@article{17591, abstract = {The rotational kinematic Sunyaev-Zeldovich (rkSZ) signal, imprinted on the cosmic microwave background (CMB) by the gaseous halos (spinning “atmospheres”) of foreground galaxies, would be a novel probe of galaxy formation. Although the signal is too weak to detect in individual galaxies, we analyze the feasibility of its statistical detection via stacking CMB data on many galaxies for which the spin orientation can be estimated spectroscopically. We use an “optimistic” model, in which fully ionized atmospheres contain the cosmic baryon fraction and spin at the halo’s circular velocity 𝑣circ, and a more realistic model, based on hydrodynamical simulations, with multiphase atmospheres spinning at a fraction of 𝑣circ. We incorporate realistic noise estimates into our analysis. Using low-redshift galaxy properties from the MaNGA spectroscopic survey (with median halo mass of 6.6×1011  𝑀⊙), and CMB data quality from Planck, we find that a 3⁢𝜎 detection would require a few×104 galaxies, even in the optimistic model. This is too high for current surveys, but upcoming higher-angular resolution CMB experiments will significantly reduce the requirements: stacking CMB data on galaxy spins in a ∼10 deg2 can rule out the optimistic models, and ≈350  deg2 will suffice for a 3⁢𝜎 detection with ACT. As a proof-of-concept, we stacked Planck data on the position of ≈2,000 MaNGA galaxies, aligned with the galaxies’ projected spin, and scaled to their halos’ angular size. We rule out average temperature dipoles larger than ≈1.9  𝜇⁢K around field spiral galaxies.}, author = {Matilla, José Manuel Zorrilla and Haiman, Zoltán}, issn = {2470-0010}, journal = {Physical Review D}, number = {8}, publisher = {American Physical Society}, title = {{Probing gaseous galactic halos through the rotational kinematic Sunyaev-Zeldovich effect}}, doi = {10.1103/physrevd.101.083016}, volume = {101}, year = {2020}, } @article{17595, abstract = {We study the thermal evolution of UV-irradiated atomic cooling haloes using high-resolution three-dimensional hydrodynamic simulations. We consider the effect of H− photodetachment by Lyα cooling radiation trapped in the optically-thick cores of three such haloes, a process that has not been included in previous simulations. Because H− is a precursor of molecular hydrogen, its destruction can diminish the H2 abundance and cooling. We find that the critical UV flux for suppressing H2-cooling is decreased by ∼15–50 per cent in our fiducial models. Previous one-zone modelling found a larger effect, with Jcrit reduced by a factor of a few; we show that adopting a constant halo mass to determine the trapped Lyα energy density, as is done in the one-zone models, yields a larger reduction in Jcrit, consistent with their findings. Our results nevertheless suggest that Lyα radiation may have an important effect on the thermal evolution of UV-irradiated haloes, and therefore on the potential for massive black hole formation.}, author = {Wolcott-Green, Jemma and Haiman, Zoltán and Bryan, Greg L}, issn = {0035-8711}, journal = {Monthly Notices of the Royal Astronomical Society}, number = {1}, pages = {138--144}, publisher = {Oxford University Press}, title = {{Suppression of H2 cooling in protogalaxies aided by trapped Lyα cooling radiation}}, doi = {10.1093/mnras/staa3057}, volume = {500}, year = {2020}, } @article{17596, abstract = {Binary black hole mergers encode information about their environment and the astrophysical processes that led to their formation. Measuring the redshift dependence of their merger rate will help probe the formation and evolution of galaxies and the evolution of the star formation rate. Here we compute the cosmic evolution of the merger rate for stellar-mass binaries in the disks of active galactic nuclei (AGNs). We focus on recent evolution out to redshift z = 2, covering the accessible range of current Earth-based gravitational-wave observatories. On this scale, the AGN population density is the main contributor to redshift dependence. We find that the AGN-assisted merger rate varies by less than a factor of two in the range 0 < z ≤ 2, comparable to the expected level of evolution for globular clusters, but much smaller than the order-of-magnitude evolution for field binaries.}, author = {Yang, Y. and Bartos, I. and Haiman, Zoltán and Kocsis, B. and Márka, S. and Tagawa, H.