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Finally, we conducted a post-hoc Bonferroni test {{cite:edc23f09f2945f8ed9a52b51ddc47ed29e2bad87}}, for ranking the proposed regularization method and the only hinge loss training and evaluating the statistical significance of the obtained results.
The performance of two methods is significantly different, if the corre... | r | 75a3d6635e9e456ef45f9aee4bbfaea9 |
Evaluation Metrics: Two widely used clustering evaluation metrics, i.e., Accuracy (ACC) and Normalized Mutual Information (NMI) are employed to assess the effectiveness of the clustering performance.
For all the metrics, higher value indicates better performance.
More details of these metrics can be found in {{cite:cd1... | m | cb08369e6d33ba590e318a70ea972728 |
the last integral is finite for all {{formula:6ab90ef9-68a2-4879-bf2b-07f4965d4a23}} and all {{formula:722e0a74-2286-4b02-b955-27bf28aec5da}} .
It is clear that {{formula:efbcae26-d3a0-4f5c-ab09-7fd6332461b4}} . Now, we estimate {{formula:479bb968-e134-4394-9f80-32c6b5b359b8}} . Taking account of Proposition 2, p. 332... | r | b208348faa36d5e904ac778eb797c754 |
where {{formula:9961341d-b49c-4172-a426-354dd21aba00}} is used to balance between the knowledge distillation with the EMD pseudo label and entropy minimization. We note that a trivial solution to entropy minimization is that all unlabeled target data could have the same one-hot encoding {{cite:bb3bbcf2cdd346722036c7e6... | m | e09c321a57219aaeb714b87fcf641a8c |
Under our strategy stack-and-finetune, the model training process is divided into two phases, which are described in detail below. In the first phase, the parameters of the pre-training model are fixed, and only the upper-level models added for a specific task is learned. In the second phase, we fine-tune the upper-lev... | m | 56d647dcda97c1ebcbfce5e9cd07ccfd |
We assume that the secondary masses {{formula:6de6c3e5-cded-4cd6-94f4-9ac48357036e}} obey a flat distribution between 0 and {{formula:034e60f9-aa83-4169-989d-50c6fc4f169d}} {{cite:6c8e516882fa20a227b9a673c367057585f9c3f8}},
thus giving
{{formula:54b84fde-030b-4f83-a6ee-e938b7eb3448}}
| m | 54c30e4eb4e39f6d15f4711d29d96f02 |
Furthermore, our point of view has been entirely from a Rindler observer. It would be interesting to work out the implications of our results for an observer that only has access to {{formula:3e941e12-1cf4-4e9f-bfa1-de0388afc625}} . One can think for example of an observer whose past lightcone contains a period of infl... | d | f2d796958c4a18fd464b9a8227950b70 |
The conservation of energy momentum tensor (EMT) has usually been assumed in the most investigations based on some physical facts. However, it was turned out that the conservation of EMT may be violated in quantum systems {{cite:f3834056e747dc2e945c7914e45055f398dcdd1a}}, {{cite:d06590331a3ff3440f2e72c0ba94e644c28c1669... | i | a5054b1ae939274b69c07f1bde202d3c |
Dark energy with varying equation of state may be
realised theoretically
in terms of dynamics of a scalar field ({{formula:5d8fe9a0-27ad-43c9-9d8e-6a2c3a2bc847}} ).
One class of such scalar field
models, called `Quintessence', is described in terms of standard canonical Lagrangian of
the form {{formula:2205a1d4-1cf1-41... | i | e2277534eb1e3cd6087883df81fe4ad5 |
Attack variants.
We have presented the DPC attack we found to be the most effective when
the adversary splits its hash power in two constant parts {{formula:ee3cba53-e5e9-4209-a52b-676caba447eb}} and {{formula:c3a855d7-197a-49fd-b674-8ac2f3eeffa5}} .
We foresee that one could devise variants of the DPC attack, e.g., u... | d | 572bd2fc9b49d65afb81ef822073c399 |
Our models only covers part of the parameter space. We assumed a fixed dust-to-gas ratio of 0.01 even in the circumplanetary disk {{cite:4c23aadff4b2cd51acb123467d91a956420e1004}}, however real disks can have smaller and larger values than this {{cite:573625f0deef42b32d80d5d98dfb2e0bf33c89f7}}, {{cite:b8b4f72cab9555cd6... | d | 9c03b2f8ca8ecc47fdc816ea06043ad9 |
and the structural similarity index measure (SSIM){{cite:9146a8141d01e256b6e6e14e6cb2875cdf0da0c5}}.
{{table:d0bd2a1c-820a-4f4b-b48c-35ad91740053}}{{figure:8fec7544-b2e1-4648-a107-5e7bd359fb91}}{{figure:d557dc1d-57ec-490d-b104-1facebf7e991}}{{figure:13741af2-a86a-400a-af86-c068ddba85e5}} | r | 13924e30e61581d345e0d40aa7cf1f4c |
Another important direction is to consider more complicated background geometries.
