text stringlengths 54 548k | label stringclasses 4
values | id_ stringlengths 32 32 |
|---|---|---|
The measured correlation functions for the four analyzed particle
species (pions, kaons, protons, lambdas) are shown in
Fig. REF . In the measurement of correlations in pp collisions at LHC energies a distinct near-side peak at {{formula:2bd7026c-ef43-40f4-ac2e-8eb9be2f7566}} about {{formula:8e1e04e5-82b3-40e1-a9b6-82462b4067e5}} is observed {{cite:b102ac771be1d88fa33469be490f2695f7b9288e}}, {{cite:418a35bd8529d963c3908731f02d7bbe591f57b7}}, {{cite:f94455a526b1a7be93a66456636f56618c1baa85}},
which is a combination of at least three effects: (i) fragmentation of hard-scattered partons, (ii) resonance decays, and (iii) femtoscopic correlations.
(i) The fragmentation originating from low momentum-transfer scatterings, sometimes referred to as mini-jets {{cite:1b7a8bcbbc7c8b272014bbeb102d75a8efb26696}},
produces a broad structure extending at least over one unit in {{formula:340e64f9-3238-4778-83ef-cd6a63e8d48d}}
and {{formula:c9a06cf3-625b-4b2e-b728-ba7b458175b9}} .
(ii) The decay of
resonances contributes to the near-side peak of the correlation function or produces a ridge at {{formula:4f07d0e5-087c-4288-a92a-776a4783b527}} (extended in
{{formula:7e161da9-8455-4c57-b146-0dfc7e9b0d7e}} {{cite:4d44f5dfb0303428b0c05bcf1222da36246de13b}}, {{cite:b102ac771be1d88fa33469be490f2695f7b9288e}}, {{cite:a99b6fe67e9d6ef1054fbb7d38bbd74572fb0427}}), depending on the released kinetic energy of a given resonance. This effect plays a significant role only for correlation functions of unlike-sign particle pairs.
(iii) The third effect, femtoscopic correlations (an enhancement due to Bose-Einstein quantum statistics for identical bosons, a suppression due to Fermi-Dirac quantum statistics for identical fermions, as well as Coulomb and strong final-state interactions), is present for particles at low relative momenta. The shape of this effect in {{formula:4efd1815-4bb0-43a6-b1e3-168ddd9599a4}} depends strongly on the mass of the particle type considered, as well
as on the size of the particle-emitting system. For pp collisions at
ALICE this size was measured in great detail with
pions {{cite:53b3799d5fdc156c0511dc39bb0bb0521b72e2d0}} and kaons {{cite:87aee3620e76651b61a0131f38c0d10d0866ae8b}}. The
expected width of the correlation peak produced by like-sign charged particles, e.g., pions, is comparable to the
one for the mini-jet peak.
In addition, by
constraints on the energy-momentum conservation, an “away-side ridge” structure at {{formula:8b984c5a-8835-4db8-abb3-1b5c6b684bf0}} , with a magnitude only weakly changing with {{formula:1bd15232-1556-41a7-a828-2e75ada9d638}} , is produced as well.
{{figure:7a9b7d5f-6cc6-4dd0-968a-2d887e7acdfd}} | r | 548951d70c970fa38fb7de6ac2a5da0f |
NumPy combines the expressive power of array programming,
the performance of C, and
the readability, usability, and versatility of Python in a mature,
well-tested, well-documented, and community-developed library.
Libraries in the scientific Python ecosystem provide fast implementations of most important algorithms.
Where extreme optimization is warranted, compiled languages such as
Cython {{cite:10164b143f7cfd826699013a3bf34a5d08f00435}}, Numba {{cite:5cb019314f48a82bf3ce1e5588b5585b4fff7fdd}},
and Pythran {{cite:5953f562042233a1a863b32b8b03203ca8ceb874}}, that
extend Python and transparently accelerate bottlenecks, can be
used.
Because of NumPy's simple memory model, it is easy to write low-level, hand-optimized code, usually in C
or Fortran, to manipulate NumPy arrays and pass them back to
Python.
Furthermore, using array protocols, it is possible to utilize the full
spectrum of specialized hardware acceleration with minimal changes to
existing code.
| d | dd9c8779126d3909793a5d27aba7fde4 |
While our proof of Koszulity in the Euclidean case relies on {{formula:8e2ed4b3-bb3d-41ed-81a9-fa5c89169afe}} being quasi-hereditary, it is conceivable that {{formula:a9ee629a-a1f1-4c0c-a90c-bdedc36aef18}} is Koszul more generally. As evidence in this direction, in Theorem REF we prove the Hilbert series of {{formula:f687ddca-eaa1-4c8a-b964-21bc8ab07d20}} and {{formula:ffe386c1-e229-4508-9ee9-9284f76f32f3}} satisfy the numerical identity discussed in {{cite:907b28c63268197e5e94073ae0396de85cdb496e}}.
| r | 58051ac8b91b449dd28fb9c4d387e74c |
Here, we explain the possible reasons. Firstly, the low SNR of spectra from SDSS leads to the large uncertainty when calculating the age of the galaxies. Combining spectra from various observation project may be a solution, as discussed in {{cite:8eb071eeab06a6791bdf4864e36a47770cb0e6e3}}, {{cite:ecca7fbbc24fc2269cab88b0eef6bf30960014c7}}. Comparing our points with the previous, especially those in {{cite:be6e1ed4a09b99b54b07c04da41933bb6391bcf9}} and {{cite:a915918e2bb7f5afedf23b781988fbe4b1991b17}}, although there really exists a big difference in terms of the accuracy, we would like to illustrate as followings. One is a theoretically good method to obtain the age of LRG in {{cite:a915918e2bb7f5afedf23b781988fbe4b1991b17}}. Still, there are many problems in the method to be solved. The other is the results in {{cite:be6e1ed4a09b99b54b07c04da41933bb6391bcf9}} with more accurate than ours because of the high quality of their spectra. OHD data points in {{cite:ecca7fbbc24fc2269cab88b0eef6bf30960014c7}}, whose quality of spectrum and method of fitting spectra are similar to ours, share the comparable degree of accuracy with us, which also proves the objectivity of our OHD data points.
| d | f85832383e6ddf8b272d38172f74d08c |
It now follows from Theorem 2.7.11 of {{cite:a18a4303f524820aa533f31c9127d34e23681881}} that
{{formula:21b9a6c6-9285-47b0-b07e-139eaff0cded}}
| r | 7f50fed4b9172089cc4e6dc5a4d8a2fc |
Visual Storytelling (VST) {{cite:b4c91d4ba683d3f954115485ee257831d6c828ae}}, {{cite:2467c4be51f2b0720d4f9211aa06d967b27a829b}} – the task of generating a story based on a sequence of images – goes beyond a basic understanding of visual scenes and can be applied in many real-world scenarios, e.g., to support the visually impaired. Moreover, VST reflects on the creative ability of intelligent systems. Although similar in concept to other cognitive tasks such as image captioning and visual question answering, VST differs as it requires to reason over a sequence of images while simultaneously ensuring coherence across multiple generated sentences.
To achieve this, VST methods need to address two major challenges: the first is visual and relates to grounding the story's text to the images. The second is linguistic and relates to the quality of the story. Both challenges can be described in terms of coherency: the story should be coherent by itself, and coherent with the images.
| i | 6042dd1f1e846df476ee2511ddecb422 |
We also conducted a user study to evaluate the quality of the generated music-conditioned dances.
All the 100 sequences of the validation set of PhantomDance were used for the study.
And then we collect the generated dances by our method and the compared baselines {{cite:2d85c7bd73c93d2bf7612e5a97ea4c37b1162117}}, {{cite:e37c2eb6ca6481fc8f4b4a5972a730678593cf56}}, {{cite:d2ee74fcbf8f05bc60800b1354e22a1a8de955e1}}.
In addition, the ground-truth dances are also included.
The user study was conducted using a pairwise comparison scheme.
For each of the 100 musics, we provide 4 pairs in which our results occur with the results from the baseline methods or the ground truth.
Thus 400 pairs were provided to the participants, and they were asked to make two choices for each pair:
“Which dance is a better performance (more fluent, graceful and pleasing)?” and
“Which dance matches the music better?”.
There are 100 participants in the user study.
Figure REF shows the user study results, where our DanceFormer outperforms the other methods on both criteria.
Most of the participants rated that our method generates better dances in performance quality compared with other works, and even more participants held the opinion that the dances generated by our model better match the musics.
| r | 5e7b93560b553120c9f1ae69d18ba90e |
The saliency regions of MNIST dataset are morerestricted to the number regions that covers the whole image, while the saliency regions of CIFAR dataset spans beyond the target object, i.e., the texture around the target object. Thus, 1) this will maximize the probability of distracting the cnn model by giving importance to non-relevant regions, and 2) any small perturbation added to such images will highly affect its saliency and, as a consequence, the cnn will target another prediction class.
Looking at the cam and guided cam, we can notice that the cnn does not necessarily use saliency regions of clean images in predicting the correct class. This happens due to the use of complex/over-parametrized classifiers to solve not complex tasks {{cite:6abe986a3eac0336ef38dafb91382bdbba789f07}}. Hence, cnn are vulnerable to small perturbation that will cause higher loss than of clean samples. This can be confirmed as well by looking at the cam and the guided cam of the ae and notice that different small perturbations from different attacks yield different cam and guided cam.
Most of ae detectors solutions rely on that the representative cnn layers output of adversarial input is significantly different from clean input, which is a true assumption. On MNIST, it was easy for most of the detectors but why detectors, on other datasets, not always able to detect ae? That might be due to 1) dataset has noisy samples, 2) cnn either very complex or very over-parametrized, 3) the behaviors of the cnn with respect to the content itself of clean and adversarial inputs, for instance, in the case where the guided cam, i.e., important regions, of the ae is slightly changed from the clean one, the detector work became much harder.
{{figure:7edba023-7e10-4589-aeca-49a5d02a3e0b}}{{figure:df9276ac-7e2a-4e66-8643-1f60433257b9}}{{figure:2141c6bb-dbd3-4c75-9fde-8ef22657fde0}}{{figure:c171c776-f9d8-4190-8238-ce404375ab19}} | d | 60458f3aa6d539f8b97df2d773e9691f |
The motivation for seeking this conformal basis is to understand the holographic nature of quantum gravity in asymptotically flat spacetimes. Realization of the holographic duality {{cite:e23d50d5716bdc3a2aeb0879359a1d6c2e52872c}}, {{cite:f73adb1b7a62d3d7f793d691121e8d26594f88e6}}, {{cite:1a2f196895cab400b5d4f4506d9b2b21f0b40079}} from the bottom up by finding the symmetries that both sides of the holographic dual pair obey {{cite:8dd52b9286e1b6de4687836750d37e28ce3adfab}}, {{cite:7bc3e37abe8742839262a17a9d63d8797013dfe2}} gives rise to the enhancement of the Lorentz symmetry to full Virasoro {{cite:7d695209bf42f628b0b4e2af3c064d41ac822343}} and the existence of a stress tensor in a {{formula:e06fa87b-7438-423c-8d00-d915b541f545}} CFT obeying Ward identity constructed from subleading soft-graviton mode in the bulk {{cite:88a35d4523bef44e0434ef649c4598ece0944b71}}. These observations along with the equivalence between soft theorems and asymptotic symmetries i.e., soft theorems recasted as conservation laws associated with large gauge symmetries lead to the proposal that there exists a holographic duality between the theory of gravity in four-dimensional asymptotically flat spacetimes and some sort of exotic CFT living on the two-dimensional celestial sphere at null infinity.
| i | e3f1ffa4f110a91e54bc52a7900305db |
We studied the equilibrium properties of symmetric self-bound droplets of 2D binary BEC beyond the standard LHY treatment,
at both zero and finite temperatures.
We computed higher-order corrections to the excitations spectrum, the sound velocity, the normal and anomalous correlations, and the free energy.
These corrections improve the ground-state energy obtained from the Bogoliubov approach {{cite:a8a4fabd0cfc317cc549aecbc3884a8dc407746e}} predicting an energy in good agreement with recent DMC simulations
owing to the non-negligeable role of higher order terms.
At finite temperature, we revealed that the droplet occurs at temperature well below the BKT transition and destroys when the temperature
becomes slightly larger than the ground-state energy of the droplet due to the thermal fluctuations effects. We found that the interspecies interaction tends to lower the critical temperature.
We analyzed in addition the finite-size droplets in the framework of our generalized finite-temperature GPE.
As outlined above, one can infer that in 2D mixtures, the droplet survives only in an ultradilute regime and at ultralow temperatures.
| d | ab3d4af9e59c8b3f54af22023d771dd1 |
Typical storage and recall of single photons for a 4 {{formula:e3684b21-34b1-45b1-b80d-e23fb02ffa0d}} s duration is presented in Figure REF . Dark/light pink shaded regions indicate the times when the memory is in the read/write modes respectively as indicated by the magnetic field gradient state and presence of the control field shown in Figures REF A and REF B respectively. The red histogram of Figure REF C indicates single photons arriving at the memory during the “no memory” stage while the control field is turned off: they are detected as coincidences with a herald detection on the idler photon produced by the down-conversion source (see Methods for details). The blue histogram in Figure REF D indicates photon coincidences with the memory active and set to delay the signal by 4 {{formula:90d0886c-141e-41fb-9c68-09f75f53abf6}} s.
We exploit the temporal correlation between the twin photons emitted from the SPDC source to provide a high quality single photon signal through the detection in the coincidence basis {{cite:d154d73c35d8971dad819b8e548f53d529bd3664}}, {{cite:f12d32af01c23b25303048408f72d352ee2f09c9}}. This powerful technique enables us to conclusively identify the single photon signal in front of a background of leaked control field photons of {{formula:1fc813c0-0f60-4280-86d4-b44833595fc7}} 5-10k counts/s (See Methods for details). Such strong background would have rendered other, more sensitive single photon characterisation measurements like cross and auto-correlation {{cite:65d41dc3d09bc5f3f80ab73d099b5ccb7fbf7731}} fruitless.
{{figure:a4d5f69c-1abb-426b-8d9a-fa3d62a33d3d}} | r | dc796399a16f0babcd886c21a90f93a5 |
We conduct experiments on the popular OfficeHome PDA benchmark proposed in {{cite:4c026d8b008abff960f3851605aee19add27a53d}}. For this benchmark, we use images from the first 25 classes in alphabetical order as the target domain and images from all 65 classes as the source domain. We make use of the same splits and experimental protocol providedhttps://github.com/thuml/PADA by {{cite:4c026d8b008abff960f3851605aee19add27a53d}} for a fair comparison.
| r | 2cfda300ac56ee10bad95c28b6618506 |
Minimizing term 1 decreases the error over the orbit {{formula:fc9a4e6c-1499-409c-aea8-0e4c7bd32e9a}} , while term 2 penalizes the size of the estimate as measured in the {{formula:3a419c6a-9839-46db-bd79-2ffa0188d6b1}} norm. Increasing {{formula:19de09e5-5abd-4914-8379-e856dab9ff82}} generally leads to smoother estimates. Additionally, it also reduces the chance of over-fitting, which can lead to poor estimates in the presence of a noisy or perturbed data set. This classic phenomenon is studied in great depth in texts like {{cite:0fb7d57630f105a678f5a255704e9afeb9038dc8}}. With these considerations in mind, proper estimates can be generated by selecting a particular choice of {{formula:1f3a3cfd-e7e1-4dea-8ccc-d17a74f63add}} to effectively regularize the estimate.
