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A new PUF using the priority arbiter called PA-PUF is designed. The PA-PUF offers a uniqueness of 49.63 {{formula:4f58365b-0761-4e44-931f-90c56780a03f}} and uniformity of 49.45{{formula:a0407075-f591-4240-b96d-0403b0d8ed1c}} at the output. The non-linearity in the output of the PUF is increased with the use of a pri... | i | de8a1e7b248d5a93e5610741ba55f4cb |
An example of a purely AI approach is {{cite:66734beaa8ea08e8985a41015265fe82d6f12e01}}, which evaluates the performance of an artificial neural network (ANN) on data from a laboratory test rig for an engine. However, it provides no understanding of {{formula:58d4ed89-c200-4aa9-ab00-f1b75bf7310e}} formation and had sp... | i | 72958dce1d5e7ec8600e81d44408e1cc |
As a second novelty, we propose the novel DeepFH segmentation. DeepFH extends the segmentation algorithm by Felzenszwalb and Huttenlocher (FH) {{cite:241c07c330f47a2eb0c18d0fe5b00190d8286d66}}, which relies on RGB features for pixel similarity, with semantically richer deep features. We generate deep features with a li... | i | 80d9f1f9978aba1d230ea5c469027951 |
MOT16 We compare PatchTrack with other MOT systems on MOT16 {{cite:b61da861df65d5c82470b40e509bd17167e026fc}} test set in private protocol (Table REF ), where PatchTrack achieves state-of-the-art results in MOTA, ML, and FN. Compared to LMP_p {{cite:f4ab442f5e5b597f26007afaed9957b414b57f5f}} and POI {{cite:97da927003d8... | r | c05a96c9e97cd8901ec4d736ffd2b60c |
The overall observational properties of SN 2012au show that it is a transitional event between SESNe and SLSNe I {{cite:e004e1bf3d8ca7a152f905399d88cd98866ecd6c}}, {{cite:5d803072cb91d443f2a80d91f3b5df49263bda71}}. Therefore, we also compared (see Fig. REF ) the linear imaging polarization properties of SNe Ib (SN 2007... | r | f6dba8c5522cf16fed50859f7471ff3b |
CTPN {{cite:143935dc198ca54eb1a6b677ee7a3589679b748f}} is a pioneer work that brings text detection into the deep-learning area, but it simply merges text segments according to a certain threshold and can only deal with horizontal texts. SegLink {{cite:57acc015606d2f35a9c4ccdec87f37f28b13898c}} is designed to detect mu... | m | be283e442fda777a3c2815f59e63dfe7 |
We observe that the Davis' decomposition is also valid in the martingale Hardy-amalgam spaces. Indeed, we have the following decomposition which can be proved as in the case {{formula:8a99b924-5f23-49d5-b7fb-5d5363d5e4f0}} (see {{cite:534428b852494c871cdb156b64110f4bf6fec525}})
| d | 8f0b941f41306269f19043366b5cc1f0 |
Variance of the gradient. Using a single domain discriminator also helps reduce the variance of gradient. Large variances in the stochastic gradients slow down the convergence, which leads to poor performance {{cite:2662ef1d8a0e467e964e5ab095e7cc425afca5ea}}. Herein, we analyze the variances of the stochastic gradients... | m | 5efaa115005039f0db3b593208aad340 |
Adapters are small bottleneck modules consisting of a down projection, a non-linearity, and an up-projection, with a skip connection (see Fig. REF ). The initial implementation {{cite:23cfccd0c024b67e8bb741ee60d078f212389f9e}} applies adapters after both the self-attention and feedforward layers. However it is possible... | m | 607b712c29d822ba4f6bfc27a2c98f69 |
{{cite:b6144640f729c6aff5d3e0d333435d485f170432}} propose some strategies for storing experiences in the replay-buffer that has been shown to reduce catastrophic forgetting in RL. All our methods use prioritized replay buffer that resembles the surprise strategy {{cite:b6144640f729c6aff5d3e0d333435d485f170432}}. In add... | m | ae3942424d4882d1cf82e02f19fe1a86 |
where {{formula:a83f335c-869c-4c80-83a2-4fef6baab4fa}} is critical exponent related to the scaling of the magnetic susceptibility near {{formula:38263b2c-6af7-4c76-9897-a9c439e61625}} , {{formula:acce19b4-1d93-451d-9ce4-6cebf2bfabda}} is the critical exponent related to the behavior of the two point correlation funct... | r | dbeed67a8f98f69348cd93f9959e21bc |
where {{formula:85c6cc4e-0501-4d4f-8134-f8372e091bef}} denotes the inverse DFT matrix {{cite:8d2ec61d4fb8bcc798c91812f78feb14c360e7fc}}, {{formula:39ac3abf-8d46-44e1-8e1d-e65abf5bf7a9}} is the matrix nuclear norm, and {{formula:b69be1da-0a03-40a8-82a2-6c916ffe4df7}} denotes {{formula:03226a1e-942c-4045-9b9e-4105045d... | i | e27f691909e575963e96ec1f2b8bfb21 |
It is usually assumed that the remnants of PPISNe are massive black holes
and no {{formula:6f65d6ef-5ead-4bcd-b536-430e3d317776}} Ni is ejected to power the SN light curve {{cite:f36d0fe5854a80197f7bbe884b1fbdf31a74b019}}, {{cite:e7da4650c3734e225362804573c5c80fceb4a716}}. {{cite:741de0b2c27cd87c887de8f1fbe88c8d476894d... | d | 1360bc900fd40d389ec1f4525bfb8e77 |
Arguably the first approach to the neighbor search was developed in 1974 by using a quadtree {{cite:1b260073ad91e4e519812edc9e4f4ff1c315a7a5}}, which hierarchically indexed a reference set {{formula:61fbf188-83fc-4cee-a996-316b33bf8b68}} .