Although only limited class of Argyres-Dougras theories are realized by the D3-7-brane systems,
more general Argyres-Douglas theories can be realized
{{cite:f8baf04f8dffa0c867242a3cdaef77a1566ea140}}
as class S theories {{cite:27b0a86471... | d | 6236ece5d5e6121d41ae77813ef24110 |
To find transmission probability, a finite size DSHG is clamped between two electronic reservoirs those are usually called as source (S)
and drain (D). Following the well known Green's function prescription, transmission probability is obtained from the
relation {{cite:34f5b46a7bd5ae9bbd4ba2620f244a1b6489462d}}, {{cite... | r | 1c2a6026609efbec8f53d274477a1b7b |
Most of the existing XAI methods attribute the output of a pre-trained neural network to a part of its input.
For a multi-class classification problem with {{formula:f95898b6-92b8-4b50-bbbd-c63b6be56770}} classes, let {{formula:9a329cfc-48af-4dbb-945f-9875c47eb648}} be a pre-trained neural network which takes an inpu... | m | e0d46eb1fa81dee2474a502eeba12aac |
This paper compares two methods: OpenMax {{cite:029adcafac3b2738655829e1d97b9683e8e205b4}}, and a baseline algorithm {{cite:352fd56ab737ee0eb1ef0c8b7089bfa48dcb22c0}}. The selected supervisors all consist of a manipulation, followed by a discriminate criterion, {{formula:1a1e2c91-172d-4625-a7d1-02c457b439b8}} , that co... | m | 2a199323e926b54bea0d7a3c43622f04 |
There are multiple approaches to tackle the task of nested named entity recognition.
One class of method addresses the problem by enumerating all possible spans of the input sequence and classify each one with its label, including a "no entity" class. {{cite:09f44cf610bf7fa093c6e76c18543f64753fee20}} classify each span... | m | 85b8a84908935fd8204a7f77fb3b55fb |
Plasmonic devices hold promise for a host of metamaterials applications, due to their ability to control light propagation on a subwavelength scale. Noble metal nanomaterials are in some ways ideal components in such devices – they possess tunable, large amplitude plasmon resonances, which can be excited at optical wav... | i | f6e934ed02be601b0a812825c4601bc9 |
To obtain the values of NP WCs, we minimize the {{formula:259ac874-41f5-482d-b9ee-5adbb809df91}} function by taking non-zero value of one NP WC at a time. While doing so, we set other coefficients to be zero. This minimizations is performed by the CERN {{formula:deff04f3-b4dd-4aff-a55b-cec2a397242c}} library {{cite:c... | m | af53c6a0ca5b6bc42f3561e2845b26dd |
The above experiments used {{formula:8e80816f-9cef-496d-92cc-6f680193127b}} -values from null hypothesis tests as
criteria for matching. However, since our stated goal is to create
groups which were equivalent—not to accept or reject the null
hypothesis—future work could use Bayesian statistical methods which
allow one... | d | b803a8df8bc0f953474b873cb0bc23a7 |
Perturbation and counterfactual explanation methods {{cite:ea0e36660ab76d9e98d6755819670be6dfd68d9f}}, {{cite:0676c260cb7d74cec01db05c8b6414d8e802bda5}}
can attribute a static prediction to
individual edges or nodes by searching the optimal change to the input graph, while we are handling arbitrary given graph evolutio... | i | 813758b7268a482f828c0ac47c23b634 |
RL applications {{cite:d605be8e9524ce769475ef45df500f0c35261fd1}} are based on the idea that an optimal solution can be obtained by learning from continuous interactions of an agent with its environment. The agent interacts with the environment by sampling its states {{formula:dde10d97-54f2-45bb-9650-259b5bb7ad67}} , p... | m | 26d6d5770cae619f7e1eae75f4aa14a8 |
A fundamental assumption in meta-learning is that tasks in meta-training and meta-testing phases are sampled from the
same distribution, i.e., tasks are i.i.d. However, in many real-world applications, collecting tasks from the same distribution
is infeasible. Instead, there are datasets available from the same modalit... | i | a30c8dcc3302003f9ae684151bdcc927 |
We compare with three pre-trained Transformer-based models: BART {{cite:62d9e9fe6205c68605fc17fafdf0111377e0db9a}}, T5 {{cite:909ccb2d7de184e080b9b4358876fd541050fb2a}} and BERTGen {{cite:06ec87847cf950a45ba4e3a8604b7d6186c6a51f}}. These models have demonstrated state-of-the-art performance in various tasks. We also co... | m | f930f3ff8f70d009ab23d24dc8d85ed4 |