{{figure:4cef490d-8144-412b-a68a-af922b64d68e}} | m | 1c0b69beb10797959e75e3fd29349ef8 |
Gauge-gravity correspondence relate strongly coupled gauge theories and weakly coupled gravitational theories. In {{cite:530ec66ac871bca36a1a887b27358ce5cd854ade}}, author realise that strongly coupled {{formula:c6093d8f-876d-4dea-b1db-dd6013050e4f}} SYM theory and type IIB supergravity background on {{formula:9ff15192-1abe-451a-bbdf-8bbc9d71eaa5}} are dual to each other. Using gauge-gravity duality we can study many interesting phenomena in strongly coupled gauge theories via a mapping to the gravitational dual. In the gravitational dual, calculations are easily doable which makes gauge-gravity duality more useful to study the strongly coupled gauge theories. If we will incorporate higher derivative terms in the holographic dual of strongly coupled gauge theories then we will be able to explore the intermediate coupling regime of the gauge theories. Effect of higher derivative corrections in gravity dual of {{formula:5007dbeb-d853-4527-893a-fbcc4126875f}} SYM theory at finite temperature was studied in {{cite:0b58c0acfe202a9088b0f99e3e0dd3b64dedf990}}.
| i | d5b83149f623fd80f33ca9b09ec2a57d |
For the VAE-GAN and {{formula:74e42556-e4d2-4d35-a4ec-e537ccc51f12}} -PVAE, enhanced speech can be obtained by waveform reconstruction {{cite:30a22723bcc477a8451644b937f5aa17dff24e41}} or mask estimation {{cite:56b9e2075a7cc47af47418ee475e5a02fa5ad9b9}}. The direct waveform reconstruction is based solely on the speech estimate, while the mask is based on both speech and noise estimate. So, we use {{formula:94c9df27-9e0e-4a45-880b-86e7431baa62}} -PVAE-M and {{formula:66b32971-a309-497e-bc60-f2756e1350e1}} -PVAE-L that represent that the enhanced speech is acquired by mask estimation and direct waveform reconstruction using {{formula:ec3c6829-98fe-48ca-adb7-0b540ba520b0}} -PVAE {{cite:07fa0cd285799dc09f8a58cf03b154bad046f110}}, respectively; VAE-GAN-L and VAE-GAN-M denote that the enhanced speech is obtained by the proposed VAE-GAN using direct waveform reconstruction and mask estimation, respectively. We use the ideal ratio mask {{cite:56b9e2075a7cc47af47418ee475e5a02fa5ad9b9}} that is widely applied in various SE tasks {{cite:56b9e2075a7cc47af47418ee475e5a02fa5ad9b9}}, {{cite:c60044c26b5e8e34d0b496c0550d63c40cce6a23}} to conduct mask estimation.
| m | ba9dfa7adfd06395ddca667939791a2c |
Multi-modal approaches are widely used in the field of action recognition {{cite:3cb76fc849db1f97effcf1459b88a6f206b2edce}}, {{cite:86a4fd6304cd878f9916da63fd8e3c2f4d77e4b6}}, {{cite:cf8b4501cec68e13215a93e8511f853f723f2ba4}}, {{cite:25082f221d60ef558153fba75446f2e3028c072c}}, {{cite:c1a431116402837ae2716d040ea33a6782a44a88}}, {{cite:901fe8076971510faec37782ba23a3bb2f49fa6e}}, {{cite:fa0bcfdeb26e49131578832bb4cdd56f20e98864}}, {{cite:3d03d66fadf4ddef59691f0ffae766aaa40649bb}}, {{cite:3ef084db42fe047c0178594be9232750e1528d96}}, {{cite:77bda477a3066833754d56a8ed141ff510a315ed}}. Augmenting data sources, such as RGB images, optical flow, depth maps, joint heat-maps, and pose skeletons, provide richer semantic cues for neural networks to infer the human action in the scene. Since a comprehensive literature review is beyond the scope of this paper, here we only focus on skeleton-based multi-modal methods.
| m | 53d402cce71e4539a4fa701cd0dd111d |
The questions of how network structures and diversity influence the outcomes of behavioural dynamics, or the roles of network reciprocity, have been studied extensively in many fields, including Computer Science, Physics, Evolutionary Biology and Economics {{cite:e24222296c2822da48445433db377c16eff9a6f1}}, {{cite:00e151de180e5d175036be8bfe0d5c5dfba5032a}}, {{cite:170df2188dc98a85381d91cb13c9f8077a4ae128}}, {{cite:af131e2c0639ecdb0e3061f617e698b06d524672}}, {{cite:25a7d85fc1557e22100c4a684d020e0ca001e084}}, {{cite:8189e3a4500dfd4c2c0fceea5073b715302d4fba}}, {{cite:37a2720317cba156ea7b8b6308c7be85e00dc5d8}}, {{cite:5588288c748b5fcbfb836f059c1c8455f0d7ccbc}}, {{cite:627a0652a888f38f39526e068b5f2bff05ab91d6}}, {{cite:6c6fcae96ab556a0787ebaf9d8c6023d9ee4e531}}. Network reciprocity can promote the evolution of positive behaviours in various settings including cooperation dilemmas {{cite:170df2188dc98a85381d91cb13c9f8077a4ae128}}, {{cite:af131e2c0639ecdb0e3061f617e698b06d524672}}, {{cite:25a7d85fc1557e22100c4a684d020e0ca001e084}}, {{cite:5588288c748b5fcbfb836f059c1c8455f0d7ccbc}}, fairness {{cite:5e12b2670c98202e6552272fcc11fdea89c50cb6}}, {{cite:b7d4282a7e44a6cf96419c8874918936773540e8}}, {{cite:7ca6ee236f106bba3aeedc59557521ab67554c22}} and trust {{cite:06889a609fc2e0645cda1d621380b05652b5bcc5}}.
Their applications are diverse, ranging from healthcare {{cite:ecb5d8bf26b2d2723cf3e61ded2dc3e2048eb3bc}}, to network interference and influence maximization {{cite:1310da7e824e883f82d0e6f1616a9fff92d7b292}}, {{cite:5174857c5856529f2d8bcfcd9f19936e0e5769ff}}, {{cite:0c8a40d05acf866dacaf1af1dd62045ed0554b16}}, and to climate change {{cite:802b8ea4cbcac7dd6b40345304d7f54a925d9dde}}.
The present work contributes new insights to this literature by studying the role of network reciprocity in the context of a technology development race.
This strategy scenario is more intricate than the above-mentioned game theoretical scenarios (i.e., cooperation, trust and fairness) because, on the one hand, whether a social dilemma arises (where a collectively desired behaviour is not selected by evolutionary dynamics) depends on external factors (e.g., risk probability {{formula:0a5bcffd-bfb9-4657-91ae-3b7242537c6f}} in the early regime and monitoring probability {{formula:822e7a5d-5902-4881-b191-3f9e32857a05}} in the late regime) {{cite:5804b776c3ac95e08b32434e401d7eb7aea8914f}}. On the other hand, the collectively desired behaviour in the arisen social dilemma is different depending on the time-scale in which the race occurs. Interestingly, regardless of this more complex nature of the scenario, the different desirable behaviours can always be promoted in heterogeneous networks.
| d | 2c4b31879aec71170da0f55f5b36cbc7 |
Our resummation approach uses well-established diagrammatic arguments {{cite:cacebaa12b62cb1b0640d3a7dd9a40ec96e58a27}}, {{cite:e766bf60c381f20a203324e5062ebac1f195d602}}, {{cite:1e542749b567243eb07a125eb3cb1b5c50e346f9}} to efficiently calculate all-order forms for the purely real emission contributions to partonic structure functions and cross sections at LL order. We then fix the virtual corrections using a variant of the soft gluon unitarity procedure that has been applied at leading power {{cite:22906b6a1bc1f11778ff0b6146233d0b8d1d0419}}, namely by requiring that virtual corrections cancel appropriate higher-order poles in the dimensional regularisation parameter {{formula:a3b28f38-96f0-4971-b060-644d8d58dcb9}} , leaving only those collinear singularities that can be absorbed into the parton distributions. Our all-order forms for the structure functions and cross sections lead straightforwardly to resummed splitting and coefficient functions, once mass factorisation is carried out.
| d | 15b877de2cb3b857d3e45a054304976b |
It has been well known {{cite:3ab3cbe1c11df2b92f6f0b7c5459747c737b1d52}}, {{cite:d0324edf31cceaddf4ef6b4a649fdb51e58e8687}}, {{cite:713c25189945372289953b20f10e886984f7fade}} that, in general, there are 14 (algebraically) independent, second-order,
invariants formed from the Riemann tensor {{formula:adb05469-6afc-43b2-bb60-bcbc7aaa8ccd}} . However, it does not provide any guidance as to how these invariants
may be constructed. An important problem is how much algebraic information that is in the Riemann tensor can be encoded
in its polynomial invariants. An “algebraically complete" set means that it must consist of invariants of the lowest
possible degree, and also it must be the smallest set which contains the maximum number of independent invariants
for any Petrov or Segre type. It was realized {{cite:99014b417089881fdfed268990d5e9b89e8d32ef}}, {{cite:c40fb986fbe40166c023f4873659ab5ae2957b14}}, {{cite:4fce3901e27a96d6b760537fa0d073d32d562832}} that an algebraically complete set should contain
more than 14 invariants and hence, in general, be redundant.
It was shown in {{cite:a24bc41a52ef07fad41932b044ac2ec0f9995655}} that the set of 16 invariants proposed in {{cite:99014b417089881fdfed268990d5e9b89e8d32ef}} is not algebraically complete and
even the set of 17 invariants proposed in {{cite:4fce3901e27a96d6b760537fa0d073d32d562832}} is missing an essential element although it is algebraically complete.
Hence it was suggested in {{cite:a24bc41a52ef07fad41932b044ac2ec0f9995655}}, {{cite:9643ee23f025db59e33a011861df03fd489a849f}}, {{cite:530342263c79448d2642b704bff0a8426e9beb7b}} that an algebraically complete set needs at most the equivalent
of 18 real invariants with a maximum overall degree of 6. It will be interesting to have the explicit expression of these invariants in terms of the irreducible decomposition (REF ) or (REF ), which may be useful to determine
whether the set of 18 invariants proposed in {{cite:a24bc41a52ef07fad41932b044ac2ec0f9995655}} is independent or not.
Any progress in this direction will be reported.
| d | 9acc4bac7bc2c2251b45c65fb50d4dfd |
In spite of the fact that Frobenius' theorem only holds for linear ODE we shall seek solutions defined as power series, as has been already done in previous work {{cite:c992ce66d37c17c440f86340861121e5c0b0416e}}{{cite:513a598edb0f0822be9db64c14bdaa990c14023c}}{{cite:9b1464ed2a96ce5099b43896f7831526617016c0}}{{cite:758c8044dcdac193bb61f27c8ebe54945bf08ea8}}.
{{formula:f7d90452-b62f-40af-a0bf-d393f193fc36}}
| r | 35c3d228850145055105658dd1a860cc |
Quantitative Results
We compare our model with the baseline models on WMT'16 RO-En, XSum, SQuAD dataset for machine translation, text summarization and question generation, respectively. Table REF shows that our proposed method CLAPS significantly outperforms the other baseline, with the performance gain of more than {{formula:67b207ee-39f6-4600-953f-8f9db199e465}} on all tasks according to the BLEU scores. Moreover our proposed method improves the performance of the randomly initialized T5 model (Scratch-CLAPS). For question generation, our proposed method also improves F1/EM as well as BLEU scores. It shows that our proposed model is able to generate semantically valid questions that are beneficial for training the QA model. Note that naively constructing the negative examples for contrastive learning on the both tasks, by randomly shuffling the association of {{formula:8552a069-f8cf-43b4-ad14-35efb4272337}} from a given mini-batch, degrades the performance. Increasing the batch size to a large value, using larger memory, may increase its performance as observed in SimCLR {{cite:c975ef6829cf47cfb5db5ae291ce1c29673ea614}}. However, such an approach will be highly sample-inefficient. On the contrary, our model outperforms all the other baseline models on Xsum dataset for text summarization, as shown in Table REF . For summarization, we observe that contrastive learning with imposters alone can improve the performance by a large margin.
| r | 0498746d79775f3a93e5f5f02407dd6c |
for the solution of the nonlinear system of equations {{formula:1db82484-7a17-425a-8243-d2b7d8f4291c}} is known to be
superlinearly or quadratically convergent to a solution {{formula:aefe7f0e-9a55-4c8e-9b86-25a8d2337053}} , if the solution is
BD-regular and {{formula:d0c0a07d-91bf-483f-ac2c-0fabe0107a73}} satisfies an additional smoothness property called semismoothness
and strong semismoothness, respectively. For the precise definitions and proofs of
the previous statements, the interested reader is referred to the papers {{cite:7f53fe6a857bad1aa3e024b30539e8c39b3632a6}}, {{cite:8d8e13f8f20b49e60991a3136abad9ab8ee06f4b}}
and the monograph {{cite:9b24ceed8b83c48a04d2407dbf3252eeedcb9252}}.
| m | 4120f4ec14c794a3c23d138ec2078b0f |
We also compare our method with other efficient ViTs aiming at reducing redundancy. Most of the existing efforts target only efficient inference. They either apply to the fine-tuning phase {{cite:34f1a05b9c3a5bc51b63bdb7062f183847f17b85}}, {{cite:fcf7a80664191105d62f20e38fca10006dc8044b}} or introduce additional modules with additional training cost {{cite:d454adafe35f6c3c3d36aefd0ea12acb91419ae0}}, {{cite:7362bf879683ae9835a48fae24f482781860b786}}.
In contrast to others, we can accelerate both the training and inference process. Our method significantly reduces the training time while restricting the accuracy drop in a relatively small range. Notably, we are able to reduce the training time by {{formula:b7657476-fc82-4786-93da-94d4e8e4b9fa}} for DieT-T with a negligible {{formula:af49329a-1154-43bc-a423-08651b7edc52}} accuracy degradation, and {{formula:27bccf1a-2097-4035-9ea5-897fdffa14f6}} for DieT-S with a {{formula:01137192-4968-497a-ae43-2595490b6bf7}} decrease in accuracy, which outperforms existing pruning methods in terms of accuracy and efficiency.
| r | a80f854795c486c72d4c70ae7430f5f0 |
In this case study, we make use of the highD dataset {{cite:7b97193f363bfca0478f3cacd433781d07fdeea7}} to show the potential of our proposed method. The highD dataset consists of traffic data recorded in Germany at 6 different highway locations. The dataset is made up of 60 independent recordings. To visualize the data and generate the images used in this work, we used the TraViA visualization software {{cite:72dfbb74d832864afba92c65f5f9216195eec563}}. The source code implementing the proposed method is publicly available as an extension to TraViA {{cite:0570c5f6b3b7fba736427e2d92da0d76a174dfa6}}.
| m | 90313b2ec1f4517f24200b4b870f0be0 |
The first method we consider to derive the diffusive jump rates is the FDM. Meinecke and Lötsedt {{cite:02738d590167d61a446de500ea732fcb90ad4a59}} chose a 9-point stencil which allows one to include a parameter {{formula:ee96e2bf-5bfc-484b-a8fc-1442f890e38d}} to study the effects of diagonal jumps, while maintaining a discretisation of the Laplacian that is still second order accurate {{cite:65b4f7686a98a0e95723d44dc72353616de97b30}}. The standard 5-point stencil can be recovered by setting {{formula:e3a0a36f-5acc-4035-a6b3-38b6a26c9378}} and hence neglecting diagonal jumps. We have
{{formula:33376b49-addd-4dba-9d38-a3e59908ac0e}}
| m | acb00d96e28e0593d75b746477286d58 |
where {{formula:64e3861f-4829-4b45-978f-ea352ad580ac}} is the {{formula:3f477707-59f9-4120-b16f-1942059474fb}} identity matrix and {{formula:31d11fa9-c8e5-4335-a03c-ecaa68c95d24}} is
a small positive value whose role is to make sure that
{{formula:3139860a-671a-4c66-9e95-07131e9fcc70}} is not singular
{{cite:90e9d591c955e7c6e70dcea5f63bd832520d2764}}, {{cite:99076bd567c4dbf2c59a3893b61711d4eb1e56ff}}.