}, issn = {0004-637X}, journal = {The Astrophysical Journal}, number = {2}, publisher = {American Astronomical Society}, title = {{Cosmic evolution of stellar-mass black hole merger rate in active galactic nuclei}}, doi = {10.3847/1538-4357/ab91b4}, volume = {896}, year = {2020}, } @article{17597, abstract = {Deep Neural Networks (DNNs) are powerful algorithms that have been proven capable of extracting non-Gaussian information from weak lensing (WL) data sets. Understanding which features in the data determine the output of these nested, non-linear algorithms is an important but challenging task. We analyze a DNN that has been found in previous work to accurately recover cosmological parameters in simulated maps of the WL convergence (κ). We derive constraints on the cosmological parameter pair (Ωm,σ8) from a combination of three commonly used WL statistics (power spectrum, lensing peaks, and Minkowski functionals), using ray-traced simulated κ maps. We show that the network can improve the inferred parameter constraints relative to this combination by 20% even in the presence of realistic levels of shape noise. We apply a series of well established saliency methods to interpret the DNN and find that the most relevant pixels are those with extreme κ values. For noiseless maps, regions with negative κ account for 86−69% of the attribution of the DNN output, defined as the square of the saliency in input space. In the presence of shape nose, the attribution concentrates in high convergence regions, with 36−68% of the attribution in regions with κ>3σκ.}, author = {Matilla, José Manuel Zorrilla and Sharma, Manasi and Hsu, Daniel and Haiman, Zoltán}, issn = {2470-0010}, journal = {Physical Review D}, number = {12}, publisher = {American Physical Society}, title = {{Interpreting deep learning models for weak lensing}}, doi = {10.1103/physrevd.102.123506}, volume = {102}, year = {2020}, } @article{17600, abstract = {The quest for binary and dual supermassive black holes (SMBHs) at the dawn of the multi-messenger era is compelling. Detecting dual active galactic nuclei (AGN) – active SMBHs at projected separations larger than several parsecs – and binary AGN – probing the scale where SMBHs are bound in a Keplerian binary – is an observational challenge. The study of AGN pairs (either dual or binary) also represents an overarching theoretical problem in cosmology and astrophysics. The AGN triggering calls for detailed knowledge of the hydrodynamical conditions of gas in the imminent surroundings of the SMBHs and, at the same time, their duality calls for detailed knowledge on how galaxies assemble through major and minor mergers and grow fed by matter along the filaments of the cosmic web. This review describes the techniques used across the electromagnetic spectrum to detect dual and binary AGN candidates and proposes new avenues for their search. The current observational status is compared with the state-of-the-art numerical simulations and models for formation of dual and binary AGN. Binary SMBHs are among the loudest sources of gravitational waves (GWs) in the Universe. The search for a background of GWs at nHz frequencies from inspiralling SMBHs at low redshifts, and the direct detection of signals from their coalescence by the Laser Interferometer Space Antenna in the next decade, make this a theme of major interest for multi-messenger astrophysics. This review discusses the future facilities and observational strategies that are likely to significantly advance this fascinating field.}, author = {De Rosa, Alessandra and Vignali, Cristian and Bogdanović, Tamara and Capelo, Pedro R. and Charisi, Maria and Dotti, Massimo and Husemann, Bernd and Lusso, Elisabeta and Mayer, Lucio and Paragi, Zsolt and Runnoe, Jessie and Sesana, Alberto and Steinborn, Lisa and Bianchi, Stefano and Colpi, Monica and del Valle, Luciano and Frey, Sándor and Gabányi, Krisztina É. and Giustini, Margherita and Guainazzi, Matteo and Haiman, Zoltán and Herrera Ruiz, Noelia and Herrero-Illana, Rubén and Iwasawa, Kazushi and Komossa, S. and Lena, Davide and Loiseau, Nora and Perez-Torres, Miguel and Piconcelli, Enrico and Volonteri, Marta}, issn = {1387-6473}, journal = {New Astronomy Reviews}, publisher = {Elsevier BV}, title = {{The quest for dual and binary supermassive black holes: A multi-messenger view}}, doi = {10.1016/j.newar.2020.101525}, volume = {86}, year = {2020}, } @article{17601, abstract = {Using numerical hydrodynamics calculations and a novel method for densely sampling parameter space, we measure the accretion and torque on a binary system from a circumbinary disk. In agreement with some earlier studies, we find that the net torque on the binary is positive for mass ratios close to unity, and that accretion always drives the binary toward equal mass. Accretion variability depends sensitively on the numerical sink prescription, but the torque and relative accretion onto each component do not depend on the