Balancing and control is central for the stable operation of power systems. Secondary control is one of three measures that are typically installed to enforce the balance between power supply and demand {{cite:9185e169aa47fb7632689fddd8d5f610e90f96e3}}. While primary control acts within a few seconds after a disturbanc... | i | db3d20881ae721a81a5d1d2739431b4d |
Although there is still a long way to go and the challenges are diverse, huge advances have recently been made. Several experimental demonstrations of quantum computational advantage have been performed since 2019, when Google Research claimed to have achieved it on a 53-qubit programmable superconducting processor (Sy... | i | 3d81ac5a0766df96e9ca7d1204dc7429 |
Table REF compares our defense with the benchmarks from the literature (all columns except the last report classification accuracies). In all whitebox attacks, we use attack settings: number of steps {{formula:0d07ddec-b42b-4b2c-875c-0caa88c5f37d}} , number of random restarts {{formula:d82d4977-3254-4b13-8f27-85a66cf6... | r | 98bed2501ea73b789d158a651b59b679 |
All main experimental results concerning the 10-class ASC task are shown in Table REF . In the training set, device A data accounts for around 75%, and device B, C, s1-s3 accounts for around 5%, respectively. Thus, we can think of device A as a source device, and the remaining ones as target devices. Based on the devic... | r | d65b9c0cb5e4d3607df3db10859b830c |
This research made use of Python (http://www.python.org) and IPython {{cite:7ba3743ac315054fa624059b92e61aa64a31744e}}; APLpy {{cite:a1ae5872719317ea2a9197e2dba994abfc2812a1}}; Numpy {{cite:1b5c19394b55fef042d89f8ca9a52ec93d06d3c3}}; Pandas {{cite:ddb370c6bea537353df420ba072d3d50ea1e1ada}}; of Matplotlib {{cite:9c92e0f... | d | c577329a2565531a5f64ff05fe583f10 |
Before describing the analytical tools employed in our experiments, we introduce the necessary notation.
Let partition the feature extractor {{formula:ee3cf023-5c33-4cee-b1dc-a7d0c8b87389}} of a generic CNN in blocks along the depth dimension and denote with {{formula:bdb49903-9164-409d-b9cf-1acc0b19e737}} the activa... | m | 01cd175a4e6b84d47b009ec2e5cea31a |
The SFR from the 1.4 GHz emission is lower than the SFR from the H30{{formula:50584080-2690-4f7c-b033-2b57ae15f0a9}} data even though the 1.4 GHz emission is measured over a much larger area. The 1.4 GHz band has been calibrated as a star formation tracer using data for solar metallicity galaxies and relies upon a pri... | r | cdf4297a2abe77bb4fff4e2970782128 |
L. Funar also proved that torus bundles are not distinguished by the profinite completions of its fundamental groups (see {{cite:b633f9a67df6ac73e30e9561cdd410b2ae2c8569}}). It follows from Theorem REF that the torus bundles {{formula:a4dc82db-c203-4d13-be3c-a56c0bb7dfea}} that are determined by the profinite complet... | i | 484db57092e1b34e42769bbbc7a5bc3c |
In Section , we show that our Rawlsian fair adaptation formulation above is readily applicable to any black-box model that computes a score function or a feature representation. For example, we train a model to maximize classification accuracy on a standard dataset used in text classification for toxicity. Our Rawlsian... | r | 00e16a0c6901169df1390ee3a6875387 |
In {{cite:7648c1b598a2f666c82b00ae6971538b3dca3bd7}}, Zakharov proved that the water waves system (REF ) enjoys an Hamiltonian formulation. Let {{formula:84a43692-f4fc-4ac3-9435-9fb99bcf2475}} be defined
by
{{formula:345824f0-1f31-47f5-b308-cf6028beed52}}
| r | 6ff3f3928797814999b4455f670295df |
A well known property of shear Alfvén waves in the expanding solar wind is that, for frequencies higher than the expansion rate, they evolve according to the conservation law of wave action {{cite:f308ce329056a7fcb526ee45ea3e51ffdda5d73e}}, {{cite:457b755370e72d6dc0fe64519c9e769d838a6daa}}. In the radially expanding so... | d | ff50c4fbe77ade113c592d86b77a5b8f |
In summary, most recent unsupervised and weakly supervised methods involving adversarial structures only consider the consistency constraints between 2D poses and 2D reprojections or among several lifted 3D poses{{cite:14c9332f8137b56ff1a2894b1053ac6a5c1beb48}}.