The AM algorithm of {{cite:99076bd567c4dbf2c59a3893b61711d4eb1e56ff}} can be
summarized as follows:
| m | bd14db5425c4bbe6e0544b3dd63e4b4f |
In relation to the previous work on imposing bias from biological networks via graph convolutions, we compare in our experiments the graph generated using ontology embeddings with two graph datasets
containing a mixture of protein-protein interaction and gene
co-expression data: GeneMANIA {{cite:b1afcf3b9ca818a70d4cfc314ce8a088c296205f}} and STRINGdb {{cite:ffffb879ffd7608bd0a9730e3b4a48286408adb9}}. We also consider a baseline of a graph with randomly generated edges (with matching degree). Such a baseline allows determining if performance gains come from the model itself or the underlying prior biological knowledge.
The results in Table REF suggest that curated biological networks are a good source of prior knowledge. The slightly improved performance in case of the automatically generated ontology graph is probably caused by the ability to freely tune the sparsity of the graph, which regularises the sizes of kernels in convolution and overcomes the problems caused by sparsely connected genes in the network {{cite:5e692afa5b31313acc33ebf975f0ce17cbc3b207}}, {{cite:7cc599fcff9baf5762c9fe08c5660177db694e2f}}.
| r | 1b37bde92918ec4f816dc0698fea43cd |
A* is an informed search algorithm {{cite:b04bae46068e88b2b9a6ad26e882cbce1ff8ac78}}, meaning that it is formulated in terms of weighted graphs: the algorithm starts from a specific node of a graph and it aims to find a path to a given goal node having the smallest total cost.
The cost is defined, for instance, as the least distance travelled or the shortest time.
The main loop is done maintaining a tree of paths originating from the starting node and extending those paths one edge at a time until its termination criterion is satisfied.
At each iteration of its main loop, A* needs to determine which paths to extend.
It does so based on the cost of the path just travelled plus an estimate of the cost required to extend the route to the goal.
A* selects the path that minimises
{{formula:aecb5e8e-bda7-4b48-a379-0e3d1d7a787e}}
| m | db4cce50f48b14fb8e6a31dcec41b92c |
This paper introduces WildWood (WW), a new ensemble method of Random Forest (RF) type {{cite:a771942330fe3b4da52e105c022e02a043682cec}}.
The main contributions of the paper and the main advantages of WW are as follows.
Firstly, we use out-of-bag samples (trees in a RF use different bootstrapped samples) very differently than what is done in standard RF {{cite:a8ca91151b6334e702938815198df94b3adf5f8a}}, {{cite:459efd0076d7c652cc750179a6495b1369dc4adf}}.
Indeed, WW uses these samples to compute an aggregation of the predictions of all possible subtrees of each tree in the forest, using aggregation with exponential weights {{cite:e8e1d526b36f8db6078990dc930051f4a8d7f05f}}.
This leads to much improved predictions: while only leaves contribute to the predictions of a tree in standard RF, the full tree structure contributes to predictions in WW.
An illustration of this effect is given in Figure REF on a toy binary classification example, where we can observe that subtrees aggregation leads to improved and regularized decision functions for each individual tree and for the forest.
{{figure:f5a3f885-ba31-4cfb-9ee2-935f8e3918ce}} | i | 4a36b5d4e6bde3941cddbadb8f729a79 |
The uncertainty associated with future events has been addressed by some approaches {{cite:f7adae0903e7831a187d2d9bbf7072dc99c835e9}}, {{cite:521fa675aff758c68da1163b05247567a94e4c33}}. Similar to Farha et al. {{cite:f7adae0903e7831a187d2d9bbf7072dc99c835e9}}, we do so by learning a probabilistic prediction model. Bayesian Deep Learning through Monte-Carlo Dropout provides a framework for estimating uncertainties in deep neural networks {{cite:836ff3ecbdff8b6fde759a542b1eb530c3f10698}}. Kendall et al. {{cite:b40449e35f74962cdb71f155aac3c506df39d241}} identify the model and data as two relevant sources of uncertainty in machine learning. Several approaches have been proposed for estimating these quantities {{cite:aed7ac22f07b99116a4ee110374901b605bc36ee}}, {{cite:a33b711ee4261cd5e60a72646dd756ca371123e8}}, {{cite:e3e3860bcb3e4622730644ed22fb57bea2781920}}, {{cite:b846a76438c13d6bc222e694746a54aa4f7ae06d}}. We evaluate our model's ability to quantify task-relevant uncertainties using these insights. The contributions of this work can be summarized as follows:
| i | a7a0e11c1b1f11bf4a94ff5ca4860f02 |
However, a key issue with GNNs is their depth limitations. It has been observed that deeply stacking the
layers often results in significantly worse performance for GNNs, such as GCN and GAT. This drop is associated with many factors, including the
vanishing gradients during back-propagation, overfitting due to the increasing number of parameters, as
well as the phenomenon called over-smoothing. {{cite:9af90c0a556ac0466e064ddff5ccc0021a040743}} was the first to call attention to the
over-smoothing problem. Having shown that the graph convolution is a type of Laplacian smoothing,
they proved that after repeatedly applying Laplacian smoothing many times, the features of the nodes
in the (connected) graph would converge to similar values. Later, several others have alluded to the same problem. {{cite:e04592e21810fc3039d93e528bc7b2d95caf0a8d}}, {{cite:d4d02b1af9fc9a7a161a37a6a7ff7aab6b00bb15}}, {{cite:96c8a22ef5e18b1074b57415ae1baf5b85c39f0f}}
| i | 196320162d008cb68a9ce5c03a8d505f |
The advancement of object detection and segmentation techniques in natural scene images has been driven largely by permissively licensed open datasets. For example, significant research has been galvanized by datasets such as ImageNet {{cite:0d31a75b61d5b13d564e07c03d084b98e12debfb}}, MSCOCO {{cite:4069e9053038b22747564e4c8c7922c594806830}} and PASCALVOC {{cite:8f32432c937c2cbe2bf02552c76c9cf7a9b53ef2}}, among others. Additionally, multi-modal datasets continue to be developed, with a major focus on 3D challenges, such as PASCAL3D+ {{cite:2159fc35912e0c052e242bd3ea474d460ad2b05e}}, Berkeley MHAD {{cite:c3fe8f58eea77475a86d14dd81977199aac61089}}, Falling Things {{cite:53e6f7e982cafe5ef2ce1e3ba15edbc943716ba6}}, or ObjectNet3D {{cite:7c358a5324bee631dc61801cadc46c25a7574a52}}. Other modalities such as radar remain generally unexplored with very few ground based radar datasets, most of which are focused on autonomous driving such as EuRAD {{cite:a5e3964b9bf7fb85a2d49a15f0b1c91b575fd425}} and NuScenes {{cite:7574cdeb5eff21ee62c21c82a799e5f5283dd1c2}}. Although these datasets are immensely valuable, the models derived from them do not transition well to the unique context of overhead observation. Analyzing overhead data typically entails detection or segmentation of small, high-density, visually heterogeneous objects (e.g. cars and buildings) across broad scales, varying geographies, and often with limited resolution - challenges rarely presented by natural scene data.
| i | 8e1ec3ac7851a2a6a370f326890a75a6 |
where {{formula:bb4c6ca1-effd-4494-b6c7-ecae72af294b}} is the space dimension and {{formula:0979f957-5089-4bae-aa7c-29870aabbfad}} is the effective temperature. The matrix {{formula:48bb29c5-a149-4032-9151-faa213adb315}} is the adjacency matrix that defines which pairs interact;
its entries can assume only the values 0 and 1, according to a rule of interaction that can be metric (i.e. {{formula:8f795671-0c3e-4feb-bc6c-ececd851397c}} if and only if {{formula:6c3c5d20-ae98-428f-adc0-b9af38869b42}} ) or topological (i.e. {{formula:4a363fd7-d63d-4526-9bb5-16abd24810ea}} if {{formula:6a50873b-35e7-43a1-bb09-448d8758399c}} is one of {{formula:5233aeb0-b9d2-4fc7-bd69-5c34a5970f93}} 's first {{formula:ab48d100-80fd-4eb4-8c90-c350d429a5de}} neighbours) {{cite:7ceef103f4d4af0d98e9099c2f07c0106d2b27e5}}, {{cite:331041b5c907623dec0b30b2bf5916c59e30636f}}. When working at fixed average density and in the very low temperature region where density fluctuations are small, there is not great difference between metric and topological interaction. Even though natural flocks are known to have topological interactions {{cite:7ceef103f4d4af0d98e9099c2f07c0106d2b27e5}}, {{cite:331041b5c907623dec0b30b2bf5916c59e30636f}}, we therefore decide to perform simulations with the metric rule, which are much less expensive computationally. In this way, we are able to study systems in {{formula:b77c7dec-aa22-4bf6-8153-3129d05fc922}} with {{formula:ee258dce-3267-4901-8511-9aa8740500d2}} up to {{formula:4962465b-e1cd-4df0-8992-d10fd066f852}} particles. We consider a metric connectivity matrix with interaction radius {{formula:377fd1d3-d318-4457-82d9-51a341776e7e}} , such that the number of nearest neighbours at the time {{formula:efedefd4-2729-4e89-ac3d-a9147d358161}} is {{formula:fec1c24a-50cb-4c9e-8522-86b188b0c50b}} , close to the biological value {{cite:7ceef103f4d4af0d98e9099c2f07c0106d2b27e5}}, {{cite:331041b5c907623dec0b30b2bf5916c59e30636f}}. We then check a posteriori that the system remains spatially homogeneous in time by computing the distribution of the number of nearest neighbours for every simulation, and verifying that it is always sharply peaked around the initial value {{formula:392350e4-99ff-44ef-b56e-5e6972b71094}} . All the simulations are made in a cubic box (of linear size {{formula:9b6d474e-2878-4ef1-bc1b-936117d8badb}} ) with periodic boundary conditions. Individuals are initialized in a global polarized configuration on a cubic lattice with lattice spacing (i.e. nearest neighbour distance) {{formula:0ca073a0-82bd-455c-95b7-7a3599e3e5a9}} and then evolve off-lattice according to rules (REF ). The effective temperature clearly drives the system from a disordered to an ordered state through a phase transition at fixed density. However, since we are considering self-propelled particles, the same configurations can be reached using another control parameter defined as the ratio between the mean first neighbour distance {{formula:d846e563-46b0-4f7e-94b6-058fd3d0e999}} , which directly depends on the density of the system, and the interaction radius {{formula:b27c0676-6543-40a0-aefa-44c7ef205e5d}} but at fixed noise. We decide to perform all the simulations at constant density {{formula:ac4ad079-7c63-4ebb-80c8-7fe0925cfbef}} , maintaining {{formula:f8697139-48fc-490b-bd82-69cf8648467a}} constant and choosing the temperature according to the desired polarization.
| m | e79a9388fdd57677e594d8e57871b2e9 |
We compare the generalization ability of our detector on a new domain. KITTI {{cite:188927b0636a9ee07e17b78e0b3cb7460e15ce93}} contains 7,481 real-world images, but with a different data collecting method than Cityscapes: the performances in Cityscapes do not perfectly match those in KITTI. In table REF we see that our OMNIA has 69.9 of AP for car, which is close to the Oracle AP of 72.1. The performance gap for car in the last experience (45.3 vs 57.3) was mainly due to a domain specialization. Learning to detect car from 2 domains, SIM10k and Cityscapes, induces a better domain invariance. We also notice that our SoftSig detector has a better cars AP than the Hard Distillation and a better truck AP than when we do not sample unsafe regions.
{{figure:0887e854-591f-4273-a574-37d52ba48d2f}} | r | 6181497bf2679265e9053105825d118a |
To understand the relation, we need to carefully analyze the dependence of the
series expansion on the choice of expansion variables.
Different choices of expansion variables give different series expansions.
Indeed, the expansion found in {{cite:da42cba28b3a6648594f3f4ed38c4f52b86c3d55}}
is the simple expansion over a single non-negative integer {{formula:765905ee-0ddb-4354-b614-0dff69d484d5}} :
{{formula:cc148d70-1316-4137-aca8-c743086f1e65}}
| i | 656d2b53bcb41671295013fe627beb6d |
As we aim to present an Arabic benchmark dataset for commonsense explanation, the dataset is provided with a baseline evaluation to address the research task and problem discussed in section . The baseline evaluation is based on several state-of-art transfer based language models i.e. BERT {{cite:f7a0aef14d907a81fe9808c03c054f9c327e0023}}, USE {{cite:ddc2c6f720996eb8a3a302dd58dbd57e3b0be881}}, and ULMFit {{cite:8db14084365092fc9768be5dcc90993a0813ce02}}. For each false sentence in the test file, the baseline selects the explanation sentence among the three choices provided for this purpose. In order to evaluate baseline models, the accuracy of the approach is measured. We also provided an evaluation code which can be downloaded with the dataset. Future researchers can use the baseline results to evaluate the performance of their proposed research.Table REF presents the evaluation results for the baseline models.
{{table:2d0777d1-591d-472f-9a21-917e5383a65c}} | r | ca918ecfcc4936a833b179a447710bcc |
First, in the BotTask condition, two participants complained about feeling “spied on”. These participants felt uncomfortable with the searchbot “listening in” on their conversation. Systems that proactively intervene with task-level advice will need to monitor the conversation to some extent. To address such privacy concerns, we see two possible paths forward. Perhaps the easiest solution is to allow users to disable task-level suggestions on certain collaborations. Amershi et al. {{cite:821855bfed22397551eccdaeb326866b561eb9eb}} proposed that mixed-initiative systems should always allow users to change privacy permissions and allow “private mode”. A second solution is to limit task-level suggestions to those less likely to be perceived as “creepy”. Recent research has investigated factors that contribute to the “creepiness factor” of personalized recommendations {{cite:6f11747e7138a8f10da7ea8f3a0ec79e47843c47}}. Results suggest that personalized recommendations are more “creepy” in certain domains and in the presence of causal ambiguity. In this respect, task-level suggestions may be perceived as less “creepy” when the conversation is not on a sensitive subject and when the system can describe the evidence and rationale for making a suggestion.
| d | 6b933a5959efc8265d6b456a99de6084 |
By Theorem REF , solving problem SLQ{{formula:5cb9f198-1c9d-40e1-9463-4ac155de1d3a}} is
equivalent to solving the system of the coupled forward-backward difference equations
(REF ) and (REF ). We may exploit the variational
character of problem SLQ{{formula:b6a7d960-d0c6-4c92-b0fe-b229f4435cd9}} to construct a gradient descent method
where approximate iterates of the optimal control {{formula:7bce05d1-1d74-482d-80ae-bd7ea482f9e8}} in the
Hilbert space {{formula:c82c2c8e-7ad9-44f3-8938-8936891c7cb9}} are obtained; see also {{cite:7b3604791352a83caf78db41e5c295cde364706f}}, {{cite:f383dcfe48df84c465e757e6b30d406f3ba94a41}} for more details.