In higher dimensions, a KD-tree {{cite:443ff3ee5553f6be8ba5f868529d5c4b8b136d27}... | r | ab0b2ed2d29d44a32a622890d55f090c |
standard NMF algorithm {{cite:410560ee5997e18f660a028c5072d556710f9f56}} {{formula:b53270ca-8f24-4cad-a7ff-958a884ba817}} LS,
original UNMF algorithm {{cite:797f1aecc39be052cfc31fd7105899e270373804}} {{formula:920215f3-6165-4d91-bfa3-7aa4bc018ef6}} D-U,
original BNMtF algorithm {{cite:797f1aecc39be052cfc31fd710589... | r | 0d3bef123854c2ec2dcd7e31878bd9fe |
The ARD procedure described above takes approximately the same amount of time as adversarial training. Adversarial training is slow since it requires far more gradient calculations than natural training. Several methods have been proposed recently for accelerating adversarial training {{cite:3fac2ecd395f1baa1ea6aa3430c... | m | 16ed359022dd52f0ad95276611739055 |
Variance reduction:
The variance of gradients is detrimental to SGD, motivating variance reduction techniques {{cite:a57ba8207eabf5c3245fe2b46c106319d218b50b}}, {{cite:62f6800bcdc198a07d396d24393f568cc5be6df1}}, {{cite:cc0afd8ae0fb94726c40ea1e0f2037f1481badf3}}, {{cite:c207d363476a08b3d15f58f411c3e2e0fa271107}}, {{cite... | m | f93821d8eb6e62c3f89f4078d3de9b5f |
Qualitative findings of the DiSCVAE handling four sequences from the Sprites test set are depicted in Fig. REF . The middle row presents reconstructions of these example sequences with swapped {{formula:1b69c6ee-6a19-47d8-be53-c6453f0d9e54}} and {{formula:9627288a-04ba-46e1-8c70-09bdcadc394b}} , where the global and l... | r | bda07a2c50394f96010077133b3d6c24 |
Blind source separation (BSS) is the problem of separating a set of source signals (sources) from a set of mixed signals (mixtures) given little to no information about the sources and the mixing process {{cite:b974c00ce2f3be28809c6fd645470bce213eac25}}. In the linear setting where the mixtures are linear combinations ... | m | 709b4b0c52635dc27e47a2afb09ed03f |
With regard to future improvements of our approach, we believe using character-level embeddings {{cite:3d43bd043cb6ef128cb12421490c0f81b0751924}} instead of word embeddings or even combing both methods could boost performance. Combining domain-specific word embeddings with generic embeddings has also been shown to impr... | r | 19973be246590c2834b97234395d2c9f |
As mentioned in the introduction, an obvious question is the origin of such a
field. The magnetic decay timescale of {{formula:e012f967-3a9f-49c4-9161-de25d4d5d409}} plasma is {{formula:3a79c596-779a-42fb-8a28-88af55301d6c}} , so
it is natural for such a plasma to support magnetic fields, and the question is
identifyi... | d | 75501e4e77db7a2ce1695a30356194f4 |
Optimality. Hierarchical Reinforcement learning is categorized into two notions for an optimal solution. i) Recursive Optimal and ii) Hierarchical Optimal. In Recursive Optimal or MAXQ {{cite:74f1a7b70ea32715969ac5f694ee38f6d6586f3b}}, the expected return for performing a subtask {{formula:5d4841c6-efa6-4719-b3c2-353b9... | m | 0838577741d9d1a222302ba49cc046df |
The master equation does not necessarily preserve the positivity of the reduced quantum state of the system.