In this paper, we propose a weakly supervised method that... | m | fc7e704af31ff456b8be2e4b6a99587c |
The random forest of decision trees model is the most popular solution for regression and classification machine learning problems {{cite:73e97bbac7fdf2e57b96fa2db1be0ead8e14cfaf}}. Its combination with Gradient Boosting, a specific method for the training, allows overcoming many technical difficulties. For example, su... | m | ba4f561ded942f0008679cbe55aba2c3 |
Group discrimination is not a first proposed concept. Previous clustering based methods DeepClustering {{cite:e35f8a398e61574e766251cb07eae5ac25e841e9}} and SwAV {{cite:607e49433c81ee2c4580c31ed1d70c83bfdc8133}} also conduct it and in terms of the loss function, the three algorithms have a similar form. The main differ... | m | e02c1ed67d5a47cad3236623c0c1e77d |
In Table REF , we also include results on detecting pedestrians using Faster R-CNN {{cite:992eb5c0564652926ffad2f4d1497c78725b9346}}. Comparing the results, it is shown that YOLO outperforms Faster R-CNN both in accuracy and detection. Moreover, in the case that we choose Faster R-CNN as the detector, the AABBFI method... | d | 1bca389323670fe2bfdca4a1e7e16f3a |
Nowadays, search engines play an ever more crucial role in meeting users' information needs by locating relevant web pages from a prohibitively large corpus. Query-document relevance estimation, the core task in search result ranking, has been the most critical problem since the birth of web search. Numerous works have... | i | 76d22b5c451dd38367f982a500071c37 |
BasicVSR. BasicVSR outperforms existing state of the arts on various datasets, including REDS4, UDM10, and Vid4.
BasicVSR also demonstrates high efficiency in addition to improvements in restoration quality. As shown in Fig. REF , BasicVSR surpasses RSDN {{cite:0a6f829fa74f994ef5d0a8d0ac7f847e57360ee8}} by 0.61 dB on U... | m | 217a26f15c0a485216de8adbf27bf4df |
In {{cite:0a81cea81a9d87a95d60b43e8be3855c996a93da}} it was suggested
that the origin for such singular behavior lies in the use of the Kruskal coordinates. The Kruskal coordinates
are suitable to the analytical continuation of the Schwarzschild metric but they are singular in the limit of vanishing of the black hole m... | d | ffb9f0d0952c9ba99504aff7ab4a33a2 |
In theoretical aspect, the inevitable ambiguities in penguin topologies result in great difficulties in evaluating the {{formula:62e8a937-0d22-4b49-bc57-ab44ba3352d6}} asymmetries in the singly Cabibbo-suppressed {{formula:a552df41-7ce1-40d8-9454-513219faffd0}} meson decays.
The Quantum Chromodynamics (QCD) inspired... | i | 7d5624dcc53337be77fabee9bc701f37 |
By the time WMAP and its contemporaries were observing, the field
had matured to the point that common tools were used between
experiments. HEALPixhttp://healpix.jpl.nasa.gov {{cite:2dbd54e4955794cf6d160e3817430e579aa82bbf}} became a de facto standard
for pixelizing the sky, and many experiments began to use Conjugate
... | i | 8402363b5a626259edb78525f3d4644d |
The method proposed based on adversarial validation in this paper can not only judge whether the dataset distribution is consistent, but also further balance the training and testing sets. Specifically, gradient boosting decision tree (GBDT) {{cite:756424b59f60c7dfde265b0993c0c4296c53480e}} is used as the classifier fo... | m | 7383da84c120d76fbfcbbde945706e8e |
The Reissner-Nordström spacetime for {{formula:364cca04-548e-484d-87db-b94d7a306bd7}} ,
where {{formula:2914158c-9b0f-4ec1-951f-2deb81cf2dfa}} and {{formula:7a6ff0c6-1357-4cc3-aa16-579f3200af94}} are its electrical charge and its mass, respectively,
does not have an event horizon but it has an antiphoton sphere and a... | i | 5d6fe426d10c98ed645b9db331dbad71 |
We set {{formula:3efed6ae-7545-44df-8e79-c633bac979b6}} and {{formula:d4263c5e-3d04-46e6-b2cc-ec240cacbb02}} .
As with the previous experiment, we show representative examples of distinctive dynamic modes and their temporal profiles in fig:eeg.
While such information alone is not necessarily sufficient for analyzing E... | r | 6d9797506e580e68143c84113d48a484 |
by, e.g., {{cite:ef99f027ef5fd9afbc537574dcd91dd985862e84}}. Thus {{formula:de7e5a60-aa8f-4e32-9727-69afe4964f72}} for {{formula:21dec757-b8c1-4c41-b137-ab6de1f192ba}} and (REF ) is satisfied.