| m | 1c873379d384606cb34338fcec59291c |
In this paper, to calculate Schwinger pair production at finite temperature, we generalize the evolution operator method of TFD to generic thermal QED systems that contain not only thermal charged particles but also photons. Then we get thermal transformation relation of outgoing operators and incoming operators under external fields. What we have done on the basis of Eq. (REF ) is similar to Sauter's calculation on Dirac equation {{cite:37d3ae5b7122079b1a4316d6a25c1dd74795d34a}}, except for the part where we add thermal distribution. Then we use an effective substitute {{formula:e43a24c9-f7b1-4d91-a126-dc2cdaaebb03}} to represent the tree level correction of thermal photons, but that has an inverse substitution when the turning off of thermal fields is considered. Lastly, applying thermal average approach {{formula:887aa4ce-95dd-4a14-8ba6-da07752ee4a4}} , we get QED pair production in thermal systems. In our results, {{formula:f00936e7-fb93-44f2-a549-3beafdfe1989}} corresponds to decaying part of incoming states under external field, and density {{formula:7fdbc57a-8de1-4033-8fc4-3e76fa473942}} arises for initial thermal charged particles and {{formula:9d5b3d07-b0ec-47f8-a13b-ffacfcf9949c}} represents effective mass of both thermal particles and tunneling particles dressed by thermal photons. In an external constant electric field, the precise integral results and the approximate polynomial results are obtained and they both recover Schwinger's result at {{formula:59a22b11-479e-4d2e-ac66-83405c1d8f25}} .
| d | d057c02a1e18b7709528ac7199dc220c |
Case 3: {{formula:4f787a44-1c1f-427e-8895-1d440bc00ed3}} and {{formula:adb64b45-8471-47c4-a87e-b8b63187b6e3}} . In this case, using the results of Dontchev and Rockafellar {{cite:1b9c6de3d11ee58d6a2dc365333ae30377cf2a41}} one can verify the following lemma; see Sect. for details.
| r | 558b40588f919d7e05af04515747dc38 |
One can also wonder if Hamiltonian truncation can be used to compute observables other than the spectrum. One motivation is the following. It is well known that one can extract scattering amplitudes from the finite size behaviour of the energy levels on a compact space {{cite:024861dd2c72a77834a61be2694b841f70f909b4}}. However, in its standard form, this method only gives access to the elastic region of the 2 to 2 scattering amplitude. One advantage of AdS is that one can extract scattering amplitudes at arbitrarily high energy from the flat space limit of the boundary correlation functions {{cite:58d2391183ab8229e74e0031a16781c3fe43e039}}, {{cite:45a272e33e7db6f444639d223f6008850896faec}}, {{cite:90fa0160fcc2d6096f58844e09c35df87c4571c3}}, {{cite:138e39db1032ee7a62256a7b215896401192a37b}}, {{cite:ee8445019070c376a6258fe12ad06627b67bf03b}}, {{cite:4ca02885fc537c2455d775ef569a17adc5d0e7a9}}. It would be interesting to adapt Hamiltonian truncation to compute boundary correlation functions and test this idea in practice.
| d | edc676f47f81b5595677f5fac918fe9f |
instead of the more common rectified linear activation function. {{formula:dd852c20-9b52-4600-9a87-f8742017d760}} is the sigmoid
function, {{formula:d73f3080-c7c0-4f09-8b19-782894016f66}} is the number of the layer, {{formula:5004f184-fc44-4d20-9ba4-b4dcf0002741}} is the element-wise product and {{formula:2ee8964b-a24d-4e7f-a28b-500db91e1278}}
represents the convolution operator. These multiplicative units help the network to
model more complex functions {{cite:5fba3cae89842dddbfc587a1825afc68dc4ac47c}}.
| m | 1d058d3c6902c3bd6a08481f2ad0dd78 |
(3) Retinal-OCT. In the medical anomaly detection task, the proposed method achieves significant performance advantages. We compare with DSVDD {{cite:f53a063a4f834396c3b2cb0ffaf9910f0fe588e3}}, AnoGan {{cite:d20b5ca666821741012fd4b647915f59a69dabf5}},
VAE-GAN {{cite:6ad18722ef468c43bb29d9680fd5856da36cea52}}, Cycle-GAN{{cite:7d61e0ecfb581a57d55d54bdc888181cddcb99c7}}, GANomaly {{cite:2da786b85472c0445ccce74bfb1abdf1dce4bd1e}}, P-Net {{cite:89c0e4008dc12bc9903a49ccc2aa416491773bd0}}, and MKDAD {{cite:4b883e102fda28a4b6612131191ab57aa44441a9}} on Retinal-OCT dataset. The results are shown in Table REF . It can be found that the proposed method achieves the state-of-the-art performance with AUROC of 98.0%. It not only outperforms recent methods using GAN networks {{cite:6ad18722ef468c43bb29d9680fd5856da36cea52}}, {{cite:7d61e0ecfb581a57d55d54bdc888181cddcb99c7}}, but even outperforms the MKDAD method. Our method does not rely on additional training data inductive bias specific to the pre-trained model. It is more suitable for medical image anomaly detection.
| r | b4ee6e374166e0817ce3a0e3262786b3 |
Our effective field theory provides a general framework in which the holographic dark energy model can be tested. For instance, the non-vanishing mass of the gravitational waves, which is small at late time epoch but sizeable during the early universe, leads to a modified stochastic gravitational waves background. On the other hand, the existence of the scalar graviton whose Compton wavelength is about the size of the Hubble radius (up to an order {{formula:77879ce0-7b43-41f3-91ae-b0f025792afe}} coefficient) during inflation may be tested by the cosmological collider physics {{cite:e917de044c64bc857f5402903de1b195feb581ec}}. It is also very intriguing to ask what would happen if the Stueckelberg field, say the time like one {{formula:7654fa14-3b63-440a-8231-abed35e0cdbb}} , couples to the standard model fields. Our EFT framework also provides a general setup where the perturbation theory of the holographic dark energy can be developed, which allows us to investigate its impacts on the cosmological structure formation in detail. All these possibilities warrant further scrutiny.
| d | d040135fdda2ac6eb46a10f4a36c4434 |
The recent kernel thinning (KT) algorithm of {{cite:51907fc26ad47441eb42e23a98e7021f7b14e9c5}} addresses this issue by producing thinned coresets with better-than-i.i.d. integration error in a reproducing kernel Hilbert space {{cite:f2f6fd0e2d5912010a337dc3841203c3703a743c}}.
Given a target kernelA kernel {{formula:69c40480-e137-46c0-941c-9154d1216aa3}} is any function that yields positive semi-definite matrices {{formula:0cf5ac6b-6cca-49a5-9e4c-84564a5915ba}} for all inputs {{formula:75bf3ef8-7eb3-4b3d-93bb-d6f89070e9ba}} . {{formula:3c5137d6-98d4-4acc-baf2-e78f422b5eae}} and a suitable sequence of input points {{formula:f8696bb3-f1ba-4b2f-a341-dbf43abf6296}} approximating {{formula:bc65877d-9fcf-4d70-926e-58e1b7259ef7}} , KT returns a subsequence {{formula:b93ade48-98b3-4adc-8b40-3c508051f3b5}} of {{formula:028c0cad-51dc-4c8a-8dc0-5e5fd0b238b2}} points with better-than-i.i.d. maximum mean discrepancy {{cite:243694fec29ef87a245c32fa6ba968b69f1da74f}},MMD is a metric for characteristic {{formula:5cc94bdc-92bc-4d0c-beef-9b9d7aece073}} , like those in tab:kernels, and controls integration error for all bounded continuous {{formula:188d24b2-7863-49e9-b771-67243237621c}} when {{formula:298ca0f6-5db6-4799-b67d-51af6d94dec0}} determines convergence, like each {{formula:4a4177c2-1a86-42ec-b0a3-0a868162165b}} in tab:kernels except sinc {{cite:b071905425fdc16f95c2f28916fff0d401eb7f53}}.
{{formula:39ba7094-0a8c-4c1c-895c-b9ec83784c8f}}
| i | 3f559192dceeff8dcad1c9a809c7ac8b |
The result on the arXiv test set is shown in table REF . The top block presents previous state-of-the-art methods with shorter input sequences. Sent-PTR {{cite:7f820e3f8ee88c88f48071fc962e7c33092a1d4c}} is an extractive model that uses hierarchical LSTMs and a sentence pointer to select key sentences as the summary. Extr-Abst-TLM {{cite:7f820e3f8ee88c88f48071fc962e7c33092a1d4c}} is a two-phase model that generates summaries based on sentences selected by an extractive model. PEGASUS {{cite:25b72f59bd9d43f7470091c831b0af6df6d7faa2}} is a large pretrained model specifically for abstractive summarization with an input length up to 1,024. Dancer {{cite:b045055269d41c3c8a14e16bc8322c27550867e3}} breaks a long document into multiple sections to produce partial summaries for different sections and then produces a final complete summary based on the partial summaries.
| r | b7cded05e03c7f0eaae09af4cbf4baad |
Two recent works have considered the horizontal length scale of convection in the geostrophic regime. Guervilly et al. {{cite:a89dfb6b4f248e27a66298ac5107bb65d3b62a31}} combine results of various numerical models to find an effective scaling {{formula:5d3026c9-0163-4d92-b222-955d41e29db0}} with the Rossby number {{formula:b57ec8c3-e809-45f2-9bdf-8317c3d38ba8}} based on a measured velocity scale {{formula:0abfffeb-b1e8-49ad-8f74-dcd8f84ba4ed}} . In our notation this amounts to {{formula:6bca60bf-8ea6-4e2f-b74e-c8a0bc37d8ce}} . They only find this scaling at very small {{formula:53e8e708-20eb-4be3-9cf2-482d260c0682}} . Aurnou et al. {{cite:8b0529fd94276b65716a4f57bfac022b73a7b94c}} provide theoretical scaling arguments based on the so-called CIA (Coriolis–Inertial–Archimedean) force balance that also predict {{formula:421ed19c-2add-4d59-b62d-cb5ab8e0e294}} . In our experiments at constant {{formula:ee1c4f9b-6dc3-46c8-b151-e7702ad852c6}} with variation of {{formula:a060605c-1a2c-494a-86ee-dcfe67cce533}} this translates to {{formula:5b615683-7fbc-4a01-ac59-fa17ccdac5d4}} . This scaling slope is included in figure REF with the solid black line; a trend clearly steeper than our data. A power law fit to our data for {{formula:21f2f051-8d38-486c-b50a-4a05d0ef4213}} (dashed black line) renders a scaling {{formula:cab041d9-71f3-4ff1-be0b-17065c415ece}} . Looking at figure 4(b) of Guervilly et al. {{cite:a89dfb6b4f248e27a66298ac5107bb65d3b62a31}}, our shallower scaling corresponds nicely to the shallower trend of their data for {{formula:1399eb3b-83c8-4b1b-87f5-c798f330c77a}} , which indeed encloses our {{formula:8847fb91-f93f-4d7c-b7d2-9c8a17a152c3}} value. While the scaling of the length scale is similar, comparison of the magnitude is not possible due to differences in domain (sphere vs. cylinder) and {{formula:9fcafbbb-5024-4d7c-b5a3-7cba62b33cec}} value (0.01 vs. 5.2).
{{figure:df376d19-9228-4aaa-b796-615dc539a1d7}} | r | 7924bc3fe1db860ae09a103f4f6b34cf |
Knowledge distillation (KD), which has become an increasingly important topic in the deep learning community, offers a potential solution to these challenges.
In KD, the goal is to improve a student model's performance by supplementing it with additional feedback from a teacher model.
Often the teacher has larger capacity than the student, or has access to additional data that the student does not.
As such, KD can transfer this additional and valuable knowledge from the teacher to the student.
In early KD methods {{cite:83d1c4100630f3cc4274660cb4f4cfbbe959fa33}}, this supplemental supervision was imposed by asking the student to minimize the Kullback-Leibler (KL) divergence between its output prediction distribution and the teacher's. Given that the prediction probability distribution contains richer and more informative signals than the one-hot labels, student models have been shown to benefit from this extra supervision.
However, the low dimensionality of prediction distribution means that the amount of information encoded (therefore transferred) can be limited. For cross-modal transfer, these predictions may even be irrelevant, making KL divergence unable to transfer meaningful information.
| i | d2171918fefefb9dc1687aa8248c4fc8 |
In this section, we first show a few examples of Feature Vectors implementation on widely used tabular datasets to show the usefulness of information provided by our method. The second set of experiments aims to show that the fature-importance measure provided by this method are objectively valid. We finally use the notion of Knockoff features {{cite:f999d1d290c9d1e2e86cebb4675fc7f1ac24ec07}} to examine the information provided by the angle. For all experiments, we use cross-validation for selecting the depth of trees. We empirically observe by setting {{formula:3f002725-62d3-46c5-8f36-ab7ffeca752c}} (number of rules), the computed feature embeddings become stable across multiple runs. We also set the window size to {{formula:63972b84-882c-4106-bf0f-6aee54b06bb1}} .
| r | 22609c4c72cafc8543d72dd96e0144c3 |
In Figure REF , we show the excess surface density
profiles derived from the g-g lensing signal for sample cAGN
and corresponding control samples cSF{{formula:deb9d413-dce2-47ba-a0fc-91ed4601adae}} and cQ{{formula:36449731-ea58-4f4d-ac1e-ed54dcb1d93a}} . As one can see, the ESD
profiles obtained from cAGN and cSF{{formula:8a0fa65a-8ae4-40bd-82e5-f619df325188}} are quite similar, while
that for cQ{{formula:ffbb10e2-79d9-4904-aabb-db30c740b41e}} is higher. To quantify the results,
We use model M1 (see Section REF and also {{cite:f56b1ee4872b34012ce5674ae81434d0777ee114}})
to fit the observed ESD profiles and to derive an average halo mass for each of the three
samples. The results of the halo mass are listed in Table REF .
The halo mass for central AGNs is about {{formula:751a1946-ba47-4800-8237-986dbc6bc6f1}} ,
in agreement with the g-g lensing results for AGNs selected from the SDSS DR4
{{cite:a587b24bc1d8e14a64a1d5f4df5ab8027e173b3d}}. The halo mass for the control sample of
star-forming galaxies, cSF{{formula:b40cff74-1cce-4ad8-bd3e-474cf3bc6ce9}} , is very similar to that of AGNs, while the mean
halo mass for the quiescent galaxies, about {{formula:ba40e6e4-3e31-467b-ac30-acd8da395214}} , is about
three times as high as those for AGNs and star-forming galaxies
of the same stellar mass. We have also carried out the same analysis
for AGN hosts and normal galaxies in four stellar mass bins, and corresponding results
are listed in Table REF and plotted in Figure.REF .
As expected, for each population, the average halo mass is larger for galaxies
with larger stellar masses. For a given stellar mass, the average halo
masses for AGN hosts and star-forming galaxies are similar but lower than
that of quiescent galaxies.
| r | e5e3589075528f17117184a2560a2a75 |
GWF for interferometric inversion is inspired by the non-convex phase retrieval algorithm in {{cite:2129016b26228795838d315c270cb77ca0fba9f1}}, {{cite:1745687a4bf49221ea264f475df5df390d1cbc38}}. GWF uses a two-step algorithmic approach to solve quadratic equations involving first a spectral initialization {{cite:1e083f621cad2cf3c131db639b4074dc96e7ed88}}, then a simple first-order iterative refinement as follows:
{{formula:d74fd807-af8b-4d1e-b507-8ae9fa36ddfe}}
| m | 11577e878c3bb231a667ce733c1e14fb |
Rain is condensed aqueous vapor in the form of falling drops with high speeds and small sizes. As a common weather phenomenon, rain can bring massive impacts not only on our human society, but also significant influences on today’s intelligent era. Such impacts are most prominent in deep neural network (DNN) based perception systems, e.g., autonomous driving, video surveillance and unmanned aerial vehicle (UAV), which can be easily disturbed by the inevitable rain effects, suffering from severe safety and security issues {{cite:a2d59f4faed21dec8d848776c9ee4fad76ddd6d3}}, {{cite:ffae8cf7e3045a26adf720ebb6bc03260fc3349f}}. Therefore, it is of great importance and pressing to comprehensively study how the rain affects the DNNs.