There is only one form of the master equation that is a completely positive
map, the deeply respected Gorini-Kossakowski-Sudarshan-Lindblad (GKSL) equation. {{cite:dd53ec4dac0febf8c4e8c56e401ba9a1aa01bdd5}}, {{c... | i | c551bc78f059819281558c900a2a29b1 |
Models designed using traditional methods usually rely on statistical and design thresholds through data such as semantics, pose, and order constraints of objects and predict the relationship between objects. {{cite:4816776230c176ad7067a5e783a86e87a2bd3c92}} employed information about the spatial layout of detected obj... | m | 20d7027221f901a818d2d8e31c3d06f5 |
We also analysed the sensitivity of semi-annihilating DM through their
neutrino spectrum in detectors like Super-kamiokande {{cite:f5f9a8dbce70e894a2deb9c22c2f104fc30dd7b9}}. We found that
the limits are atleast {{formula:a09e8d0b-26eb-4c51-a664-2ccf69bc7b01}} magnitude weaker than the DM
annihilating to two neutrinos... | r | c5ca1d5522832c8221dfdb8b4f92f866 |
The successful reconstructions raise some concerns about the potential vulnerability of certain common anonymization methods used for MRI data. While the current implementation has only been shown to work on test data coming from the same dataset used to train the model, pre-trained networks are commonly used outside o... | d | a06f37ec9f43edf3280ad5a9639ee809 |
In this section we report our main results. We first consider the convex case with constant step size, where we prove 1) that the existing bounds in {{cite:d6f5e571e539304853308cbbd342dc1f7460eaf2}} are tight, and 2) for linear models, the we report a data-dependent analysis to show that {{formula:c5320acc-083c-4089-a... | r | ea778a5daed2e522520e25016cd3c8c7 |
In the previous research on covert communication, researchers studied the performance of covert communication for various channel models, and proved the existence of the Square Root Law (SRL), i.e.,{{formula:2a93fee1-30dd-42ba-acde-433eb4b7fdf2}} {{cite:c024ee6190b3482dee6d581d5e15a3561b08e5df}}, {{cite:5325c8452edd57... | i | 76c548c46efe82c5899de5ea0731598e |
One approach that has gained attention recently is the use of skills which are short sequences of single-step actions representing useful and task-agnostic behaviours extracted from datasets of expert demonstrations {{cite:195270eef9fc7119d5a0cdaaed88563dbb61ba36}}, {{cite:1d474d6fe5363240a9c9d5ace8e8c7ded4d3c559}}, {{... | i | 24c8abec1580619b93d4a685b0bbb470 |
The study of neutrino properties is known to be a powerful tool for searching for physics beyond the Standard Model. The observation of flavour oscillations in experiments with solar, atmospheric, reactor and accelerator neutrinos imply that neutrinos have nonzero mass; this, in particular, means that they should also ... | i | 533a6dfcc622383db0aa35251f40d6b2 |
Expert Evaluation. Two error metrics (i.e., MSE and MAE) have been used to evaluate the quality of our models in Tables REF and REF . They are both pixel-based, which may be insufficient for assessing structured data such as images {{cite:57afb9acca600614a3736cf5e643bb5391696f72}}. To rule out any subjective evaluatio... | d | 0b1af98ac86f17f4b6ddb422007a3880 |
We evaluate the impact of different parameters on the sum achievable rate. We assume that the Rician factor is {{formula:cb9c837d-795d-46e2-a3fa-1b6606c9fa38}} = 10, the noise power is {{formula:3bdd44a9-7758-4b73-86e3-c52450be7523}} , and the transmission power is SNR = {{formula:7e11b0db-94e2-4108-91ab-79d111dc7abc}... | r | dca5f07bfdc2ff5bd8dcf0c880af94a2 |
We test Mixing Method++ on three well-known applications of (REF ): MaxCut, MaxSAT, and MIMO signal detection.
We use the same formulation in {{cite:35a04744a3215fb8f16a41467a88d694867cfb49}} to solve MaxCut and MaxSAT, while for MIMO we follow the experimental setup in {{cite:6415a185e9315f9238cb58825373f3f0e40570cc}}... | r | 0abde96b410cd169ece803a2e9965017 |
In this work, we propose a new multi-resolution spatiotemporal analysis of multivariate time-series. In contrast to standard multi-resolution analysis using wavelets defined on Euclidean space {{cite:4fb670708417584e6291db37f679345b3b1bdea4}}, {{cite:f93a4417863abf4c2661613ea3ad28a70cdd3e86}}, we present an operator-ba... | i | f21bfcc3b8e2519d0ca0486766c226e9 |
In the non-trivial eigenspace, {{formula:89bd7ef1-9216-4c30-9770-c255e40cbd05}} takes damped Newton-type steps,
while flat directions are updated with SGD at a learning rate {{formula:6ddb8f47-62ec-4072-b5ff-2df5c1774879}} .
Based on the findings in sec:exp-noise-monitoring and
{{cite:b9d5463346ea3ee475a498ee6111ebc9e... | m | df5bff35735d19fa0e7217aa8b1755e8 |
Quantum frequency conversion of photons is important in the development of long-distance quantum communication and effective optical quantum computing {{cite:89a9070638f764a4819d1d34b0a46e245f2ad754}}, {{cite:0198ca6db716efca371b0493ee6813a451ff8e2e}}, {{cite:5270c5a63c30828c91bb71111661f987e884a4e7}}. In nonlinear opt... | i | 24677f13a258db49789fe18ad39e16b0 |
However, the in and out of distribution performance of any Bayesian method depends heavily on the choice of model (prior and likelihood combined).