| r | 5bf46bf5d23fb26f493279323907be59 |
The motivation of the tracklet booster is simple yet effective. We take the tracklets from any tracklet generators {{cite:e2b0550b582f2e1629d8af11169cc18a3db00f71}}, {{cite:56d4c686d16e01a154840654bf79936447f2fde0}}, {{cite:7ccba6239e875241f04475d40b47ff05959d6102}} or preliminary tracking results as the input. Due to ... | m | ab038a97e225df2f50cd01a8fcc3c165 |
The second challenge is the communication between the computing nodes. Although GPU RDMA is discussed in {{cite:787076a8cfd728d4946d3d6dae64cdb9fce54d5e}}, the inter-node communication through the network is slow and has incomparable bandwidth with the inner-node PCI-E data transfer—frequent inter-node synchronizations... | i | ba2862b1a1fbee7a8286f318a5365657 |
About 15 years ago there was the claim that
a meteor originating from comet 114P/Wiseman-Skiff was photographed in the Martian sky by the Mars exploration rover Spirit
{{cite:f94de72aa7aa84f2b9c5797bfb5e922e8d43dc22}}. However, later work quantified the effects of cosmic ray hits on the Spirit
Pancam as part of a dedic... | d | 38ba5e61b79afede0e11b2dc8164777b |
The contour {{formula:dbc1d2b9-120e-4022-9f30-07a8dc3822bc}} is the one appropriate for an inverse Laplace transform, running along the imaginary direction with a real part such that all singularities are to the left. This solution matches the one found in {{cite:253664401f3550b34a8093c507150befc0000948}}, {{cite:165d... | i | f874f25630e99e8628d92dfabcaf166a |
Training DNNs on perturbed examples is the primal approach to improve the model robustness {{cite:1fc061ec5921eaaffdd0a9271b5bbb36bf1b7d8c}}, {{cite:9432a04f8dbe076ff482ec2d33a4bc9da31f9227}}. Representative methods include noise injection {{cite:32801b6bdd7443069ddb4d6e1e46af4efc11f8ec}} and PGD-based robust training ... | i | 6f23879c252bc0baa91e5abf03af9274 |
Blind-spot, self-supervised denoising techniques, such as N2V {{cite:bf898ab7942ecdad80653acfbf11052c57a46cba}} and StructN2V {{cite:1fddf124d633933f79eab69b929824e3801b6547}}, remove the common requirement of clean-noisy pairs of data for training a denoising neural network. However, the main drawback of N2V is the ra... | d | 1311787258566a36099fcdb415c55b05 |
The simulation results for optimizing objective (REF ) are presented in Figure REF , where we compare vanilla decentralized (no compression) algorithm with our proposed compressed optimization procedure using {{formula:1b360bb4-3246-47a0-977e-acf6ffc525aa}} {{cite:da3d5c54eeb5513e41a6e6fef0c72c093aff3cf5}}, {{formula:... | r | 7739238d8cd5020fbcd97c2e6529adf3 |
However, in quantum signal processing the classical preprocessing to find interspersing single-qubit rotations
for a given transformation function {{formula:e1ebe994-13d9-4b48-89c0-71ffbee45d3f}} has been so numercially unstable
that it has been unclear whether it can be performed efficiently.
In fact,
Ref. {{cite:a9e... | i | 1a3aa8b8153af31857cba9bae747c86d |
In the rest of the present Section, we are going to establish a connection between the universal scaling functions {{formula:864048c5-4ae6-4dc7-be1f-905c4ba7aef7}} mentioned in Eqs. (REF )-(REF ) with the extreme value theory. This connection was first introduced in {{cite:8581cf8b0e9ea06a2be40e8940db24abce7a11a3}} fo... | r | 2d4a98ad51b8a1afad24ccb12fce19da |
In the following subsections, we present the dataset-aware loss that can be utilized in the multi-dataset training without any label cleaning effort. The dataset-aware loss can be easily combined with existing state-of-the-art softmax based losses, like SphereFace {{cite:97975305de43f5086e2b8826887869f791d77d27}}, {{ci... | m | 00d2f2c4fcfd889f8dddbb2485ba88de |
In order to push forward the state-of-the-art (SOTA) in fake news detection we present an end-to-end deep learning approach based on the Transformer architecture {{cite:5f3ab67abe2af54eefc536dca86650e7cf7c32d3}} at the core of which we incorporate different methods to transform the original text into some condensed for... | i | 0a5410fce4738e22de3e42dd97b01e6a |
Intuition tells us that if our features are invariant to the domain, then the main task should not be affected by the domain of the input. In fact, recent theoretical argument {{cite:05824a323af640fb2b6d36ed1cd20f4f4726c76c}} formally suggests such domain invariance in the feature space as a solution for DG. This motiv... | m | c446aa0a91a8d0b36f42f334e636f264 |
It is difficult to compare the physical scales at which the effective dimensions appear for different {{formula:4b7f6a19-2593-49eb-a8e7-f2713476a4f9}} . One reason for this is that the discrete Laplace-Beltrami operator, constructed from the unweighted incidence matrices of the {{formula:140f48a7-936e-4a25-9922-dcd701a... | d | db6f09c3e54c10d5f86705d4ceefb4fb |
- For the first time in the literature, we propose a novel data-free approach, MINIMAL (MINIng Models for AdversariaL triggers), to craft universal adversarial triggers for natural language processing models and achieve state-of-the-art success (adversarial) rates (§). We show the efficacy of the triggers generated usi... | i | d470769438be3d68b7f6ef6cd4bdb11e |
[h!]