sink timescale. Positive torque and highly variable accretion occurs only for mass ratios greater than around 0.05. This means that for mass ratios below 0.05, the binary would migrate inward until the secondary accreted sufficient mass, after which it would execute a U-turn and migrate outward. We explore a range of viscosities, from α = 0.03 to α = 0.15, and find that this outward torque is proportional to the viscous torque, so that torque per unit accreted mass is independent of α. Dependence of accretion and torque on mass ratio is explored in detail, densely sampling mass ratios between 0.01 and unity. For mass ratio q > 0.2, accretion variability is found to exhibit a distinct sawtooth pattern, typically with a five-orbit cycle that provides a smoking gun prediction for variable quasars observed over long periods, as a potential means to confirm the presence of a binary.}, author = {Duffell, Paul C. and D’Orazio, Daniel and Derdzinski, Andrea and Haiman, Zoltán and MacFadyen, Andrew and Rosen, Anna L. and Zrake, Jonathan}, issn = {0004-637X}, journal = {The Astrophysical Journal}, number = {1}, publisher = {American Astronomical Society}, title = {{Circumbinary disks: Accretion and torque as a function of mass ratio and disk viscosity}}, doi = {10.3847/1538-4357/abab95}, volume = {901}, year = {2020}, } @article{17604, abstract = {Numerical studies of gas accretion onto supermassive black hole binaries (SMBHBs) have generally been limited to conditions where the circumbinary disk (CBD) is 10-100 times thicker than expected for disks in active galactic nuclei (AGN). This discrepancy arises from technical limitations, and also from publication bias toward replicating fiducial numerical models. Here we present the first systematic study of how the binary's orbital evolution varies with disk scale height. We report three key results: (1) Binary orbital evolution switches from outspiralling for warm disks (aspect ratio ~0.1), to inspiralling for more realistic cooler, thinner disks at a critical aspect ratio ~0.04, corresponding to orbital Mach number ~25. (2) The net torque on the binary arises from a competition between positive torque from gas orbiting close to the black holes, and negative torque from the inner edge of the CBD, which is denser for thinner disks. This leads to increasingly negative net torques on the binary for increasingly thin disks. (3) The accretion rate is modestly suppressed with increasing Mach number. We discuss how our results may influence modeling of the nano-Hz gravitational wave background, as well as estimates of the LISA merger event rate.}, author = {Tiede, Christopher and Zrake, Jonathan and MacFadyen, Andrew and Haiman, Zoltán}, issn = {0004-637X}, journal = {The Astrophysical Journal}, number = {1}, publisher = {American Astronomical Society}, title = {{Gas-driven inspiral of binaries in thin accretion disks}}, doi = {10.3847/1538-4357/aba432}, volume = {900}, year = {2020}, } @article{17605, abstract = {Despite the rapidly growing number of stellar-mass binary black hole mergers discovered through gravitational waves, the origin of these binaries is still not known. In galactic centers, black holes can be brought to each others' proximity by dynamical processes, resulting in mergers. It is also possible that black holes formed in previous mergers encounter new black holes, resulting in so-called hierarchical mergers. Hierarchical events carry signatures such as higher-than-usual black hole mass and spin. Here we show that the recently reported gravitational-wave candidate, GW170817A, could be the result of such a hierarchical merger. In particular, its chirp mass ∼40 M⊙ and effective spin of χeff ∼ 0.5 are the typically expected values from hierarchical mergers within the disks of active galactic nuclei. We find that the reconstructed parameters of GW170817A strongly favor a hierarchical merger origin over having been produced by an isolated binary origin (with an odds ratio of > 10^3).}, author = {Gayathri, V. and Bartos, I. and Haiman, Zoltán and Klimenko, S. and Kocsis, B. and Márka, S. and Yang, Y.