We begin by noting that the training-data-dependent EmpCov priors from {{cite:a2a8a259c9e27a4a972a04e0bbf7eaec78a0a122}} can be equivalently viewed as training-data-dependen... | i | 8e3ef4d4dc2d732c5cb5b78d882e56a6 |
Ideally, the discriminator should measure the gap between {{formula:56a90c08-f28b-414a-926b-6998b9bd4e63}} and {{formula:a318c868-02ec-421d-8c00-c4678cac9fa2}} and guide the generator towards {{formula:846097d9-5880-4540-ab60-3d715c2ddf0c}} . However, in practice, large capacity discriminators can easily overfit on a... | m | cd49f02c09f8d4861481cbdab905d658 |
The structure of CNNs employed in this paper is chosen to be simple enough so that it may be trained on most modern high performance GPUs. The architecture described in Fig. 5 was found to be effective and is employed in the experiments here. During training, mini-batches of size {{formula:17d2fac1-891c-4614-8aa8-0dba6... | r | 51e42c68c93ef93e3ed573da5f1fdd97 |
Theorem -1 (G-computation formula. See {{cite:7adbe1d2aa26a2b6c92593bea8984d69fe3d2186}} and {{cite:a376a67c44adc6f4b49f50572ac44dfafe3d9996}})
Let {{formula:363b8af2-709c-4ed5-8681-fac940e93041}} denote a counterfactual outcome in a
hypothetical world where {{formula:1151ce60-3567-4dae-8869-0c731ccd38f6}} . Assume
{... | r | 3f46ab52d156974361240318257fa4f6 |
Exact solutions in the Einstein-Maxwell theory are always fascinating objects to be studied {{cite:ebfdb0237f1af14cd15c28e9c92b49917c7db36c}}, {{cite:b2f3567c166d83d2054689bbb26e23c9482b7ab6}}, {{cite:85c79cb6268ac136d4b9998c090723a4338247d1}}, {{cite:e25545e4808f0ce80faa2b43b33e0f8b05e93ab2}}. It starts from the mathe... | i | 0df87f19bd1f0c585b0fa97e01f57d01 |
Deep neural networks (DNNs) have achieved great success in various areas, including computer vision (CV) {{cite:a61accd68126c61f54a89120c46cf66c98dcca5f}}, {{cite:63671c5892a0d7c92e60bfc4a7794c0da9375099}}, {{cite:fb1757f12ff4ceb57a7ef02a62fad55deee27b18}} and natural language processing (NLP) {{cite:0fb2e302a039f6f0ea... | i | b6600f5d8a84aba0c4b8cd800e8b9560 |
First, the parameter setting procedure used in this study could be refined to avoid optimization bottlenecks and limitations that affect all layerwise training protocols at scale. In particular, the Powell method while effective at small {{formula:77e1a046-2097-42cd-aeca-6ea2ba911cb2}} would eventually become intracta... | d | 7bc5bedfc1fab2966f64a9ff4ae6c04e |
As already mentioned earlier, our proposal to calculate the smeared
spectral function is not limited to the case just discussed.
Any sort of weighted integral of the spectral function can be
considered.
A well-known example is the contribution of quark vacuum polarization
to the muon anomalous magnetic moment {{formula... | d | 9ba6bc72fc23862780ee519fb64e1373 |
Luminosities of gamma-ray pulsars are over-estimated when the decay of their flux density {{formula:57fc5d05-1920-4c7b-aaa8-75e104fe1033}} is assumed to obey the inverse-square law {{formula:02210b96-552b-4e9f-904d-b954fd114de3}} instead of {{formula:d34d3161-e54c-4d9e-a748-2f60d3d35d8b}} by the factor {{formula:a23... | d | bd5962bf82c21607e4e52e18741c2df1 |
Finally, we present the changes in {{formula:744f8816-f8bd-4df8-a237-87d94b8bdf68}} relative to the Planck+ACT {{formula:55ef6861-bce8-4f77-878d-8fd4a68b9d2e}} CDM baseline for the other three best-fit models,
listed in Tab. REF .
The model M1 increases {{formula:6b001b59-0d5c-4d02-bfa8-da79bdaa4cff}} by about equal ... | r | 8877716f8deef358515e061d80d86c8b |
derived in Fokas {{cite:7e0ec8e4c4edbc8dbd94c67f4d0b79effb208f56}}, Fuchssteiner {{cite:85a92f80eaa28dbca3ac211bd9efe57a7dab283b}}, Olver and Rosenau {{cite:e3093fe7e829c5d70c97ae6a77f15890650688c0}}, and Qiao {{cite:af66442690e80c3a61dc3c3e8d1025bb9e6a2f5f}};
while the choice a = 0, b = 3 gives the Novikov equation (N... | i | 3111e7233e3ae5a23f35395763306315 |
From Table REF , there are several observations. Firstly, for both the nature models and federated adversarial trained models, randomisation is successful in mitigating the adversarial attacks. When we apply randomisation onto the original test dataset, the performance only drops by a small amount (usually less than ab... | r | b47cd47ac483092d50c7a2c1779ea499 |
Group testing is a well established area
attracting attention of specialists in
optimum design, combinatorics, information theory and discrete search.
The origins of group testings can be traced back to the paper
{{cite:c4264b5700b62938581e682b8c1837467b9c9675}}, which is
devoted to sequential
procedures of blood testi... | i | 98283c53b8df0e42988d925027d005a8 |
In the result below, Theorem 1, we prove the existence and uniqueness of the process describe above and provide an uniform control on the maximal membrane potential of the system.