Input: data and label {{formula:b2e5abdc-a0dd-497d-9c0e-f3f5af8b34b0}} and unlabeled data {{formula:fb9c831b-9c99-4937-a747-1a1e8313d9f3}}
Init: set training step {{formula:51fc9552-0c92-4590-bcc0-ebc415f7b072}} , total training steps {{formula:cb94651f-9f9b-4813-bc67-b3c3fde91b6c}} , generation step K, randoml... | m | 5d4b0e852d4c1a0cb05e51e4dc4c5d93 |
GAN inversion is the process of finding the latent space representation of a given image.
There are two approaches for GAN inversion in the literature, optimization-based and learning-based, as described in a review paper by Xia {{cite:b7c7cc4f4830f88807e1be5fbc39d02e5952616b}}.
Optimization-based GAN inversion {{cite:... | m | 2909d463ececceeee95040afb01df32b |
A second implication is that ID and OOD performance are not necessarily coupled.
Without further assumptions, in-domain validation is not a reliable model selection strategy for OOD performance despite contradictory suggestions made in the literature {{cite:f06e651e7f959d312f6396c2d33619909e6adba0}}, {{cite:596a7077700... | d | cc42d78ea335a0b72000b509a117050a |
As mentioned previously, the performance of our method is not well-captured by comparisons to the original image (using, for example, PSNR or SSIM). Performance should ultimately be tested using experiments with human observers, which might be approximated using using a no-reference perceptual quality metric (e.g., {{c... | d | 90561b0deb1e883193e4145be58da7e3 |
Recently, the layered label propagation algorithm (LayeredLPA) has been introduced in {{cite:5e1b5bac8c829c5494eee8b4925f680cd49c7a4f}}. This propagation method is based on the Potts model {{cite:8fdf094f01923e2135731354581b4de112cb2a08}}. This algorithm is significantly more successful than natural, random, lexicograp... | r | c40e11fa4daf00d7ba2c03f2d4a05c6d |
Besides rank aggregation methods we also experimented with a supervised learning-to-rank approach, specifically the LambdaRank {{cite:baa4ff46e0102032488981a2a5efa19daf15289e}} implementation from the XGBoosthttps://github.com/dmlc/xgboost package. In this case, for each training query, we collected the relevant passag... | m | 1cacaf3079ad4a6007a722647251ea75 |
We further quantify this through the “residual,” computed as the difference between the BayesWave and Bilby reconstructions.
Specifically, we subtract the maximum likelihood Bilby waveform from the upper and lower 90% credible intervals of the BayesWave reconstruction as a function of time to obtain a region for the re... | r | 7b1d068bc1f6d088333627598f2c9b2b |
The third interference term in eq. (REF ) basically allows to distinguish the initial flavour of neutral kaon.
Parameters {{formula:51eb6524-361b-421e-9f78-2c1dadc24d1c}} and {{formula:a847548c-cd8e-4903-b9fd-e1def5771bea}} have been measured with great precision and current world averages (assuming CPT invariance) a... | m | e78d0b2559061a46e95b593c01d21a65 |
For quantitative evaluation, we compared the Dice similarity score in Tables REF and REF , which used 50 or 60 subjects for training, respectively. Note that the larger Dice similarity score indicates the better segmentation performance. The best results are bolded. With 50 subjects for training, our SSL framework out... | r | f421596ace32ad14e25206aebb98046b |
This result shows that our algorithm generalizes the
regret and long-term constraint bounds of {{cite:4f20d2fa5df1c6303cd4efc2344780468c959220}}.
Please refer to the Appendix for this section's proofs.
| r | 24a0f710e4cfcc9ddebf1e85ce239d05 |
Measurements of the accretion shock feature, on the other hand, are currently still dominated by the instrument noise. We have partially worked around this issue by stacking {{formula:340e8d9a-4b5f-4b35-890e-dc669aa94d36}} clusters, which significantly lowers the noise amplitude, but this still results in only a weak ... | d | 1a8be8fb0c823f78e6206a190f1ee052 |
Using simulated data sets, we first examine the performance of the maximum likelihood estimation method used to estimate the parameters of the linear motion model of a moving molecule in terms of the bias of the method. The bias is assessed by the average of the deviations of the estimates from the true value. For this... | r | 2f03e5eab46d49d2529a18e262130238 |
when {{formula:88c03c44-011f-4de4-817a-ab067fb4e1a1}} is non-singular.
In (REF ), the inequality constraints becomes equality constraints since the equality should hold at the optimum.