}, issn = {2041-8205}, journal = {The Astrophysical Journal Letters}, number = {2}, publisher = {American Astronomical Society}, title = {{GW170817A as a hierarchical black hole merger}}, doi = {10.3847/2041-8213/ab745d}, volume = {890}, year = {2020}, } @article{17607, abstract = {The heaviest neutron stars and lightest black holes expected to be produced by stellar evolution leave the mass-range 2.2 M⊙≲m≲5 M⊙ largely unpopulated. Objects found in this so-called lower mass gap likely originate from a distinct astrophysical process. Such an object, with mass 2.6 M⊙ was recently detected in the binary merger GW190814 through gravitational waves by LIGO/Virgo. Here we show that black holes in the mass gap are naturally assembled through mergers and accretion in AGN disks, and can subsequently participate in additional mergers. We compute the properties of AGN-assisted mergers involving neutron stars and black holes, accounting for accretion. We find that mergers in which one of the objects is in the lower mass gap represent up to 4% of AGN-assisted mergers detectable by LIGO/Virgo. The lighter object of GW190814, with mass 2.6 M⊙, could have grown in an AGN disk through accretion. We find that the unexpectedly high total mass of 3.4 M⊙ observed in the neutron star merger GW190425 may also be due to accretion in an AGN disk.}, author = {Yang, Y. and Gayathri, V. and Bartos, I. and Haiman, Zoltán and Safarzadeh, M. and Tagawa, H.}, issn = {2041-8205}, journal = {The Astrophysical Journal Letters}, number = {2}, publisher = {American Astronomical Society}, title = {{Black hole formation in the lower mass gap through mergers and accretion in AGN disks}}, doi = {10.3847/2041-8213/abb940}, volume = {901}, year = {2020}, } @article{17908, abstract = {Ferrocenes are ubiquitous organometallic building blocks that comprise a Fe atom sandwiched between two cyclopentadienyl (Cp) rings that rotate freely at room temperature. Of widespread interest in fundamental studies and real-world applications, they have also attracted some interest as functional elements of molecular-scale devices. Here we investigate the impact of the configurational degrees of freedom of a ferrocene derivative on its single-molecule junction conductance. Measurements indicate that the conductance of the ferrocene derivative, which is suppressed by 2 orders of magnitude as compared to a fully conjugated analogue, can be modulated by altering the junction configuration. Ab initio transport calculations show that the low conductance is a consequence of destructive quantum interference effects of the Fano type that arise from the hybridization of localized metal-based d-orbitals and the delocalized ligand-based π-system. By rotation of the Cp rings, the hybridization, and thus the quantum interference, can be mechanically controlled, resulting in a conductance modulation that is seen experimentally.}, author = {Camarasa-Gómez, María and Hernangómez-Pérez, Daniel and Inkpen, Michael S. and Lovat, Giacomo and Fung, E-Dean and Roy, Xavier and Venkataraman, Latha and Evers, Ferdinand}, issn = {1530-6992}, journal = {Nano Letters}, number = {9}, pages = {6381--6386}, publisher = {American Chemical Society}, title = {{Mechanically tunable quantum interference in ferrocene-based single-molecule junctions}}, doi = {10.1021/acs.nanolett.0c01956}, volume = {20}, year = {2020}, } @article{17911, abstract = {Single-molecule break junction measurements deliver a huge number of conductance vs. electrode separation traces. During such measurements, the target molecules may bind to the electrodes in different geometries, and the evolution and rupture of the single-molecule junction may also follow distinct trajectories. The unraveling of the various typical trace classes is a prerequisite to the proper physical interpretation of the data. Here we exploit the efficient feature recognition properties of neural networks to automatically find the relevant trace classes. To eliminate the need for manually labeled training data we apply a combined method, which automatically selects training traces according to the extreme values of principal component projections or some auxiliary measured quantities. Then the network captures the features of these characteristic traces and generalizes its inference to the entire dataset. The use of a simple neural network structure also enables a direct insight into the decision-making mechanism. We demonstrate that this combined machine learning method is efficient in the unsupervised recognition of unobvious, but highly relevant trace classes within low and room temperature gold–4,4′ bipyridine–gold single-molecule break junction data.