The proof of Theorem REF is omitted here since it is analogous, modulo a small modification of the notation, to the proof of Theorem 1 give... | r | ac428788d481252cad5102ffd3698c1d |
In existing BP literature, there has been much interest in exploring the use of message schedulings for improving BP performance. The naive scheduling is known as Synchronous or Loopy BP (LBP), where all messages are updated in parallel {{cite:dc439716de2483ea759a36c2081156f33d04e2b0}}. Asynchronous approaches, where s... | i | 71800721bf4b221f1367db4c021cac94 |
Symbolic representations need to capture both syntactic and semantic structures in code. Approaches to generating symbolic representations can be categorized as sequence-, tree-, and graph-based. Sequence-based approaches represent code as a sequence of tokens and only capture the shallow and textual structures of the ... | i | ec39175de24a253f774c6fab837457fb |
In this context, machine learning (ML) is beginning to provide powerful new capabilities for the computational design of materials with targeted properties. For example, ML can be used to train a model that replaces the direct computational evaluation of the property of interest, which significantly decreases the time ... | i | 183cffe8525e18b0c46aef32657c92ea |
Sample images and faces generated by three systems are shown in Fig. REF and Fig. REF , respectively. Qualitative inspection shows that models are able to generate high quality images. The generated images have high fidelity, are diverse, and most of the time are physically plausible (with few exceptions e.g. the righ... | r | 5279eff479e6fa878c85beddfdfd835b |
Various works investigated the possible observational signatures of wormholes. They include studies of the shadow {{cite:148b332094921b19e8d5e52182b56d437928131a}}, {{cite:545de532760f084e2bcbb2e6c8410d55614f6d04}}, {{cite:8333fd5bb899369627af6ac8b65f4d69765ca5b4}}, gravitational lensing {{cite:321286b02ac32ce62dff2729... | i | 54a550022f779d6e40a6461d49fc9247 |
The last expression is known as the Jacobi triple product identity {{cite:4e3d6e119b733aaabce7b37ff30b1fdfaaabceed}}. The function {{formula:8f9c8f07-6686-4f01-8085-24c63d59675e}} has simple roots on {{formula:4a691ecf-2de7-4ed6-b9d0-04fff9caae49}} . We define the {{formula:be4e41e1-9a3e-4a3a-b230-8c14696c9d12}} -char... | r | 10b08b920554d640253dbcb456ef889b |
When studying black holes in {{formula:011c4953-25f6-4439-a892-49c2805c555d}} dimensions with {{formula:ba3cd009-1a99-4368-b0cf-456b483d366d}} , it turns out that the non-trivial, interesting part of gravitational dynamics are strongly localized within a region closeby the horizon {{cite:233298858920c559abbd8c93ac89f7... | i | 747e96aedf4920f693e46191dda3f6e8 |
A standard way to calculate a spectrum of doubly heavy baryons is i) to first calculate heavy diquark mass, and then, ii) regarding the whole system as a heavy-light system, to apply the potential model like a heavy-light meson. In this situation, especially in the second step, people normally obtain the eigenstates wi... | d | 5fbc72599bd1cfa51b96dd0f48efd373 |
Fig. REF compares the AUD performance of different schemes versus the number of OFDM symbols {{formula:c67f3344-834c-4d7f-a70c-13771672b0f0}} .
In Fig. REF and Fig. REF , we set {{formula:4e3a92cd-6cf1-4ed0-9683-e0da1f0cd1bb}} and {{formula:449127f5-973c-4b9d-8ddb-887ef4c63745}} , and we consider the cases of {{for... | r | 022a8d2c7d2ad36dfbb81cf680490ec0 |
Linear Projection (LP):
This is the simplest method developed in Chapter . For every embedded word vector {{formula:af3554e7-780c-49b8-b22e-34a4aabf3ab9}} it projects it along {{formula:a9c68425-96b3-42cd-82e7-4b529cd63783}} to remove that component as
{{formula:16134ee1-fed8-4c27-a75c-66bba46e3b7d}}
Afterwards th... | m | e930f26a101e61b3e2f4fd5f9a19f831 |
Discussion – Several reasons explain the superiority of the proposed method over conventional PIT. First, the conventional PIT applies a hard decision on assigning the output-label permutation that minimizes the total separation error. This is not an efficient decision especially in the initial steps of training when t... | d | 5de505bb042199ec22b04251a30129d2 |
In the limit where all the quantities and functions in
() are known with perfect accuracy,
{{formula:9cd002a1-f477-449c-9c97-1531d631d1b2}} is a likelihood. By the Neyman–Pearson
lemma, the ratio between the likelihoods obtained under two different
hypotheses {{formula:a2aba987-f0e7-4b0a-ae5b-817086f42254}} and {{for... | m | b2b476048d7a1058327c2569603e1efb |
Estimating covariance matrix: In this work, LogDet estimator measures entropy with covariance matrix. In which case, we can adopt many covariance estimation methods to improve. One possible approach is to introduce prior knowledge about estimated samples. For example, when estimating with image data, the locally corre... | d | ab0a140224dc1ec6de623e9f35f56c4e |
The last sub-net makes the final prediction {{formula:740d75d6-d224-4b0d-9dcf-4deedf9b873e}} , which is used to compute three of the losses: i) a Reconstruction Loss {{formula:fb83073a-d3df-4f27-838d-8b20c9e637fb}} ii) an Adversarial Loss {{formula:c77414f3-8784-44f0-9eb6-a0ab9fb31f99}} and iii) a Structural Similari... | m | 5116c4a8b9a3dd3ceac6b36268783564 |
where
{{formula:42c39814-4a48-4176-948b-1f148571454d}} is the Frobenius norm and {{formula:042b182b-9293-4b28-9bf2-5499202f60b2}} denotes the smallest singular value of {{formula:4f7a89c7-fe88-45ef-ae83-9e626a5346d1}} . This elegant result triggers a great of researches into developing new variants and corresponding ... | i | c43ada3cda86793b0367de6ce31f7852 |
Agricultural environments, such as an orchard, offer many challenges for autonomously and safely deploying robots at work {{cite:accbf63d6bf5ccb7660587a57b542b688c1d6d0c}}. For example, the authors in {{cite:07fccbd30ca9f8ceaad03011ab524865a189a651}} compared state-of-the-art SLAM methods in a simulated vineyard enviro... | i | f2433dcfedb253aff3d0fa973f860baa |
Off-policy Actor-Critic.