The optimal value of (REF ), named Sato upper bound {{cite:1e581a6a270c789398596c2ed157d2bcef9bc08c}}, {{cite:42179725800a1ea3d252c144... | d | 7b83e18e0e2e5e5f41755fc7053573a9 |
To tackle the first problem, as illustrated in Fig. REF , we argue that if the network could be supervised by more fine-grained labels, more object regions will be activated to provide sufficient information for differentiating different classes. Therefore, in this work, we propose the Visual Words Learning (VWL) modul... | i | 38b106c49814270c425ee1ab7434d026 |
Cosmology and notation. We assume a universe with dimensionless energy densities at the current time in total matter (baryons plus dark matter) {{formula:90425ee1-1a8f-4c98-9b2c-da567667bff0}} and vacuum energy {{formula:9e3573e0-70d5-4607-af05-027aeb705484}} , with Hubble constant {{formula:080223a4-158d-4c12-bb88-e3... | m | 5d7071a882849ebcdfc99153731e853b |
The lack of a probabilistic interpretation in a convolutional neural network under this sparse coding interpretation may be problematic because the prior expectation in the model via the prior distribution (exponential distribution) is lost. This issue may contribute to fooling convolutional neural networks {{cite:4365... | d | 2d4a5f8a32eceff2b5812ed0067686a8 |
Before concluding this section, we elaborate on the choice of the parameters {{formula:bcc1020b-77b4-47f1-810e-bc90ab7f1b97}} .
It can be seen (see {{cite:d093db3b93462167ac56bf35bcf3bbfae394c6ba}}), that the speed of convergence of Algorithm REF to the global minimizer of (REF ) is a direct function of the conditioni... | m | b1e352ebf1a67d7e502ab17d09d66747 |
In this study, we argue that the literature has proposed a large array of dimensions of homophily without accounting for the model uncertainty problem inherent in the identification of the determinants of friendship formation. This research aims at initializing such a research line. Focusing on 20 particular individual... | d | 2e4cd10a86470dc88f415b851c7dd82d |
The Soft Gamma-ray Repeater (SGR) J1935+2154 was initially detected by the BAT (Burst Alert Telescope) instrument aboard the Swift satellite as an X-ray burst. Subsequent observations of this source allowed to classify it as a magnetar and they found that the source became active again in April 2020, whilst it exhibite... | i | 2d8a85dc995c588ff576bff9044c45a1 |
See Table REF . We achieve comparable accuracy with much less number of parameters and GFLOPs on Moments in Time {{cite:82e72cc0662ceb2e002fc139d98064628b2baf82}}.
{{table:2fe6314a-871b-446f-bb1d-fa7610327b1d}} | r | 1c40aecbae987991e65435c84c2f7251 |
As our theory suggests, one should add the temperature to the last layer linear classifier; this is in agreement with the results of {{cite:93757f341ae34082562dce9727b9984229334e25}}. However, we find that the class feature means do not converge to an equiangular tight frame as {{cite:37010c682220beb6c92aed3dc6d8d5f28e... | r | d326031ed7ea339882f470e095fa38ec |
The information about the position, direction and energy of the muons hitting the first mu-PSD was analyzed using the ROOT framework {{cite:0e29e0af7326dc18497ac1472ccd926ca451ddf2}}. We simulated three different conditions:
| r | d32561ebf156cb1dc3a2a4c68e7346fc |
where {{formula:c8408769-06a2-4576-9cbd-d45d08d0e562}} when {{formula:3576416c-a164-4f2d-9d88-9199963dd441}} , and {{formula:b3e4fad6-8ca5-4e75-ad06-6b765e669301}} can be chosen to be as small as wanted by choosing {{formula:528df51f-c802-4bdf-8794-cd8a78b023dc}} small enough.