}, author = {Magyarkuti, András and Balogh, Nóra and Balogh, Zoltán and Venkataraman, Latha and Halbritter, András}, issn = {2040-3372}, journal = {Nanoscale}, number = {15}, pages = {8355--8363}, publisher = {Royal Society of Chemistry}, title = {{Unsupervised feature recognition in single-molecule break junction data}}, doi = {10.1039/d0nr00467g}, volume = {12}, year = {2020}, } @article{18194, abstract = {Realizing strongly correlated topological phases of ultracold gases is a central goal for ongoing experiments. While fractional quantum Hall states could soon be implemented in small atomic ensembles, detecting their signatures in few-particle settings remains a fundamental challenge. In this work, we numerically analyze the center-of-mass Hall drift of a small ensemble of hardcore bosons, initially prepared in the ground state of the Harper-Hofstadter-Hubbard model in a box potential. By monitoring the Hall drift upon release, for a wide range of magnetic flux values, we identify an emergent Hall plateau compatible with a fractional Chern insulator state: The extracted Hall conductivity approaches a fractional value determined by the many-body Chern number, while the width of the plateau agrees with the spectral and topological properties of the prepared ground state. Besides, a direct application of Streda's formula indicates that such Hall plateaus can also be directly obtained from static density-profile measurements. Our calculations suggest that fractional Chern insulators can be detected in cold-atom experiments, using available detection methods.}, author = {Repellin, C. and Leonard, Julian and Goldman, N.}, issn = {2469-9934}, journal = {Physical Review A}, number = {6}, publisher = {American Physical Society}, title = {{Fractional Chern insulators of few bosons in a box: Hall plateaus from center-of-mass drifts and density profiles}}, doi = {10.1103/physreva.102.063316}, volume = {102}, year = {2020}, } @article{18228, abstract = {We introduce two constructions in geometric deep learning for 1) transporting orientation-dependent convolutional filters over a manifold in a continuous way and thereby defining a convolution operator that naturally incorporates the rotational effect of holonomy; and 2) allowing efficient evaluation of manifold convolution layers by sampling manifold valued random variables that center around a weighted diffusion mean. Both methods are inspired by stochastics on manifolds and geometric statistics, and provide examples of how stochastic methods – here horizontal frame bundle flows and non-linear bridge sampling schemes, can be used in geometric deep learning. We outline the theoretical foundation of the two methods, discuss their relation to Euclidean deep networks and existing methodology in geometric deep learning, and establish important properties of the proposed constructions.}, author = {Sommer, Stefan and Bronstein, Alexander}, issn = {1939-3539}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, number = {2}, pages = {811--822}, publisher = {Institute of Electrical and Electronics Engineers}, title = {{Horizontal flows and manifold stochastics in geometric deep learning}}, doi = {10.1109/tpami.2020.2994507}, volume = {44}, year = {2020}, } @inproceedings{18247, abstract = {Convolutional neural networks (CNNs) achieve state-of-the-art accuracy in a variety of tasks in computer vision and beyond. One of the major obstacles hindering the ubiquitous use of CNNs for inference on low-power edge devices is their high computational complexity and memory bandwidth requirements. The latter often dominates the energy footprint on modern hardware. In this paper, we introduce a lossy transform coding approach, inspired by image and video compression, designed to reduce the memory bandwidth due to the storage of intermediate activation calculation results. Our method does not require fine-tuning the network weights and halves the data transfer volumes to the main memory by compressing feature maps, which are highly correlated, with variable length coding. Our method outperform previous approach in term of the number of bits per value with minor accuracy degradation on ResNet-34 and MobileNetV2. We analyze the performance of our approach on a variety of CNN architectures and demonstrate that FPGA implementation of ResNet-18 with our approach results in a reduction of around 40% in the memory energy footprint, compared to quantized network, with negligible impact on accuracy. When allowing accuracy degradation of up to 2%, the reduction of 60% is achieved. A reference implementation accompanies the paper.