{{figure:86f109e7-5a15-42f5-b6ce-c4c29c879cbe}} | i | ed7121c26e372cfabedc98c6e85d3e6f |
Next, an end-to-end method called LO-Net {{cite:ab030c38bd04b7f6a59e5398afb5f9b585d002ae}} was introduced that takes in LiDAR point cloud data and computes inter-scan 6-DoF relative pose. With it being an end-to-end trainable method, LO-Net learns an effective feature representation. This is facilitated by a new mask-weighted geometric constraint loss. This loss helps the algorithm encash the data's sequential dependencies and dynamics. Here, the position and the orientation are estimated simultaneously. L3-Net {{cite:93d88b45af4bfbfb0151eb4552f662cb820d75b8}} has a blend of multiple approaches with mini-PointNet being used for feature extraction and 3DCNNs being used for regularization. Most approaches are supervised learning approaches, but for the first time, the paper DeepLO {{cite:2323ad6bad5b3c5f28a43313d790ca7d848301a7}} introduces supervised and unsupervised frameworks for geometry-aware LiDAR odometry. DeepLO also incorporates vertex and normal maps as network inputs without precision loss.
| m | a2841608c6ef3316cb31d7a383175974 |
To determine joint BAO and {{formula:0bf56d4d-81a4-4d9c-ab45-f30942763a90}} constraints, we multiply the likelihoods to get a joint data likelihood. The same procedure is used to determine the joint QSO, BAO, and {{formula:e94c892e-c564-4bc1-86be-675096bc55c7}} data constraints. In our previous analyses {{cite:0edeb3e0a02ea6491cdbe34f21843bb3e75669a1}}, {{cite:8f09ccd14e5848bfeb8ab1e909a301985ba182d1}}, {{cite:bb28c1ba021395123e51118b7d9f94bc5444106a}}, {{cite:8e047a513cb4dd8022fa8042f319866d93165c35}}, in the case of the BAO data analyses, we assumed values of {{formula:e74d3406-03f7-456c-a35e-6d8c5c33ee39}} for the six different cosmological models from {{cite:c41902be5c5ba8032df6d14c5f7b60671310d5d4}}, {{cite:4f86a0e3a517d14644d927749f6fb0816c8fa0d1}}, {{cite:73505868907817c6e56c4ecca9ea2f9c58c3d2b3}} that were determined using CMB anisotropy data. In this paper we do not use these values of {{formula:fb7e94bc-76e9-4a81-9274-358b35f3ed6b}} from the CMB determination, rather we let {{formula:a2d5c5b3-ba2d-4f99-bb98-eee5ea13d433}} be a free parameter to be determined from the data we use in this paper.
| m | 0f68a8c73cd5446cb1df69a7097cfb48 |
Although we can use classic machine learning approaches, they don't produce any representations. As we see a lot of new types of geological data {{cite:2d116b28b13040ed64ecd913c445a6420d064ee6}}, it is more convenient to obtain similarity during representation learning and apply neural-based models for it.
| m | cc6faba523996b7622f7aaa1779ac929 |
(5)
It is seen that branching ratios for
the {{formula:5756bafa-810d-4973-8e27-a7cf08492c07}} {{formula:80731498-9e73-4d6b-894e-e29bc97d3937}} {{formula:ef68cfcb-31d1-4f3d-807d-a0068e78c0d0}} ({{formula:a6a40a5d-30d9-4453-95b2-a0c8b209dcc3}} )
decay can reach up to {{formula:9667b3ea-3509-4039-864d-0c3c6d83d20b}} ({{formula:5e620de9-3459-4a5c-a8bd-c5057cd09432}} ), which
might be accessible at the running LHC and forthcoming
SuperKEKB.
For example, the {{formula:8f3af1a4-887d-4cee-906b-a3d7d31bf79c}} production cross
section in p-Pb collision is a few {{formula:1fcc0dc1-204b-4c78-b1d5-b469f3614417}}
with the LHCb {{cite:705501ddaae5966c216df6b306a077aca94f7937}} and ALICE {{cite:3a68b572c9a7a02d6bc67f29629780386b5d2fce}}
detectors at LHC.
Over {{formula:89b76a7f-c5bd-4f26-a0a8-8cf02c87f83e}} {{formula:f52d0002-5109-46f5-bbd9-bc0ddfa5a705}} mesons per {{formula:cae8d856-86c7-48f4-ae4a-876db2067ad5}}
data collected at LHCb and ALICE are in principle available,
corresponding to a few hundreds (tens) of the {{formula:a7360778-8516-4779-8541-587711ca4dd9}}
{{formula:41e25162-cfdc-4fc9-ad86-b6588192a2bc}} {{formula:2d32882d-a191-4d7e-b7f4-e5c55ca3f5ae}} ({{formula:4c2f2a46-3dc0-4b57-a624-5f8f9d8e5dcd}} ) events.
| r | 1c53874aa854fa557b68c4f7516d5ebb |
Visual content recognition and understanding have greatly made progress based on the advances of deep learning methods that construct the discriminative model by training large-scale labeled data. In fact, two reasons limit the current deep learning methods for efficiently learning new categories. One is that human annotation cost is high for large-scale data (for example, thousands of the diversity samples in the same category and hundreds of the various categories in one cognition domain), the other is that the rare samples of some categories are not enough for the discriminative model training. Therefore, it is still a challenge question that the discriminative model is learned from the rare samples of the categories. To solve this question, few-shot learning {{cite:8d0cff654494962b5b8ed90366c32780c28c46cb}} {{cite:6a6ef65043a183ac8cf388f17563f7fcad14b9b8}} {{cite:6aeb75bc26dab0cfa7d1a5b39230f850bb915a2c}} {{cite:4a1be3a0c809157f49fc6c87b99c87657677e1a2}} {{cite:20b3ac7626f3ba55dc3eb3a917f64a1ca3720ce6}} {{cite:244d3775b6334f11a6bc2d6ad21a3dabe64482b3}} {{cite:e46d676853f9a56710c6ddeaaa41bf4f88344731}} {{cite:6fcd11700fb8fa02a5f0ca4dd1ebb76e447813c9}} {{cite:be092810ffbc4f476936867877d06aa0de16e943}} {{cite:0c02d86373d57158204f780e4ca7f5680dad8d6f}} {{cite:d543d9cbfe3a879374e39e50432524fa925982f2}} {{cite:2accb5268214147a37651ef0a61662e062a28ca5}}proposed from the inspiration of human visual system has been an attracted research to generalize the learning model to new classes with the rare samples of each novel category by feature learning {{cite:6716734f3ae5058883362ed3592432e2f5eee00d}} {{cite:b68a056258d276f8d86ac71f5d368317d35eed38}} {{cite:67c4a77ed51b0c59cc0536c7a286e68daf0d482c}} {{cite:5662db5d6a3abfe9bc3b0a9d0bdf3fbce4b9763d}} {{cite:116ed3233ccbc73adf2920fa7d279cdd678da555}} {{cite:10e18507d18ffbe6339bef58de8035f2f1cf9c3e}}or meta-learning {{cite:2accb5268214147a37651ef0a61662e062a28ca5}} {{cite:de0830de8ebbffce5ea95057d478116c1c2906e0}} {{cite:0c8ab30b01b8e20d64ee27c3dfc0116d61a0193b}}{{cite:9bf4f6e2c740ad3881c1ca175ceb688b4e248cd7}} {{cite:3f1f98aed2297ab0093d158b47bb870d052249d8}} {{cite:418adc31ef844ca709e9069daf9aa2a28bfe282a}}. Feature learning emphasises on feature generation and extraction model construction based on invariance transfer information, while meta-learning focuses on the relevance model between the samples for mining the common relationship of data samples by the episode training.
| i | 1aff48d8b576793bd09e2ce765071b19 |
The human perception system is inherently multi-modal. Inspired from this and to leverage the learning of new concepts we propose a multi-modal interaction module that embeds semantic conditioning in the visual processing scheme as shown in Fig. REF .
The overall model consists of:
(1) Encoder. (2) Multi-modal Interaction module. (3) Segmentation Decoder. The multi-modal interaction module is described in detail in this section while the encoder and decoder modules are explained in Section REF . We follow a 1-way {{formula:edf21711-6ae6-42d7-b800-3396544b79a5}} -shot setting similar to {{cite:cd99ee1f5ea41e07b3c014f8f9c68e27b43a61c8}}.
{{figure:02fec9cc-c5dc-4d5c-832a-48fc8ea1f817}} | m | 03dc286529b1a2e70a7f882540d640b7 |
By accurately and efficiently solving RPM tests, we have demonstrated that NVSA enhances the aspects of both perception and reasoning by adding a distinctive vectorized flavor to them which is based on the high-dimensional distributed representations and operators of VSA.
In the proposed neuro-vector perception, instead of naive local or distributed representations for the objects, we exploited high-dimensional vectorized representations.
A multi-attribute meaning was structurally assigned to every object vector by binding its attribute vectors, which can be further bundled to create a composite vector representing multiple objects—all in a fixed dimension that is significantly lower than the combinatorial attributes.
These structured representations were used as the target vectors to train the deep neural network by using the additive cross-entropy loss.
Being able to train this deep transformation permitted the simultaneous inference of multiple attributes of multiple objects in a visual scene with neither exploding the representation dimensionality, nor facing the superposition catastrophe.
In the second part of NVSA, we proposed a vector-symbolic reasoning where the probability mass functions of discrete or continuous attributes are expressed as the vectorized representations.
This permited the use of VSA operators to manipulate the vectorized representations for efficiently encoding the constrains and implementing the rules.
As a result, the time/space computational complexity of the distribute three rule search was reduced from {{formula:e0d48859-a421-49f2-9f00-3d7bceda45cc}} to {{formula:0bd87bf6-6ae5-407a-b4c8-fd36f5ee7672}} , leading to two orders of magnitude shorter execution time.
It was shown that NVSA surpasses both pure deep learning {{cite:bb95609ac5e8a6299c4341a5f3367a1374b68a52}} and neuro-symbolic {{cite:5fba3c9cf692c4955002182d8b16036d809e9c8d}} state-of-the-arts by achieving average accuracy of 97.7% in the RAVEN {{cite:720f7cc4661d7b445d6d5c465245f07f42765150}} and 98.8% in the I-RAVEN {{cite:2d2e161bbc20a73cb30d12c77c9e9f140dbfe1ca}} datasets.
NVSA also enabled real-time execution on CPUs, which is 277{{formula:d3d3ce9a-c9dd-4ed4-9c3c-02082f6aa8c1}} faster than the functionally-equivalent symbolic logical reasoning.
| d | c05b51eeadea8042acb2c7e3b08566bb |
Parameters of the above mentioned models are fitted to different sets of the then
available cosmological data. In most of the {{formula:be17e432-30d3-43a6-b8bf-61f7f69e975a}} minimization but for
Ref. {{cite:82b1f19516e63bd90ad134a10c43ba34012ee019}}, contribution of radiation has not been taken into
account. The inclusion of radiation may play significant role while fitting data
of early universe, such as BBN.
In the present analysis, we include the contribution of radiation
with {{formula:887f87ef-7d4f-4613-9088-592d66ba16e4}} . Although, the Hubble
constant has been fitted with a few parametrizations, the results of
the present work are almost independent of the magnitude of {{formula:dfc9e185-c2de-468b-81f6-a43edec2969c}} and hence, {{formula:3601f5e5-a44e-4081-a6e2-5aca439e7df9}} {{cite:82b1f19516e63bd90ad134a10c43ba34012ee019}} is used for convenience.
| r | 782841f577ebe6fdd7997d59c392a417 |
{{formula:cdace7d3-7cef-40a7-a872-e3b6c671809a}} leads to statistically significant improve over {{formula:c821c2c7-9b01-4c1b-b17a-6c3eec9ae8ba}} if the lower CI for {{formula:7de77ad9-aaab-4b66-83e1-7a2c1084b803}} is larger than 0.5. Per the Neyman-Pearson statistical testing criterion in {{cite:5810ad20d6842450b6a44b8f44a2cc36f02c8076}}, {{formula:6251ed6b-d623-4f59-9093-2055186cd8be}} leads to statistically meaningful improvement over {{formula:03cb4b79-eca2-4d55-b2c8-781c351008b1}} if the upper confidence interval (CI) of {{formula:52a6ff26-ce92-4811-9aaf-716f87d0eb87}} is larger than 0.75.
| r | ed81941f406f792f83c62f30a4b2e33e |
We propose a Ranked Policy Memory (RPM) method to provide diversified multi-agent behaviors for self-play to improve generalization of MARL. Then, we incorporate RPM into MAPPO {{cite:dd96ebcf637dc169f6f4492b3552593f3415ceda}} for training MARL policies.
{{figure:ddf2abdc-734b-4b8e-9775-fd7954365c62}} | m | 52a8397ae54954c44d236a972474ea38 |
In conclusion, we have shown that a driven Rydberg chain with
staggered detuning term leads to several interesting phenomena.
These include ergodicity violation via Floquet eigenstate clustering
in the strong staggered detuning limit, dynamical freezing,
sustained coherent oscillations with perfect revivals near the
freezing frequency, existence of separate class of quantum scars
with large overlap with {{formula:b8631d27-83ce-4eef-bf23-b31b1d912f18}} , {{formula:ced22da2-1a32-4962-9161-9dc79e0e5b5b}} and {{formula:88a84d6b-add4-4191-a523-70f9f3047079}} states, and the possibility of tuning ergodicity
property of these chains with the drive frequency. The experimental
implementation of a Rydberg chain has already been achieved
{{cite:8ab1e17c9b46ede17bada1063ee628960ab94aa6}}, {{cite:bb39de90f56685177f07642b8026ead94353a953}}; a possible extension of some of these experiments
with implementation of staggered detuning may provide a suitable
experimental platform testing our theoretical predictions.
| d | 9bf11d995625391e4e2aea3a874888c1 |
Fig. REF shows the architecture of our proposed children’s speech recognition system. It uses a pre-trained wav2vec 2.0 model {{cite:57e95e4d158330a09f3f1f0f775dbca0cce89cbd}} to extract contextualized speech representations. This wav2vec 2.0 model is fine-tuned for children’s speech recognition by adding a randomly initialized linear projection to predict characters, where the classes in the projection represents the vocabulary of the task. The model is optimized using the CTC algorithm {{cite:250a4f1d77ef002df144f840c869d43d921b1fca}}.
{{figure:53e29f20-52c0-4501-83af-6eee4a9bd63b}} | m | fd4790787e6616dd09bc2b5679b73e1b |
It is standard that Assumptions (H0) and (H1) are satisfied for the elliptic operator {{formula:313566af-ad86-49da-b145-a352144df8ac}} defined in (REF ); see e.g. {{cite:b6f9fb033403c791635e995f925817f84254adfd}}.
It can be checked that {{formula:9b0df26d-bf3d-4a7e-948a-deb4c1a85a7b}} is locally Lipschitz as mapping from {{formula:578acf53-c851-4f38-bfd2-ab41ef669f99}} to {{formula:84352b74-1870-4477-98a4-2bb3216641a6}} , in the sense of (REF ). Thus Assumption (H3) is satisfied. It is also clear that {{formula:82ee4688-b1eb-4802-a9ec-e1056ac91899}} defined above is Lipschitz on {{formula:387c97cb-6719-484f-a71f-0a2dc0082f58}} and satisfies furthermore that {{formula:48a3a461-59b2-49d3-96f6-34c015bd4557}} .
| r | bcd843c240324469940181835cd46a0c |
Our results are shown in
Figures REF . Similar to the observation in {{cite:51612414c33d7a68b29bbc20aa47d6039c5671e4}}, SVM with subsampling achieves low worst group error, lower than both SVM and GS-SVM with {{formula:e3157cc9-b80e-4038-90ad-768bf1642647}} . Specifically, note the low errors for Groups 2 (Figures REF (b) and REF (c) in ) and 3 (minority groups) with SVM with subsampling. However, this happens at a significant cost paid by the majority Groups- 1 and 3 (Figures REF (a) and REF (d) in ). This results in the misclassification error increasing by a factor close to 5 in comparison to the standard SVM without subsampling (Figure REF (a)). We expect that, with better tuning of the parameters {{formula:76e6b157-0cd4-498e-bfce-5604ac7e22c7}} , the GS-SVM on the full dataset can help achieve even lower group errors for the minority groups without hurting the majority group errors significantly. We leave this such investigations to future work.
| r | 616982493582d7052bf68e3b14b9eb26 |
The carbon monoxide (CO) molecule has been used historically to trace the overall distribution of molecular gas in our Galaxy and other galaxies {{cite:2d9563f0667647398f60589ed7f88131bb862897}}, due to its substantial relative brightness and abundance. Surveys for the lowest rotational transition of {{formula:b7d9e7ba-d12a-4b18-9545-982fd13feee5}} CO (J=1-0) at 2.6 mm have made invaluable contributions to our knowledge of the overall distribution, morphology and mass content of molecular gas {{cite:3686a566895795afdf63f06671739f80e127fcec}}.
| i | 18af337a0bd53b62a5c875d8d85b52b5 |
Thus, variational inference can be suited for larger data sets and scenarios where the model or a variety of models have to be estimated more quickly.