We note that the proof of {{cite:d9e7c... | r | 08538fb811a495f1815c8eb7fe3ee599 |
Dark matter halos are present in the vast majority of galaxies, and thus one has considered the formation of traversable wormholes inside them (see, e.g., {{cite:4cde94e531b6b558e6f2806a5325959c583ea2a3}}, {{cite:9b25df5425828e9f8c16b94e1e9e1c66f685c26d}}, {{cite:e6ff5a4e54f077a4c8a01887d6e177711715a08f}}). These hypot... | i | c724dcd8848dc8bf16ef900c450d80e2 |
Due to presence of non-Gaussian terms, it becomes inconvenient to cast the effective action into stochastic Langevin type equation {{cite:b97564e831e1de0dcaaf0251123ff880a8d32efd}}. Nevertheless, we successfully convert the non-Gaussian effective action into a deterministic Fokker-Planck type equation, which correspond... | d | fa3e13092dc21dc57fc8ba3da17731ac |
A common feature of these analyses is that they study (deterministic) gradient descents (GD), while it has been shown empirically that stochasticity may be of primary importance to match best generalisation guarantees {{cite:01016cb82c880ab028ef50cf013386c2ce80fe69}}. Hence, it is natural to try to understand the role ... | i | 6efd72c47dad667d60edc4cd67f83ab7 |
The overall picture we seem to find – that large models can learn a wide variety of skills, including alignment, in a mutually compatible way – does not seem very surprising. Behaving in an aligned fashion is just another capability, and many works have shown that larger models are more capable {{cite:362e8c9d39128e688... | d | f5c7ebbd9b51d76feda5bdfd2c2f8a1c |
As shown in Table REF , our proposed method outperforms existing unsupervised methods with a considerable margin. Compared to weakly-supervised methods with image-label supervisions, our method achieves better performance on all benchmarks. It proves that the subitizing supervision helps boost the saliency detection ta... | m | cc981deeb99ebf3f4373bea860c132bb |
The perspective of cerebellum foliation as the action of a nonlinear oscillator can be a useful one given the extensive theoretical studies of such oscillators {{cite:cc0eb260d412bbfe20c4432f9a8a21a049dc166b}}, {{cite:150608136096f672aca02898ecff45c751457b3e}}. For BWBM of the cerebellum, the linear model with constant... | d | f99114e003d96bba2a6bd84f550e5976 |
We assume that the gas has a uniform solar metallicity and adopt the standard ratio of helium to hydrogen, and abundances of carbon and oxygen taken from {{cite:2d04fa5cc41a577079e02469fc338e3ab5ad6f4d}}, i.e. {{formula:e84b81df-1009-4f2d-859c-8ddd395378d9}} and {{formula:c86d2789-998e-4c39-bb31-e3e35ce8fde8}} , where... | m | d0963d7e9cda3d0d50cc42fd4e0dc14f |
This research has made use of data obtained with the Global Millimeter VLBI Array (GMVA), which consists of telescopes operated by the MPIfR, IRAM, Onsala, Metsahovi, Yebes, the Korean VLBI Network, the Greenland Telescope, the Green Bank Observatory and the Very Long Baseline Array (VLBA). The VLBA and the GBT are a f... | d | f4a54c8ab9d305f8dc1a58869a33bffc |
In recent years, attributed to the wide deployment of smart devices, the interest in speech-based applications has been rapidly growing.
One major sub-field that is gaining popularity is speech-based identity recognition task, which includes speaker verification, language identification, and voice spoof detection.
Sinc... | i | 2e7cf3e860e5ca3d3954856ff00caf96 |
Finally we comment on the properties of the finite
temperature holographic two-component superfluid that are different from that of the weakly coupled zero temperature
two-component BECs studied in {{cite:42dc4550a4a36d555d768e9dca6267ca39e690e3}}, {{cite:597e5c9fc2853dbbdfa3763b5a849d5d5a2c6011}}. Firstly, in the fini... | d | f85d15d9053de210046de9ca1f932dbf |
The simulation of quantum many-body systems on quantum computers has
natural advantages by avoiding the exponential scaling of computing costs on classical computers {{cite:0abb47209cf2f340d8a4e4becd3a43a4a6a48c49}}.
Atomic nuclei are strongly correlated finite quantum many-body systems, for which
the accurate treatmen... | i | 7d9a44121baea41eb34201a440d39c4b |
Estimating click-through rate (CTR) and conversion rate (CVR) accurately plays a vital role in E-commerce search and recommendation systems. It helps discover valuable products and better understand users' purchasing intention. Due to the huge commercial values, much efforts have been devoted to designing intelligent C... | i | f2fb8342f5b73f0bf40882c7ba54fbf6 |
We shall now reproduce the experiments form Sections REF & REF with different feature importance methods. To approximate Shapley values, we use the Monte-Carlo sampling from {{cite:65af1e13ffdff5152df5b5debd5aa67b908f5f9a}}. We found this approach more computationally efficient than KernelShap.
| m | 01917f680ed01917f2da1c4bfdc601c1 |
{{formula:cb64aa11-6b30-435d-b5f2-0f9640bc217f}} Taking into account the sections 2 and 3 of {{cite:b15230f2ab2b7264bc276af0df71a27789ece23b}}, the
next theorem can be resulted (Find more information in
({{cite:4d3c4d5785192da58ffe9877028bb56e00816ab6}}, Chapter 3).
| d | bb835a66b1e4352d768c80fe1525747c |
Similarly to Algebraic Triangulation proposed by {{cite:73ed7b6f8cd32ed7e86d9f24663527e69d7d931c}}, our method consists of a 2D backbone network applied to each view, followed by a differentiable triangulation step.
Unlike {{cite:73ed7b6f8cd32ed7e86d9f24663527e69d7d931c}}, our method directly models keypoint uncertaint... | m | 22ecb5c806b8aa87a4e8f3673465b7e9 |
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