorem. The fact that ensemble average... | d | b654a36bdae17b3a6be29e105a0ef016 |
where {{formula:6c1a1766-a362-498a-a3cd-0b43edb84cd0}} have the disjoint coherence support, while a strictly incoherent operator has the form {{cite:32a5eb1b7cf535247de18d198ff43588621135c6}}
{{formula:00a3a91b-a19c-43b5-9728-5149870a5f5a}}
| d | feb65072fc9a23e38090826f410bf219 |
This is a paper about geometry of variations. We formulate definitions of the objects and structures which are cornerstones of
Batalin–Vilkovisky formalism {{cite:a62926e61002d890f4319af8d331e56c095c32a8}}, {{cite:5db1eabe16e1e4726f7bbf0789079a031719c588}}, {{cite:2588b9a01e8b34a23e70a6e8bb0ac7f71b987718}}, {{cite:e37c... | i | 3cb9d45e2ff38afdef883482793217f6 |
As mentioned above, directly finding the solution of CSP is infeasible. However, as developed in {{cite:ef7215a502de6207c139c5394d87509436fa4bdf}}, iterative algorithms such as compression-based projection gradient descent can be developed to approximate the solution.
In this case, the denoising step such as (REF ) in... | r | b50de3fd39efe284aa0c49a168cf56b9 |
The computation of a primitive element of a field extension can be done by hand (if the degree is small enough) or with the aid of any computer algebra system.
For some of the computations we used the algebra software Magma {{cite:95600d751deac7d21de9315637994b3106193d67}} and Singular {{cite:2f4f7af3af6c9428adbf2ba2c1... | d | d7f3036e7082dc233e44c3ba435f3a5f |
The performances of the baselines represented side-by-side reflect the limitations of GNNs stated prior. Of all networks, GGRNet {{cite:21bcb1a3a3f0df455eace2cefe8a5c4653fe24ba}} performed the worst, ranking even below the MPNN {{cite:4dad8920cf985e9749e2c5e8bc15bdd142cec4b4}} implemented by Flam-Shepherd et al. {{cite... | d | 387a37cb5eaee885614bb627ad317201 |
Overall, prosocial agents underperformed their selfish counterparts, but the picture is nuanced.
Optimizing for per-capita return can be difficult because it complicates credit assignment, and creates spurious reward “lazy agent” problems {{cite:43633327218145ec582cd03af32ea1f878a306cf}}, {{cite:3d4b2e2d05669542e3f11f4... | r | 763d91d6ae1458b984306ab42199bb2f |
Our goal is to organize the implicit storage of knowledge, to add target knowledge (yellow box in Figure REF ) and anchor to select target knowledge.
A simple approach is to train updated LMs from scratch; however, this is far too expensive considering the parameter sizes of recent LMs, such as 175B for GPT-3 {{cite:9... | i | 0051d9637555eaa8c766cc0ea7621d72 |
Notice that Proposition REF does no exclude existence of Turing kernels (we again refer to {{cite:fdcb4defe39009e7c51941a1712cee62dc9f2cae}}, {{cite:9197a9ea2555b4f0a2ec0046a87b131b1ef1cebd}} for the definition of the notion). This makes it natural to ask whether Elimination Distance–({{formula:7c35c3be-3e54-4a86-95e2... | d | 0dc6fd06717940114b992d2bd59a6a55 |
Interestingly, the ({{formula:3953c45d-d781-4bc9-98a4-24ef333bfee8}} )-surface (Fig REF ) has basins and the saddle region arranged along both directions. Conventional wisdom would have led to the conclusion that {{formula:bf453a75-3476-486a-90e3-d9678016c4ab}} is important in defining both reactant and product basins... | d | 87e856d2cb35dbea825f2ccd945812cb |
The electromagnetically induced transparency (EIT) phenomenon{{cite:dbfd70f742804ea8e2fe913a928efcfb9b647f7b}} is another important quantum optics effect in the driven three-level cQED system, and is closely related to ATS effect {{cite:5f59e593cdac5589d56c4c1d591566cef00a65b8}}, {{cite:b6e09298f88272520b4866602256853b... | r | e7486bb46442f0d6f29c95313069c544 |
Also noted that SetRank outperformed the baselines of DLCM and GSF, in the cases of both with and without the initial rankings. In Istella dataset, without the initial rankings, SetRank{{formula:e31ab979-8f8a-45a0-842d-f4963d03e378}} improved DLCM{{formula:1b89c624-f0d1-479c-9bc5-da7232621fef}} about 0.05 points in t... | r | 2ba5c6f178f16e7c72690115fc8f590f |
One of the limitations of this work is that we assume knowledge of what TG in the set to use during deployment. In future work, it would be interesting to eliminate this assumption in order to autonomously and efficiently select a suitable TG using Bayesian Optimisation through Intelligent Trial and Error {{cite:5064e2... | d | ac310fcefb17c03ce7b41ac4fea66f25 |
The baseline demonstrates that the DS problem exists for all the 5 anatomies. The difference between the Dice scores on the training and test distributions is sometimes as high as 60 Dice points; a model that provides almost perfect segmentations on test images from the training distribution can potentially provide co... | r | 569e158f25809ebcd81abb9f934dad0c |
In addition to previous work {{cite:88d7438414aa238decb2aa563df331c485436a8f}}, in this paper we have also considered corrections to the entropy which are logarithmic in the black hole entropy and the shift in the black hole entropy, the latter arising from the backreaction of the diary. This refinement is necessary in... | d | 495425e55ba4b4eb8babaacd3780bbdd |
where {{formula:84975781-1480-4b57-bc42-eb06fb872c3e}} for {{formula:4f332e46-4109-4c00-987d-124d8ff4d75f}} and {{formula:2100f46a-28e8-46c3-a6b7-b68c7739c889}} for {{formula:32dd4663-b9e4-4ecb-ba3b-937f906d5a82}} , respectively, are the regular functions. Eq. (7) can be further written in terms of scaled magnetizat... | r | af69f9282c82e588f77df7c204d22856 |
Although the Standard Model (SM) is successful in explaining most of the observed elementary particle phenomena, there are at least two evidences which hint new physics beyond the SM, such as nonzero but tiny neutrino masses and the dark matter (DM). A simple extension of the SM is to introduce an exotic {{formula:a5a9... | i | ef0d69b32d9b001565da9f76927db7bf |
As any other study of this kind using metallicities obtained from Hii region emission lines, our results depend on the calibration of the strong-line diagnostics adopted. In our work, we have re-calibrated the {{cite:92f82d251e0e8a1e51d3bf4a18bdb91e9f0879b3}} oxygen abundances by applying a shift {{formula:3ef9701a-b44... | d | 884fc2fdd026cff667225b195acf22da |
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