It, however, only provides an approximation of the posterior distribution, does not provide the same guarantees such as MCMC methods, and underestimates the variance of the posterior distribution {{cite:259c2aaef731cd8897e84850c4acb1d64b251eff}}.
| i | b1c69c43d9d3189d04c0dd02ef79d259 |
To the best of our knowledge, there are few truly unsupervised methods that can work only with the image being inspected, with no prior training or information about normal samples, as shown in Table REF . In order to compare our proposed method with the state of the art, we use MVTec AD {{cite:7f95cf44fb4474554694b2d4b68e12869a0b241a}}, a recent anomaly dataset that simulates faults or anomalies in industrial conditions. The MVTec AD dataset contains several subsets, divided in two categories: texture and objects. We focus on the texture category, as we are interested in the detection of low level anomalies. Also, one of the subsets has special importance for us,
as it considers anomalies in leather samples. MVTec AD also provides several non-faulty images, which most of the state of the art methods use for learning the normality. In order to extend the comparison, we also include the baseline methods, even if they use normal samples and the comparison is not completely fair, in their favor. Results are shown in Table REF . To perform the comparison, we use the area under the receiver operating characteristic curve (ROC AUC), as it is the most used in the literature, and enable us to compare with state of the art results.
| r | 58b90ece78439367def94d769203b9f6 |
and then taking the {{formula:5d54606f-3afb-47fb-911f-29b32c232d58}} limit of exact solutions to Einstein-Gauss-Bonnet gravity. This approach allowed for non-vanishing contributions from the GB action term in {{formula:0c4cccd7-5be4-418d-8e3a-a9a20026bc40}} . In doing so, a number of 4-dimensional metrics can be obtained (for spherical black holes {{cite:d1325b1434c1b898745471c8e026a219bfb888b6}}, {{cite:99a7c860203d24c184c00a72387fda6cde6bb647}}, {{cite:9eef8bb850a6ded9d49b9788814094a731b58707}}, {{cite:dfcf07291797cff429f04b61634c2fd327920525}}, {{cite:80799887576e425144e56bcae0ac1199326a850a}}, cosmological solutions {{cite:d1325b1434c1b898745471c8e026a219bfb888b6}}, {{cite:2116e6a7bba7921798a01c28bc6c2573e5c1ffd6}}, {{cite:ec501aa60467b7f4cf1296d3ba184d4f35ba3beb}}, star-like solutions {{cite:12044d6dd557b44df3678426e2caaf7f00a11ff5}}, {{cite:5220d75f1b612ede34b3711774bf74948e4ba844}}, radiating solutions {{cite:8e800366c62899914f9d410f1177ae1131b745e1}}, collapsing solutions {{cite:cd652a79731d5532dfeea70bef68dcec70f72c20}}, etc.) carrying imprints of higher curvature corrections inherited from their {{formula:90b88371-6025-4055-9336-210c2215918a}} counterparts.
| i | a12db3578db6fa3d1d31a17ab18c6420 |
The multiple comparison problem is more general than hypotheses testing and is raised in feature selection literature, too. A recent and powerful feature selection method called the knockoff method, developed by {{cite:7aad0bfe9114e02a3299ed9643c1e78ea7fbc2a0}} theoretically controls FDR in finite samples. The original method, referred to as fixed-X knockoff assumed normality, linearity and homoscedasticity assumptions {{cite:7aad0bfe9114e02a3299ed9643c1e78ea7fbc2a0}}, and the other version model-X knockoff relaxes these assumptions, yet it relies on the joint probability distribution of features to be known {{cite:e4023ed5477ea0b3cf4234fe64a9766177f79cb2}}. The knockoff method is motivated by the permutation method {{cite:413ff68aa73bc0b457451b7d720b941af0b45352}} where one permutes the features and compares the feature importance before and after the permutation so that large difference evidences against the null hypothesis. A drawback of the permutation method is that it destroys the correlation structure between the inputs by permuting the individual features. Barber and his colleagues aimed, in their paper {{cite:7aad0bfe9114e02a3299ed9643c1e78ea7fbc2a0}}, to rectify this by constructing a multivariate copy of the features (here referred to as knockoff features) with the condition that the correlation structure remains unchanged, but each knockoff feature is independent of the original feature. Barber et. al. have applied knockoff in high dimensional settings {{cite:40127cc511cf47294a1712f931f8245d30c9959b}} and have discovered a lower bound on the power of the knockoff procedure {{cite:280bad01a67d31a276d4cc8c8ae628ccfe481c03}} using data splitting ideas. An earlier and similar work to knockoff was introduced by {{cite:ece54257df5358a2618e27d1e52ce9179982343e}} which creates pseudo-variables instead of knockoff features.
| i | 265fcf9333a708cf7702f67315dd5aca |
To conclude, we have presented a framework that explains the emergence of biaxiality due to the magneto-nematic coupling in nematic liquid crystals with magnetic inclusions or ferronematics. This topic has generated interest because of its potential application in the multi-billion dollar LC display industry. Further excitement has resulted after the benchmarking experiments of Liu et al. {{cite:93a349b1b72c2770e51b09df61bbcae72c728acb}}, which demonstrated the emergence of the elusive biaxial order in FNs. Our framework to guide experiments in these unique systems with the twin properties of magnetism and biaxiality is therefore very timely. We have used coarse-grained Landau-de Gennes free energies and a time-dependent Ginzburg-Landau formulation to explore the free energy minima of this coupled system. The different feature is the inclusion of a coupling parameter {{formula:66cc4fa2-db96-416f-9a45-f37357db270e}} due to which the FN relaxes to a state where {{formula:8edcdc59-b448-4892-8fba-759260ea58d9}} . This choice is crucial for the emergence of biaxiality in our study, and is also consistent with the experiments of Liu et al. {{cite:93a349b1b72c2770e51b09df61bbcae72c728acb}}. Our formulation provides a quantitative evaluation of biaxiality and its dependence on the magneto-nematic coupling strength. The latter, in principle, can be manipulated in the laboratory. We hope that this quantification will enable more systematic experiments.
| d | e2fdcd1671a3c7bd9ef56dcf9b321637 |
In this section, we present our numerical results to investigate the performance gains and to validate our analysis. In our simulations, the large-scale fading is modeled as {{formula:fe18e10c-3c59-49a6-9997-c386476226ba}} , where {{formula:e2dd87d6-acfb-4284-bf22-5768dfc30927}} , {{formula:7ab0035b-673f-4796-8954-2e2c481fd0a3}} , {{formula:c1ef7a3d-a859-4d46-a3c4-e5556b9f9315}} is the distance between nodes {{formula:52edbedf-a7d5-4088-8db2-f3ba61aa2fa3}} and {{formula:2cd31763-a6e8-4aa2-b97a-d382f32117e2}} , {{formula:a1d9dbb0-bf11-44bb-a5b8-85b231fb9e91}} m is a reference distance, {{formula:12d065b9-0519-4bee-90c8-b5e59751d0c6}} is the path-loss exponent, and {{formula:0414bd6a-ab07-4f79-87a6-1ac1bd1400d3}} captures log-normal shadow fading with {{formula:a0daed8a-267d-4ffd-b623-a6c3f18fa9e2}} {{cite:e27f1a0f40e91186754cb739a3413afd96aee0e5}}. In the system topology, {{formula:8ecbf74d-662f-4e0c-8d08-c1249423962c}} and {{formula:7d3248e3-4f17-4719-931f-8d302ff3d0d3}} are in positioned at fixed locations and {{formula:1e72f36f-4f98-456d-a011-b4bd6f82a94c}} m apart from each other. The IRSs are randomly distributed over an area of {{formula:c5a7018f-5c44-4482-a55c-6aaff9b19645}} {{formula:b53ae109-a065-4f68-b8ec-3e1e9d56da62}} . Moreover, the amplitude of reflection coefficients {{formula:daf614c6-c6eb-4ab2-aa8f-8ec4cecc6611}} for {{formula:5c0bbb31-dd69-4b5b-98b7-716c07483569}} and {{formula:1f9519d1-57f7-4aa1-b45f-e943ff7ed527}} is set to {{formula:6b929ffd-ceba-4625-9b5a-18e5d5e3368a}} , and unless otherwise specified, the Nakagami-{{formula:3fc737df-312f-46d1-8015-ffada6e54a03}} parameters ({{formula:25dfe913-82d6-4937-b506-df698827d28b}} , {{formula:9fece07f-a689-4372-b819-2f7e8e0b3a9a}} , and {{formula:efad7d57-8d01-4852-98c3-1289df484a2f}} ) are set to 3.
{{figure:24068882-1689-4f42-af6f-83c2f53b86c7}} | r | 74908c3cd451a74db19208430fbea135 |
One can reverse the logic which we followed in this paper by assuming that the entropy of the strings and branes is equal to the entropy of the black hole to all orders in {{formula:c19cdb85-44e3-4839-bd93-6de822fe4cc6}} , and ask what are the conditions on the inner boundary of the manifold. Two possible answers emerge, either a punctured geometry or a geometry for which the volume of an {{formula:f71a1370-f621-4df4-ac38-8f4bb212a306}} at the origin shrinks to zero, while both asymptote to an {{formula:bb2b3650-59e0-4a2b-8795-24690efb2eac}} -corrected black hole solution. In both cases, the inner boundary is not a standard horizon and the geometry is non-singular. The absence of a horizon is consistent with the general pattern found in the Fuzzball program, that a horizon results from an insufficient inclusion of stringy effects {{cite:53ab4df255763b5a2a71cb4041222059b582638a}},{{cite:6dd7c1c3bd5426c5f052c28703c5c7698e5304fd}}. One can view this class of solutions as corresponding to the state of the black hole when the string sources are included explicitly, with a string scale resolution. When these sources are integrated out, the result is a geometry with a horizon. The entropy of the black hole in both descriptions needs to be evaluated using different methods, which nevertheless lead to the same value of the entropy.
| d | 8f1460fe21158e7d2738789312151ffc |
Average value of the order parameter calculated during the simulation {{formula:bc469132-21bd-4c23-a61d-466d9383db42}} , is used to identify structural transition induced by the change of the packing fraction of obstacles, {{formula:c8de8aba-6ef5-4ecc-a04f-56bde4047959}} , in a similar way as it was done by increasing the noise amplitude in the bulk system in the original Vicsek model{{cite:f51567d2fbab6424bbe6318bce13a7c9fc7ebfb0}} and its variations{{cite:99b19b32288f7c40d59426c919cacf744d16da9c}}, {{cite:75e103a7a2eaa04ab399175ebbb75e4cd27087bb}}, {{cite:1f7053e22447feb63c5777a018439d25d06d4b77}}.
The simulations are run for {{formula:f096c5ef-e0db-4d70-84a2-ede34f3d6602}} time steps, {{formula:9231eac5-9112-40aa-8506-c49ec84a1e5c}} for the system to reach the steady state and the rest for averaging properties.
| m | c20675452c24c7b2cb31bfc0270b2d66 |
Many networks evolve through time and are liable to modifications in structure with newly arriving nodes or emerging connections, the GRL methods have primarily addressed static networks, in other words, a snapshot of the networks at a specific time. However, recent years have seen increasing efforts toward modeling dynamic complex networks, see also {{cite:3e69d20ff8aa65c80023ee9b6ed25a1ab880e284}} for a review. Whereas most approaches have concentrated their attention on discrete-time temporal networks, which have built upon a collection of time-stamped networks (c.f. {{cite:f59bab6ee7daaa051c197fbc02d1ff53a507a0e4}}, {{cite:8cd9f00aff43054108170678c29a4cac1612ea9b}}, {{cite:5669879a81eb11bf1c19a00f0023425ee56fc10c}}, {{cite:60821b46f4d56df00cde5b369e5462c08dd62686}}, {{cite:3e22910bca44a2601181b915e86ad754124e9db0}}, {{cite:95c5c037e5c5c23eebbb9372dd72844e661451cc}}, {{cite:1c9ba40626e715d880a6a592c22d94f686dd85ea}}, {{cite:3e69d20ff8aa65c80023ee9b6ed25a1ab880e284}}, {{cite:89e72a18d5c4140ee22eaf82fed4a12162ad8289}}) modeling of networks in continuous time has also been studied (c.f. {{cite:352e174dfe7a6b9f1953ab432f1f25207bfb7623}}, {{cite:6f48671720e72cc5ea008972d902a83a71ddfc3f}}, {{cite:74641c414138b33396c8542a1a771c42883b77c9}}, {{cite:07628dfabda7e91430bcfe75ddf419104693f34a}}). These approaches have been based on latent class {{cite:f59bab6ee7daaa051c197fbc02d1ff53a507a0e4}}, {{cite:352e174dfe7a6b9f1953ab432f1f25207bfb7623}}, {{cite:6f48671720e72cc5ea008972d902a83a71ddfc3f}}, {{cite:74641c414138b33396c8542a1a771c42883b77c9}}, {{cite:8cd9f00aff43054108170678c29a4cac1612ea9b}} and latent feature modeling approaches {{cite:5669879a81eb11bf1c19a00f0023425ee56fc10c}}, {{cite:60821b46f4d56df00cde5b369e5462c08dd62686}}, {{cite:3e22910bca44a2601181b915e86ad754124e9db0}}, {{cite:95c5c037e5c5c23eebbb9372dd72844e661451cc}}, {{cite:1c9ba40626e715d880a6a592c22d94f686dd85ea}}, {{cite:07628dfabda7e91430bcfe75ddf419104693f34a}}, {{cite:3e69d20ff8aa65c80023ee9b6ed25a1ab880e284}}, {{cite:89e72a18d5c4140ee22eaf82fed4a12162ad8289}}, including advanced dynamic graph neural network representations {{cite:f47d9ccd2143e54c019ea32c27ce7203a95a6a4e}}, {{cite:1ec93ade329cfd0a646b63f59be0cb404fad033b}}.
| i | 7f99754ad423f3877846e87e0e38d2ab |
The convergence rate we get is comparable to the convergence rate of Q-learning with a linear learning rate as given in {{cite:177b27fc85468817efd1062fcace251ca2ff260b}}, where the bound they give for synchronous Q-learning is {{formula:2d8bb4c1-3c75-4493-9fe2-3316f8514946}} where {{formula:600fa4f3-9524-40f6-b933-4797548afed8}} is some positive constant. For the asynchronous case, they give a bound of {{formula:199b3184-82ba-4f23-822f-475a2c97052b}} , where {{formula:1ccaac71-ef32-4f1e-a832-1f5f7e3b1e0e}} indicates the 'covering time' constant - a time interval in which all state-action pairs are visited.