In on-policy AC, the data samples are generated in an online manner, always sampling based on the current policy at hand. In contrast, in this paper, we focus on the off-policy AC, where the algorithm updates the policy based on the data collected (possibly in the past) by a fixed policy, calle... | i | 748543839a56faaf9381763e103a2f5d |
Quantum simulation remains one of the most promising applications of quantum computation due its potential impact on high-energy physics, cosmology, condensed matter physics, atomic physics, and quantum chemistry. While the vast majority of quantum-simulation-based algorithms have been designed for the fault-tolerant q... | i | 92178abbad4868ba229bf36f947bb7f3 |
Using the tools described above, we find the allowed/ forbidden areas in the {{formula:6c5ab11c-3f20-4cbc-8f34-87cfb246f74c}} plane as shown in figure REFComparing to the results presented e.g. in {{cite:9572875467f69a713dbf2eea2c9f30baeabefcb2}}, {{cite:966bb5bb250c57116680ef4c61f6d49fdc2f0fdb}}, we see that constra... | r | 21cb035c88c7b76bbb12607660b0d4ca |
In this section, we rigorously prove the main results given in Section REF . The technical tool so called the backward error analysis {{cite:7c5e2fa310306c95686d7a019537c78fa32300b6}} which is indispensible for the study of equations or numerical solutions over long times will be used in the following proofs.
| r | c599e7c741d1428e86e9f28d12a89700 |
When the {{formula:3aaf9da7-7a46-4c6d-853c-7ebabe7f2599}} matrix is learned using our extension of the relative attention proposed in {{cite:fb8b1b790dc7a1814c2d5512dfb0a6675a93aeb3}} (Learned relative), the performance is further improved. We think that in this scenario where a decent amount of data is available for ... | r | 1a89a270924c08a972c8be46afbb4f84 |
With rating prediction and LFM method on CF have been widely researched in early literature {{cite:de057674dd65bad1e6c0ee46da3d1d4e47e489b7}}, {{cite:3772390372f7f7fe034c216d51ce9eca839b9a14}}, {{cite:efd48d51d6a79f71db5b6f58058eb66dc2ac605e}}, {{cite:2f9ca5d5f79487837bea4ac2a89d2e9327758c7d}}, a variety of probabilist... | d | b8a3a39159692da551c74db3afe7e84a |
In this section we demonstrate how the GRE framework reveals further connections between Gaussian process regression and the Nyström approximation for kernel Ridge regression (KRR). These connections have been known for a while and received some attention recently {{cite:c5b900e306bb6e1660070d6dfee7c725cc0e70b9}}, {{ci... | m | a44cd1abd6b1c45605f3c1a5080b6c79 |
Furthermore, we find that the separability of two non-adjacent displacements results in the relation {{formula:d5ff719f-223a-4afd-81d1-ee370a1a701f}} for {{formula:6dcfa154-6e7c-4b52-94b7-461686cce7b8}} . Since {{formula:d2c88451-09bc-4aca-95e2-6b0325bde304}} is a multivariate Gaussian with 0 mean, we can apply Isser... | m | 1ad0d2c5dfc4912c376640d7d75820e6 |
To address such “data isolation" problem, Federated Learning (FL) was proposed as a decentralized approach that enables collaboratively training while keeping the data on clients by only exchanging gradients/model parameters {{cite:3775ba6a86bc0eff8b28ed00d3f8c06ddc2690db}}. In particular,
Federated Averaging (FedAvg {... | i | 4fc49ed473d4e91e061996b5fd57ee53 |
The ACADO code generation tool, an open source software package for optimization problems {{cite:aafe85f3af3b2ea8bcdabdbc34e87831a4ec5cda}}, {{cite:23a35f46985f1efde9454c70dd308df78251a132}}, has been used to solve the constrained nonlinear optimization problems in the NMPC and NMHE. First, this software generates C-co... | m | 9cf24b433608f0ffed881e04d2b859e7 |
In this paper, to get a well defined principle symbol, we have shown the spacetimes in the so-called “Einstein-Gauss-Bonnet Gravity in four dimension" have to be (locally) conformally flat. So, locally, the metric
always has a form {{formula:f3ece315-766e-4bb9-a82f-88db4d884726}} . Although the theory is diffeomorphism... | d | 241372cd6a5736c55c89a45df8b4e06b |
Most algorithms on word vectors and related representations are applications of linear models, that perform predictions using linear combinations of feature
values weighted by learned parameters. The tasks that are commonly solved using linear models include regression, classification, ranking and clustering.