The bounds we get are comparable to the ones in {{cite:177b27fc85468817efd1062fcace251ca2ff260b}} in the sense that the dependence on the parameters {{formula:ec8149f3-4005-4967-8ae4-b79a1b4269f2}} are similar, but furthermore, our parameter {{formula:ab5c2278-5a1e-49ba-8a6c-8f61feb56647}} can be viewed as an equivalence to the scaled covering time {{formula:733b6a82-b449-4381-8a8f-b00b71d7d318}} - {{formula:cb14f435-5f17-4ca8-8def-a5a0ae5471b7}} represents the time it takes to cover all state and actions, whereas {{formula:c6afdde0-1500-4add-814d-d1346507760e}} represents the fraction of transitions seen per state and action. When we perform replay, we reduce {{formula:c9090d07-c880-4c08-8a45-020b5a9abecd}} , causing the number of iterations needed for convergence to grow, similarly to the effect caused by a larger covering time {{formula:3f85b7a2-2087-47b3-be47-4459103083b2}} . Indeed, the dependence on {{formula:7160ce84-3743-4bf4-89f7-0a586d085cb8}} is much stronger, because we are not only accounting for the time it takes to update all state and action pairs, but also for the noise accumulation due to the finite number of samples in our memory buffer.
The similarities in the bounds suggest that experience replay iterations can in some cases replace 'real-life' updates, and thus save us iterations in the real world. When we have enough memories, replaying is almost equivalent to sampling from the actual MDP. However, employing experience replay too extensively can lead to the opposite effect and highly increase the number of iterations needed for convergence, as reflected by the dependence on {{formula:d933647e-58d7-4d12-abc5-1c42800f00bc}} which in turn depends on the replay parameters {{formula:ebe64abb-068b-42a5-ad3b-54a4474c1462}} . As we perform M iterations of replay every K iterations in the real world, the requirement to have enough real samples of each state and action in the memory buffer at every iteration (assumption REF ) makes sure that one cannot simply run as many replay iterations as wanted, and that there must be an ongoing balance between real-world and replay iterations to ensure convergence, at least in the proof we provide here. These findings give theoretical support to experimental findings {{cite:d4ed497fd1d83799aec825577ed1b1b3a3658c9a}}, {{cite:0aad9e697773eecbb6488e1fa275d26f1a8b90ff}} showing that algorithm performance is sensitive to the replay ratio - the number of replay updates per real world steps.
| r | b69de0eb05149855089c55392f40f2e7 |
It follows from {{cite:7b1f3bee17a759ae98e1e0f39ea1baddd4cd1ed2}} that
{{formula:7e2101bd-e3d2-49eb-89ae-c24fab1e402d}}
| m | b47b238683896851d61ff388d57c9b81 |
{{formula:c0b88ee6-79d6-4c37-a949-3fd1426003a4}}
{{formula:c4289859-b5d9-4798-93d3-7f6e80d1db23}} {{cite:87d092d3841fbd83cf8e7729d858e90fc8efba79}}, {{cite:0e62fb819c0ea1a785a116b8a2bf6f2a30363d62}} spectral clustering on strongly connected digraphs. To extend this method to the graphs used in our experiments, we employ the teleporting random walk {{cite:8828d0ca53a68d6c7d363ec49752611d481cf187}} defined in Eq. (REF ) endowed with the parameter {{formula:73175850-1639-41ed-a549-e1c5158a7430}} . We use the same cross-validation for {{formula:07f532b6-a715-408e-8485-b6fac9dc6200}} as what mentioned earlier for GSC.
| r | f53f32f5ca974fa21b8a6431276ab92d |
(1) Token slimming compression reduces the complexity of ViT models by reducing the number of input tokens (patches), which is also the focus of this paper.
Table REF shows the comparison with existing token slimming based ViT compression methods, including DynamicViT {{cite:7938e42642c8dbd8f245b613093075ae333fef9b}}, IA-RED{{formula:7deb4998-3796-4cf2-b314-6afe75b66dfa}} {{cite:8cb872f007d4f0e7774fbc319d60eb3fea812891}}, PS-ViT {{cite:fc147daa6e4adb43be4c6286b4a2970d6f75444f}}, EViT {{cite:c619bfd3979e797c8535f2fd81d2cd4f560260aa}}, Evo-ViT {{cite:910a4d451b2217844ffdd145f9b4e67577519bb4}} and SiT {{cite:b3e70c27e07236a8d05169bcc822a9661a358dc1}}. We report top-1 accuracy and FLOPs for performance evaluation. Results with DeiT-S and LV-ViT-S as backbones indicate that our CF-ViT outperforms previous methods w.r.t. accuracy performance and FLOPs reduction. For example, CF-ViT significantly reduces the FLOPs of DeiT-S to 1.8G FLOPs without any compromise on accuracy performance, while the performance of recent advance, Evo-ViT, has only 79.4% with much heavy FLOPs burden of 3.0G. Similar results can be observed when using LV-ViT-S as the backbone.
{{table:3116a93b-babd-4483-9efb-e29f0794f6d0}} | r | 1b559b7879f02d0630f456151186812a |
Table REF shows the results for imbalanced image classification. OPeN obtains SOTA results on all benchmark datasets. For example, on CIFAR-10-LT and CIFAR-100-LT with an imbalance ratio of 100, OPeN outperforms the previous SOTA method, MiSLAS {{cite:4fd4b1d7e3e8d64982799d4521e2cd296ff367a7}} by 4.5% on both datasets. On ImageNet-LT, OPeN is higher than previous SOTA method, LADE {{cite:829cb71c9a59251622624be0e940729e0596f4af}}, by 2.1%. On CelebA-5, OPeN surpasses the previous SOTA method, M2m {{cite:8fa9a9915747e31f7fb5ce9b76a4db06c14f9f81}} by 3.8%. On Places-LT, OPeN achieves comparable results (0.1% better) to previous SOTA, MiSLAS {{cite:4fd4b1d7e3e8d64982799d4521e2cd296ff367a7}}.
| r | ccead979b45c3a7fd8b7d128b2098f2f |
Variable cosmological constant notion has led to a rich structure of phase transitions and critical phenomena. Indeed, in the recent years, thermodynamics and phase transition properties of the black holes in AdS space have been discussed in many studies {{cite:95c9bf93645fabd43aef6d0c130e942f230d8b67}}, {{cite:0ee4327507bdedd3b5766f570d493f311afcde9d}}, {{cite:8eaf0e49e48c952c0559454b502d5a2ff9ea2c28}}, {{cite:7ef47c1d6d9c358bee4da48c8e9b59e64e6b8f89}}, {{cite:36ac959871a8c2f75aa089b4eeb2b25731aa4aff}}, {{cite:58b41157f2b1994621b597477fc9a14cfb3f51bb}}, {{cite:38e000f4ebd53b73fae92f702d5095916ef9f692}}, {{cite:670e331c15c7348a8c93db482fdf9b3e9712180a}}, {{cite:20a0b311bcc8d4219ef85b6b682d921012a5b87d}}, {{cite:62b510202feff11b0073ad48f42b01574a34a065}}, {{cite:a7876dffe888b895798f1cb9e8961d478d104987}}, {{cite:3cb004d0767ba76391cf5503a00974c4740e975a}}, {{cite:05127346fd22520604dc45f8b894411246930dc8}}, {{cite:d64071faaa75fae6506b65df4d2911c4dcd2a6be}}, {{cite:8a85b96d7e8818dffc94bcfde6bad8c2d3714c6a}}, {{cite:40a6b7e3b1a9d909a143dc9b88705934a8cfc01b}}, {{cite:f5b5568b9dce8d8ce2f527dfd4fc89d805a37dae}}, {{cite:51b4ee2564207748bc9833d615f713665b0b1004}}.
| i | 27e0545db9bbc62055b6837997bbb07c |
We also note that the observation of the center of galaxy M87 by Event Horizon Telescope Collaboration {{cite:a3648f1bf540e3841cca355c8b03df7f9fe99c2c}}, {{cite:8d030a2dd07a27ac62ebce8cc690e76b2e3b328f}}, {{cite:138e524826dfbc567cbbbe9e43ca43d6d69c0953}}
implies that the large deviation from Schwarzschild metric and Kerr metric is excluded.
If we assume that the observed shadow size is the same as the size of the primary photon sphere,
the critical impact parameter must be in {{formula:6eaa1295-b646-4d3b-944a-9476948dc3ad}} at 68% confident levels.
Figure REF shows the Schwarzschild and regular black holes with a photon sphere for {{formula:3f7a5279-932c-42b1-ac7d-6c9f06155022}}
and the wormhole with the primary photon sphere and the secondary photon sphere on its throat for {{formula:9e207353-d915-4456-ac43-07793ae2a26d}}
and the wormhole with a photon sphere on the throat for {{formula:18a3976f-474d-4f67-a46b-a91d39258026}} could survive.
However, the effects of the throat on the shadow have subtle problems and we would need simulations to treat them.
{{figure:13ff2efc-92fe-4613-b673-57d93d4b9465}} | d | 133f8f5375483305ec816bc99717bfa5 |
All the data collected with the Pierre Auger Observatory were searched for candidate neutrino events in
the direction of TXS 0506+056 with
negative results. Instead of providing a flux limit we calculate the expected flux
that would have been deduced if a single neutrino had been observed, assuming
a steady flux over a given period of time. This illustrates the expected
sensitivity to a given flux and can be easily
converted to a flux limit at {{formula:69ed0c50-9c1b-427f-85b3-c37b16ff29c7}} confidence multiplying it by a factor
{{formula:667ee8db-e23f-4d78-b8a4-0a7c68c2522d}} {{cite:0e1280155c1f95ebb14052363f035e5ccd0300d1}}. The results
naturally depend on the assumptions that are made with respect to the
time period over which the search is integrated. Two benchmark scenarios have
been discussed in the original article addressing the correlated detection in
neutrinos and in the HE and VHE gamma-ray bands {{cite:1045f5b3a3c21aef85c10b8234af186163f5a628}}.
The first is of half a year and it is motivated by
the time window that gave the largest
significance to a search for an excess of neutrino-compatible events in the
archival data of IceCube, interpreted as a neutrino flare {{cite:d761a81326d2060570e00eb10cacdedfb11201de}}.
The second period corresponds to 7.5 years, the whole observation time that
the IceCube detector had been in operation at the time of detection.
We here address similar scenarios of half a year and the whole observation period
of the Pierre Auger Observatory which is 15 years from 2004 January 1 to
2018 August 31. We note that periods over which the SD was unstable have been
removed from the analysis and that during the first four years of operation the effective area
was a rapidly growing function of time because the Observatory was under
construction until 2008 June.
| r | e522d88a182283584e02ee4fa4a5e043 |
In neuroscience, neuronal assemblies are hypothesized to generate oscillating electromagnetic fields within specific frequency ranges, also called brain rhythms. There has been overwhelming evidence that these brain rhythms can be captured by in-vivo brain recording techniques, from noninvasive methods, such as electroencephalography (EEG) or magnetoencephalography (MEG), to invasive methods, such as intracranial EEG (iEEG) or microelectrode recordings (measuring local field potentials, LFPs) {{cite:30390a03ef5cca413fae2516543ce5058030df2d}}, {{cite:652dc8bcbb5c6f771b92d7e66d1457f0b0278b64}}, {{cite:83ee4e9b5dbb6add477dbb7950ddf5774278e5d7}}, {{cite:ed0cffbb54c5079ba5042614054306cce3e8bcd7}}, {{cite:75acd66e18d96cea4690f42ec8a669ce732e22db}}, {{cite:9d9e45544c47d4c7022781397e30f8f346633c3d}}. A common procedure to investigate brain rhythms in brain recordings is the so-called event-related protocol, where one records how the brain responds to a given stimulation over many repetitions, called trials. For {{formula:3e5dac3e-103a-4e8b-8e59-b78eda8a66ec}} trials, one then has a collection of {{formula:7439e7c0-7326-4769-9498-67650cf28cf9}} signals {{formula:ff409f77-f0f9-4056-8d66-21761b09141a}} , {{formula:7b27f9e4-5574-42be-85c9-293d1fb91b6b}} . The brain activity measured in response to each stimulation is called an event-related response. The exact interpretation of event-related responses remains an open issue in neuroscience. It was suggested that they may result from a variable combination of two phenomena: stimulus-evoked neuronal activity and stimulus-induced phase resetting of ongoing neuronal dynamics {{cite:6d47bc2b70bba6d02e81fef3cb9434efbe8c8292}}, {{cite:975fab8f34454c035fdc3e499d5a23fb97abf475}}, {{cite:eef2b2c8cabf09ad6b550b430f048594921419fc}}, {{cite:1a774cf2307e4460769d340d83524ef570e3d87d}}. In the former, the stimulus leads to the activation of specific neuronal populations, which contribute to new time-locked oscillations at each trial, translating into new power contributions to the ongoing brain activity. In the latter, the stimulus resets the phase of pre-existing oscillations so that a portion of the ongoing activity becomes transiently phase-locked in a particular frequency range. To analyze brain recordings from event-related protocols and be able to discriminate between evoked responses and phase resetting / induced responses, increasingly sophisticated methods have been used, including time-frequency analysis {{cite:d87b3651e7c56ad54e78b3b205665c07c12b2b9d}}, {{cite:45b1252cd392b67cbc2a7e33c2757aa20614c3e5}}, {{cite:4494651d2822196cfc7bac5da83fc1c4beeffc4f}}. So far, three time-frequency measures have been considered. A first measure is the average time-frequency amplitude, i.e., the amplitude {{formula:89c0eed5-7aac-4131-a80b-17b0d52950fa}} of the transform for each single trial {{formula:d691ca63-8227-4627-9f0b-933804a32a2f}} averaged across trials, {{cite:af98f33ea000fff98cfe6ec8aeb17c9120c28d46}}
{{formula:54872122-2b7f-4037-a525-3f182e3f55db}}
| i | dd67f95e899c4fc93b558a7150a8b30e |
We first show that multi-layer HAG graphs do not have a significantly higher value for small {{formula:1fdbe84e-55d2-40a4-b81d-ef50c33e5296}} compared to single-layer HAG graphs; this justifies our focus on single-layer HAG graphs in Theorem REF . We compared FullGreedy single-layer and multi-layer results for three datasets: a Facebook dataset {{cite:371767bc36afe9ea457739aaee205ea5c371549c}}, an Amazon co-purchases dataset {{cite:58d89f4f486485daac989cdc88a9f95d4bd4d45d}} (the subset from March 2nd, 2003), and the Email-EU dataset {{cite:91958a75674944e5fd7f2b30d0705aae79685f91}}All three of these datasets can be found at snap.stanford.edu/data. On average over {{formula:ec88ad56-04a4-42a1-9333-a8986d25511d}} , the multi-layer results increased the value compared to the single-layer solution by {{formula:b016124d-8ff5-41df-a442-8e5a345728ab}} , {{formula:b043f688-af76-46c6-bd71-b3d2f35bed0b}} , and {{formula:6d70ada9-1723-48a1-b011-68c9ad4fb543}} , respectively (see Table REF ).
{{table:a0dfaec0-1056-4586-a374-a7d3675d146c}} | r | b65ef456beb545f93f364aa424dbcbda |
All results in the previous sections are based on mathematics;
the derived relations, however, require interpretations. In traditional applied mathematics, interpretations are based on existing narratives within a scientific field. However, for the present work such a discussion seems to be beyond the current understanding of theoretical physics, on mechanics and on statistical thermodynamics. The most unsettling element of the implication is that the law of mechanics could be understood through an entropic theory. This idea is not new {{cite:51a1d8b7cb5bac9c924da4f834bfb8ba070374e8}}; the verbiage below, therefore, could be considered as a part of a scientific hypothesis. In terms of a mathematical limit, there are several widely used idealizations in our understanding of the physical world: inifitely large systems and infinitely long time stationary processes, for examples. The present work explores the statistics of infinitely divisible time itself.
| d | 40952d0733e0dd938a1deb8d6bb45d98 |
Most of the existing CNN-based deraining methods are full-supervised, in which they pay most of their attention to the architecture design of the network such as the multi-stage {{cite:5691aa20f3f128834917cc064ae7fb21a5e9c7f2}}, {{cite:3544c903062787f0dc66fc66a71a0af466195eab}}, {{cite:2c5f0445fdaf61affe87d5e1e18db6c9b4c3b6f6}}, {{cite:e42d7ce37b8084806b9560302a1186516c4a14d9}}, {{cite:88f267fb03a1e993003a194903b1fc1f3ae1af4c}}, multi-scale {{cite:3978c6479e977942086d528de2f2910f9b4532aa}}, {{cite:03401e0118ba82cd7593adb3880b2d73a589d4a2}}, {{cite:a27459cc44bde31866c6e8336c81a46587a45fd2}}, {{cite:1016b26979d9b068655a0c76d72589903424918c}}, attention {{cite:af09be1ed9476235bddf47e3405cb2eba6811b87}}, {{cite:b59b44fff237e26bf1fabda5c6c261d80155fba6}}, {{cite:8c1d9af9ce460213141f6f7e67db07ac7541fc64}}, {{cite:a27459cc44bde31866c6e8336c81a46587a45fd2}}, so as to better improve the representation for the rain streaks. The full-supervised deraining methods heavily rely on the paired clean and rainy image. The existing full-supervised methods usually construct a rain synthesis model to generate the simulated rainy image. However, there exist a huge gap between the real and synthetic rains. That is the main reason why the existing CNN methods have been less generalized for the real rain streaks. The semi-supervised deraining methods {{cite:54316f7932eaf14a34f8dc7ed0a6ade3358463fb}}, {{cite:953c9e271ba548a01e7623b71cf5cd57c45d38e0}}, {{cite:0ba73cdba5fc2a45aa4139e8f79d16e536d4e373}} could alleviate the generalization issue to some extent by introducing the real rainy image as the additional constraint. However, the problem still exists since these semi-supervised methods also employ the synthetic rainy image.
| i | 8b8e0aca57d0cd515268a169445dee0a |
Our findings open a new scenario of phase-ordering kinetics, associated to the landscape-inversion phase transition (LIPT), in which several unexpected phenomena take place. In particular, these include a new mechanism to form metastable phases, namely by spinodal decomposition. By this mechanism, metastable phase formation is robust within the LIPT scenario, occurring regardless of the amplitude or the rate of the quench. Remarkably, the LIPT never leads to nucleation despite the existence of a metastable state in the free energy. A summary of the phase-ordering processes allowed by this nonstandard scenario is provided in Fig. REF a. There, the case of a conserved order parameter has also been included for completeness (see Supplementary Discussion for details). Additionally, a concise summary of the phase-ordering processes associated to the classical A and B models of phase transition dynamics {{cite:1022e953af992882c0f678df7811c3c2c1d083a8}} is given in Fig. REF b for comparison (see Supplementary Discussion).
{{figure:9228fc16-ec4b-4983-9224-d329d3b761df}} | d | 1a599dada0f7fac1b38dc35b2048d3b3 |
A wide range of imaging modalities use tomographic reconstruction
algorithms to form 2-D and 3-D images from their projection data. While
the classical Filtered Back Projection (FBP) algorithm, and its
variants, are widely used in practice {{cite:33c2874c214990976d421f8643d4ccbfa6373dd8}}, iterative
reconstruction algorithms hold great potential to enable high-quality
imaging from limited projection data (e.g., few views, limited-view, low-dose) and reduce exposure to radiation. Developments, over last several
decades, in this area often formulate the image reconstruction as an
(ill-posed) inverse problem where a regularized solution is found by an
iterative optimization algorithm. Several aspects of iterative
reconstruction algorithms {{cite:af7bfd1ea17d0c0cfc2d38b76398517a7cf01d55}}, {{cite:3d227738d923525db1f96df8ec75e23fcfd63206}}, {{cite:1cb5db7ba39d5e346233f222789e9f306b213a95}}, {{cite:7eb433ffd46425047814d2e3a9d1e39af17a56e4}} overlap with active areas of research in
solving these optimization problems efficiently as well as image modeling
with regularization (e.g., sparsity-based, network-based) that enhance
the quality of recovered image from limited data.
| i | 1927fd20eb9fa78fab252af31e9c47a3 |
However, the aforementioned spectral GNN methods share a few significant weaknesses: the Laplacian eigenfunctions are inconsistent across different domains, and therefore the method generalizes poorly across different geometries. Moreover, the spectral convolution filter is applied for the whole graph without considering the local graph structure property. In addition, the graph Fourier transform has high cost in computation. To solve the locality and efficiency problem, ChebNets {{cite:0d4b713f6d99ef61ccdc170b37ae7fec5458d5af}} employs the Chebyshev polynomial basis to represent spectral convolution filter instead of graph Laplacian eigenvectors.
| m | 3e496e5a924480ee66ecd03ae2b6a80b |
The flux threshold of the current MeV gamma-ray mission INTEGRAL is {{formula:2f665ded-d3ea-44c0-baf8-826b09072b1a}} erg cm{{formula:d7488777-3c80-457e-a792-c6ecd6f5f1c1}} s{{formula:a63b3e79-2b62-4f9f-b9e4-95c903204269}} (15 keV{{formula:9c365c47-03b2-4eb0-9c99-9d0abe8f864e}} MeV, exposure time {{formula:1986c4de-19e6-4c89-b574-5cdae674ab14}} s, {{cite:3e4bed6deb72e151660f4e5b9b11da54e14d9957}}), being much higher than the peak gamma-ray energy fluxes derived from our analysis.
The proposed MeV gamma-ray instruments, such as ETCC ({{formula:33a4115d-d4f8-4123-ab9e-ee17da1e6cff}} MeV, {{cite:ff098136d3dccf913a3f46514d1b898db45ac834}}) and AMEGO ({{formula:d9eb5c15-b1dc-4daa-97b7-a5f7e3aefa15}} MeV{{formula:325993ab-6f2c-4832-b230-ddc63076fc10}} GeV, {{cite:bcdf9c0891e6220a42f51f26b917986e09dbdac7}}), have sensitivity of {{formula:247df9a4-ccaa-4a2b-8c44-13050a6ba58c}} erg cm{{formula:eaaea82a-84f6-4ce8-b431-10f21cb766a7}} s{{formula:a7f44d87-c6d3-49cd-9a4a-161b57219f6e}} and {{formula:151db600-3fa4-443e-a563-22ed51961f25}} erg cm{{formula:60793f88-5bdb-4135-8e0e-850eb3f4be55}} s{{formula:300a8fb8-ea38-42a7-bc1c-77d63094c81b}} for exposure time {{formula:490dc049-4691-4854-ad24-8d11f455609e}} s, being closer to the peak gamma-ray flux of the GRT. For convincingly detecting such a GRT, the sensitivity of the instruments should be improved at least one order of magnitude.
To identify the gamma-ray line features, a detector having precent-level energy resolution would be most desired.
The proposed gamma-ray mission AMEGO can provide {{formula:98fe74d7-2dfd-4b4c-af8f-60f80b5da106}} energy resolution in the MeV band, but it is not sensitive enough for convincing detection of such a GRT at 40 Mpc.
| d | bec0f36d27294697b5d283d2e9daf12d |
The success in obtaining WSS from core flow measurement is tightly related to flow physics and the relation between WSS patterns and coherent structures away from the wall where the topological {{cite:008567ac24df7343501e55bfcc21b55f912161df}} and dynamical {{cite:316db86a5a8b36c53a9d6585cdb6c719309bb2ee}} features in WSS depend on flow patterns away from the wall. Velocity vectors away from the wall could be obtained theoretically using Taylor series expansion of wall quantities such as WSS and pressure {{cite:8da9b66e8736d6ce30907a88bb7cbebe68018b44}}, {{cite:6905c50c6c101f79c0a420b72adc59d31057ac69}}. Machine learning models have been designed leveraging these mathematical relationships in estimating near-wall turbulent flows {{cite:8c5d205e24ac95ea02a5e2abfff7d9088598db8b}}. It is possible to account for these relationships in PINN when the data analysis is only focused on the near-wall region (e.g., the 3D example in this study).
| d | 4820a6acd79a3a3157c427ae19c9159e |
The operators that we want to study in this paper, are
in the complex Lebesgue space {{formula:d4953c2a-db72-4aad-87f2-f22e7e703387}} , when
{{formula:9ef00d46-84b1-48f2-bb5e-c9ed72cc7bcc}} is a locally compact group with a right Haar measure
{{formula:0573f8c2-6711-4cb0-b0a8-bb013054a589}} . Note that, the Lebesgue space {{formula:ab808e13-0df2-4b3b-be2d-67156383c3e3}}
with recent properties is separable whenever {{formula:ad9c3550-428f-4915-89d9-60d478564e1f}}
is second countable.
We remind that a torsion element in a locally compact group {{formula:7075c463-3929-4fce-9b27-e8d844a501af}} is an element of finite order,
and an element {{formula:023ad848-dba7-4336-a4dc-bb4b6a0bd211}} is called compact {{cite:5d619b0efbc0b3b23643d2423cdfb702d9575a1a}} (or periodic {{cite:1eb64e340f25168c26b164e828fb6d40bf9b86e2}})
if the closed subgroup {{formula:8867e922-671f-4eae-904a-3d72e3e929e2}} generated by {{formula:f03a1320-ae30-48ba-abbe-01e194aaad14}} is compact. Also, an element in
{{formula:a601ee67-8051-4ea2-b579-c2e3062200b0}} is called aperiodic if it is not periodic.
Observe that in discrete groups, a periodic element is a torsion element and conversely.
| r | 6cd97dbd144821ae96a422b5d62bcdd1 |
As described in previous works {{cite:c71215f911a92a872107278dcc6aaa97f2a0929f}}, {{cite:41b6d685450cb33f7c0efd5fccd7d453f15f3521}}, the magnetization of {{formula:8f75a67c-cbb7-4208-a737-b68db5e09344}} ions in bulk {{formula:8733d2e4-6b47-4ea7-891f-b3396d17cbc1}} samples displays a strong axial-SIA with the axis symmetry along the c axis of the wurtzite structure. The magnetic behavior of {{formula:b2f198d0-cb38-458f-8b30-e9402c48c1b2}} ions can then be described by using an effective spin S = 3/2 and the conventional spin Hamiltonian {{cite:848a0782a5de40fd0472822593aaaf30961b6450}}:
{{formula:3f602b35-10fc-4545-b6b5-18a86f030bc7}}
| r | f14fba3bf98c10c845437835a6a0a164 |
Specifically, given an input facial image, we first encode the high-level and low-level feature maps by the ResNet backbone {{cite:8a885f0937b5d117314d323127e84974ac1fdcca}}.
Then, we build a projection matrix to map a cluster of pixels with similar features to each vertex.
The feature of each vertex is taken as the weighted aggregation of pixel-wise features in the cluster, where features of edge pixels are assigned with larger weights via an edge mask.
Next, we learn and reason over the relations between vertices (i.e., regions) via graph convolution {{cite:3a78016e9d74e2b26319bd09a308a88feeab06e2}}, {{cite:1cce8a0c473f30874cc39b05b79a27ddf3df0bc7}} to further extract global semantic features.
The learned features are finally projected back to a pixel-wise feature map.
We test our model on Helen, CelebAMask-HQ and LaPa datasets, and surpass state-of-the-art methods.
| i | b8c32381f635b63fd31495bca0e04562 |
In this work, we focus on a quantum pulse optimization problem having binary
control variables and restricted feasible regions derived by linear
constraints. These constraints describe a so-called bang-bang control in a model
that corresponds to a quantum circuit design similar to that in the quantum
approximate optimization algorithm {{cite:d9d9a6b87623363c1a002edfaf5c3f8c11e11d82}} and other
variational quantum algorithms. We
introduce three quantum control examples: (i) energy minimization
problem, (ii) controlled NOT (CNOT) gate estimation problem, and (iii) circuit
compilation problem.
The nonconvexity, binary variables, and restricted feasible
sets in these examples lead to extreme challenges and difficulties in solving
the related binary quantum control problems.
| i | e4b6df5c00d310417f932cba9a2f22ac |
The implementation of the different CL methods is based on the one published in {{cite:bef85a0c86199dc39195f5c8e9cace148b55ea9d}}. For a broader reproducible benchmark of CL methods in wireless communication, we will publish on Github the SNR and {{formula:200ef24d-1fb0-4c3f-9dde-f7040f02cdea}} tasks as well as instructions needed to reproduce the main experimental results presented in Section .
| m | 98edede9f31d555bf7dd569c36b21f08 |
We observe that {{formula:bb2e4614-e0f9-403a-b259-fbcced837da3}} , thus {{formula:1acfdf5b-f579-4f95-9aa6-2b481a23820a}} is an atom with additional vanishing moments.
The same argument utilized to obtain the pointwise estimate that appears in (REF ) works if we consider the operator {{formula:9d119999-3624-438b-994d-dcd72c7304ef}} instead {{formula:818a58fa-60b0-4ba7-82c4-e04ed08c7530}} , so
{{formula:b25ac4a8-0736-4801-a1f5-6742e5b44ed7}}
for all {{formula:bcfa4912-af0a-4f7f-8471-94ad3f58ea79}} and all {{formula:77091c3a-c82d-4935-82ad-7adca59d74f9}} . Taking
{{formula:8dbcfd88-3e4c-4dbc-a7c9-2d78852ea2ba}} in (REF ), a simple computation gives
{{formula:d76f19e0-240d-4d33-95f5-eaa79e86f263}}
where {{formula:e208652b-6fc0-40d0-80e8-593c5e330222}} .
Let {{formula:cb6dc09c-168b-412a-aa6f-f4d667ff1bfb}} , from the {{formula:81d7eb39-c326-40e9-9eab-10a08149163f}} boundedness of {{formula:677f0b3e-7692-47f0-9cb4-8579f92951b4}} and Remark 8, we obtain
{{formula:a22b02f0-3d3e-45ec-bbbc-94bde4c29ef6}}
Taibleson and Weiss in {{cite:9ed6198b138ab364ffc4b21d6004420b66cea021}} proved that
{{formula:8565954d-1966-4169-9af5-2669a5c10945}}
for {{formula:4da25e6f-edef-46a6-8f40-c0d87bec128f}} .
Finally, we observe that the argument utilized in Proof of Theorem 5.2 in {{cite:24a8a37f777825ebdb695e6b0608e742169ba83f}} works in this setting, but considering now the estimates (REF ), (REF ) and the moment condition (REF ). Therefore we get (REF ).
| r | e105fabffc8b95c36d95588b3ed6433d |
Our results differ from previous studies of this system as we added 119 new HARPS spectra, making a total of 172 RV measurements. We also incorporated photometric observations. {{cite:028de7e9892ff5e3ab69f22688c1dffaac77055e}} performed their analysis with a total of 109 RV spectra when reporting on planet c. When analysing this set of data, after performing a 1-Keplerian fit to the planet in the outer orbit, we also found a significant (3{{formula:f8fe540d-0f5c-4b29-85f6-7aa3d01831b2}} ) signal around 35 days in the residual. However, as we added the new HARPS data, this signal has lost its significance and does not even reach the detection threshold (bottom panel of Fig. REF ) and it is only with the new HARPS RV data that we are able to determine the true origin of the 35d signal. This is particularly noticeable for the addition of the RedDots data (56 RV measurements) which were taken with approximately nightly cadence to minimise sources of correlated noise and to be able to quantify the evolution of stellar activity features {{cite:a0a0cb83a5a136dee03e3a9975e5e67631686de1}}.
| d | a2df5858bb75fb1b6655bee09e43f8c8 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.