In text r... | m | baed503d9cd518c7c3bb0bd8283aff51 |
We have evaluated Pattern-Net on the ModelNet40 dataset {{cite:400c7de4fd01fb78218403b22e3412f2a028db32}} for the classification task. It contains 12311 meshed CAD models from 40 categories. Similar to the other work, 9843 models were used for training and the rest for testing and the models were normalized to a unit s... | r | 1377465ffece0d5588c2664414d88556 |
We present Smooth-Reduce, an extension of the randomized smoothing approach proposed in {{cite:f9f7b8ac62a2cd6a55cde939bc035ab87104f3f1}}. We empirically and theoretically proved that Smooth-Reduce classifiers improve over standard randomized smoothing in terms of certified radii, as well as abstention rate. Our approa... | d | 08f8a0648e6569544278dcdbff6c26fc |
In this work, we have focused on the spectral properties of the
normal state at zero temperature. The next step could be the
analysis of superconducting states {{cite:511020b7cf2976797f02a2c49f9b90067bc4ecd5}}, {{cite:d58c29395f4c6ae0d9b1e57ee2ff237135c6bb0c}} where the
coupling to longitudinal optical phonons plays a ... | d | dd877c6e5e1ef5d515f31488707deb95 |
Then {{formula:a23b661d-04f4-40e3-a0d3-e7a610afb251}} is said to be twice epi-differentiable at {{formula:9519b432-2cd6-4f0c-a4c5-81a6c14df597}} for {{formula:de4be383-0d69-4c6b-83d1-844cefd9a693}} if the sets {{formula:8352a355-b326-4bc5-8e82-ae1d69e08d24}} converge (in the sense of the convergence of the correspo... | d | 97ff67ad21d5ad75606dcdb04cb224c2 |
Finally, we have proposed a construction method which can be used to find out some new non-trivial examples and discover the computational power of Deutsch-Jozsa algorithm {{cite:105df883f95a24490cc7adca9b970c153e4c391f}}. Most importantly, the construction method paves a way for finding out more problems that have qua... | d | f32582b9fe4f2db8a25f70d2f8fa0fb0 |
We implement the QBMC method described in Section as a module within HyST {{cite:ea427b0096d006ba800aae358023de6c99f4675d}}.
HyST takes as input a hybrid automaton model in an extended form of the SpaceEx XML format {{cite:932afa90d5fbef6c53d4aeebe20880ad18434cb1}} (supporting e.g., nonlinear functions instead of only... | r | 8fc1694b1c6514eed543fcc31f4810ff |
In Bayesian deep learning, temperature scaling is a practical technique to improve predictive performance {{cite:3cf0edcc73a126b51bf1e8e69d89a3da47a69b33}}, {{cite:0e0898398cfc0d098e965b8ab0636d2e167db150}}, {{cite:08659ddc5d10e836ae2fce1269400b711b7734f9}}.
There are two main approaches to tempering the posterior, nam... | r | 0b5d00945d48edbf7388a5125d8c2edf |
1. FedHealth with incremental learning. Incremental learning {{cite:c4e133068cac5342ee4ec9ecdf2b397c4c679a6b}} has the ability to update the model with the gradually changing time, environment, and users. In contrast to transfer learning that focuses on model adaptation, incremental learning makes it possible to update... | d | c7308807d7ffa7ba59d3770201d81442 |
The potential of deep autoencoders in this study opens the way for exploring more complex AE such as denoising or variational autoencoders {{cite:8cd66c3af205eb7875bd1e2f479eee8f3993693f}}. Future work also includes the possibility of considering this problem as a multiclass classification problem rather than a binary ... | d | b4bc512ee45d3e49ee35d33a450cbcca |
Comparison with State-of-the-Arts. In Table REF , we evaluate our model on the HO3D v2 testing set and compare the results with state-of-the-art methods {{cite:727d5fa77d1211b2323df36cb0b3d1b993dc98da}}, {{cite:8ec68f50b9c40ebcccca7e7a30238990b91d25b5}}, {{cite:dcaa50574c4d9018a279e4f20b1923158b554e09}}, {{cite:d383a45... | r | adde938373a1edaace731a4503a9d172 |
Investigation on Other Pre-training Tasks.
As mentioned in the main text, we only adopt Masked Language Modeling (MLM) and Video Text Matching (VTM) as pre-training tasks for both the proposed Lavender and the task-specific baseline Lavender-ts. Here we briefly discuss other popular pre-training objectives with Lavend... | r | 20c4541dc6b4ea0ddf146a0e954cab59 |
Generally, the matrix {{formula:67dbf481-258d-4d54-ae3f-2bd96b779470}} need not be invertible (in particular, {{formula:24784fc2-e1c6-4981-975f-b82156cf7191}} is not invertible – see {{cite:cdc34bd46f351564bec7041a1071084d67cddac3}}). {{cite:7d516bfbcfb55aeb70999ce0f051011e0533df0f}} proposed using the Moore-Penrose ... | m | 384415c1398580a7171fbebadbc9669c |
Baselines.
We have six baselines: (1) Noise-unaware search: the SubCircuits are searched with noise-free simulation. No noise information is involved. (2) Random generation: with the same gate set, we generate random circuits and constrain their #parameters the same as the QuantumNAS searched circuit for fair compariso... | m | c273520442fc719fcb0fd3f82e6705e9 |
Salakhutdinov et al. {{cite:6872736bc6cfc8a327ed200c2b4cb339cde8e96d}} proposed BPMF based on MCMC and rating matrix which we have discussed detail in experiments.
Lim et al. proposed a Bayesian approach to alleviate overfitting in SVD, where priors are introduced and all parameters are integrated out using VI {{cite... | d | 1caad5ae15b4268c07d25be42adeea0f |
The previously fastest algorithm involves maintaining the matrix inverse {{formula:a73e9e60-b85e-4108-85d0-2be91355728d}} using subspace embedding techniques {{cite:664403739ac7fb8a42277a63e95ddb4f01c65678}}, {{cite:88955101cee61169acebf0121b57691421683187}}, {{cite:1dbe4bdf83b803bd2985b3b07d9f978f097f5596}} and lever... | m | 46f9d926a0a6ed50dcad7e6065cb335a |
Despite the above remark, models {{formula:0c7c9ffe-58ad-49f9-9f3e-60eac558a2ad}} 2 and {{formula:8f09fab7-eb1c-48d4-8eb6-5b00f778e2a9}} 2 fit the data much better than models {{formula:b1abf016-8142-4006-8487-172ee622aead}} 1 and {{formula:b8861804-9cc1-45f0-90f1-1b16ad643cc5}} 1, especially for late-type galaxies.
In... | r | fbc215658fab58973134f50704b97bd2 |
How does the performance of a semi-supervised distillation method compare to CheXseg and fully-supervised methods?
We find that the implemented distillation method has a 6.65% worse performance (mIoU) than CheXseg. That said, we observe that self-distillation can improve learning performance for each fully-supervised m... | d | 1c1ddb0c00bf2f339c66e128d5813026 |
Random: All network weights are randomly initialized and no pre-training is used. The ViT backbone initialization follows the code of {{cite:1cf8c6642c79d0b7f60a520968c86aae30d23096}} and the Mask R-CNN initialization uses the defaults in Detectron2 {{cite:6bcfcb2fdcaf2109af9ae728751e7b500f8a2968}}.
| m | 017afef449ecea3f903c30f27d4a1a10 |
Training details. We used Faster R-CNN {{cite:b5a7609b7f6f7856137f66233ef12e0b56eeafdd}} and Cascade R-CNN {{cite:8bc9a6b669458460d8eacd0523efe527a05cbc31}} as anchor-based detectors and FCOS {{cite:06fae47dd06c5af0122bfdd0e7df00a9d0a70a8b}} as an anchorless detector to compare performance in the Epic-Kitchens object d... | r | 08546ae3453d735f46b26110fa660958 |
where {{formula:c520a19a-5156-457c-8a71-86d985f8b30c}} denotes the conjugate transpose. By standard arguments one can prove that there exists a unique sequence {{formula:d3f424b4-57ed-468c-98aa-3a605674da19}} of monic matrix valued orthogonal polynomials (see for instance {{cite:ca072a1ba66a4d9473d5ca26552ff714ea77d5... | i | d957661de83baafb911f9b27aa714b1e |
We first note that some adaptations of the lemmas from the global convergence proof of the Adaptive Regularisation algorithm using Cubics (ARC method) are used to prove Theorem REF , see {{cite:4ef2aae9afb38076fe92f4c341559d82cb70acb2}} and {{cite:e0cb14a3d0fe3260f50116e9bc54d3aae5035fd0}}. We begin the proof by derivi... | m | 281dd7505ebaf326f9db4ce803a2e964 |
We now record estimates due to Lang and Weil {{cite:54cd22cae01a8236e244ad6fe9c57d066369c2c8}} on the number of points on varieties over finite fields. The following is a well known consequence of Theorem 1 of {{cite:54cd22cae01a8236e244ad6fe9c57d066369c2c8}} (see, for example, {{cite:163861d2e529ae3b5ea4b2593dfa1508ae... | r | 61d4de2723c3fc5aefcbdb36f29872c5 |
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