}, author = {Chmiel, Brian and Baskin, Chaim and Zheltonozhskii, Evgenii and Banner, Ron and Yermolin, Yevgeny and Karbachevsky, Alex and Bronstein, Alexander and Mendelson, Avi}, booktitle = {2020 International Joint Conference on Neural Networks (IJCNN)}, isbn = {9781728169279}, issn = {2161-4407}, location = {Glasgow, United Kingdom}, publisher = {IEEE}, title = {{Feature map transform coding for energy-efficient CNN inference}}, doi = {10.1109/ijcnn48605.2020.9206968}, year = {2020}, } @inproceedings{18249, abstract = {Magnetic Resonance Imaging (MRI) is considered today the golden-standard modality for soft tissues. The long acquisition times, however, make it more prone to motion artifacts as well as contribute to the relative high costs of this examination. Over the years, multiple studies concentrated on designing reduced measurement schemes and image reconstruction schemes for MRI, however these problems have been so far addressed separately. On the other hand, recent works in optical computational imaging have demonstrated growing success of simultaneous learning-based design of the acquisition and reconstruction schemes manifesting significant improvement in the reconstruction quality with a constrained time budget. Inspired by these successes, in this work, we propose to learn accelerated MR acquisition schemes (in the form of Cartesian trajectories) jointly with the image reconstruction operator. To this end, we propose an algorithm for training the combined acquisition-reconstruction pipeline end-to-end in a differentiable way. We demonstrate the significance of using the learned Cartesian trajectories at different speed up rates. Code available at https://github.com/tomer196/fastMRI-Cartesian.}, author = {Weiss, Tomer and Vedula, Sanketh and Senouf, Ortal and Michailovich, Oleg and Zibulevsky, Michael and Bronstein, Alexander}, booktitle = {ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, isbn = {9781509066322}, issn = {2379-190X}, location = {Barcelona, Spain}, publisher = {IEEE}, title = {{Joint learning of cartesian undersampling and reconstruction for accelerated MRI}}, doi = {10.1109/icassp40776.2020.9054542}, year = {2020}, } @article{18250, abstract = {Many shape analysis methods treat the geometry of an object as a metric space that can be captured by the Laplace-Beltrami operator. In this paper, we propose to adapt the classical Hamiltonian operator from quantum mechanics to the field of shape analysis. To this end, we study the addition of a potential function to the Laplacian as a generator for dual spaces in which shape processing is performed. We present general optimization approaches for solving variational problems involving the basis defined by the Hamiltonian using perturbation theory for its eigenvectors. The suggested operator is shown to produce better functional spaces to operate with, as demonstrated on different shape analysis tasks.}, author = {Choukroun, Yoni and Shtern, Alon and Bronstein, Alexander and Kimmel, Ron}, issn = {2160-9306}, journal = {IEEE Transactions on Visualization and Computer Graphics}, number = {2}, pages = {1320--1331}, publisher = {Institute of Electrical and Electronics Engineers}, title = {{Hamiltonian operator for spectral shape analysis}}, doi = {10.1109/tvcg.2018.2867513}, volume = {26}, year = {2020}, } @article{18253, abstract = {PCNA, the ring that encircles DNA maintaining the processivity of DNA polymerases, is modified by ubiquitin and SUMO. Whereas ubiquitin is required for bypassing lesions through the DNA damage tolerance (DDT) pathways, we show here that SUMOylation represses another pathway, salvage recombination. The Srs2 helicase is recruited to SUMOylated PCNA and prevents the salvage pathway from acting. The pathway can be induced by overexpressing the PCNA unloader Elg1, or the homologous recombination protein Rad52. Our results underscore the role of PCNA modifications in controlling the various bypass and DNA repair mechanisms.}, author = {Arbel, Matan and Bronstein, Alexander and Sau, Soumitra and Liefshitz, Batia and Kupiec, Martin}, issn = {2150-7511}, journal = {mBio}, number = {3}, publisher = {American Society for Microbiology}, title = {{Access to PCNA by Srs2 and Elg1 controls the choice between alternative repair pathways in Saccharomyces cerevisiae}}, doi = {10.1128/mbio.00705-20}, volume = {11}, year = {2020}, } @inproceedings{18255, abstract = {Learning an object detection or retrieval system requires a large data set with manual annotations. Such data sets are expensive and time consuming to create and therefore difficult to obtain on a large scale. In this work, we propose to exploit the natural correlation in narrations and the visual presence of objects in video, to learn an object detector and retrieval without any manual labeling involved. We pose the problem as weakly supervised learning with noisy labels, and propose a novel object detection paradigm under these constraints. We handle the background rejection by using contrastive samples and confront the high level of label noise with a new clustering score. Our evaluation is based on a set of 11 manually annotated objects in over 5000 frames. We show comparison to a weakly-supervised approach as baseline and provide a strongly labeled upper bound.}, author = {Amrani, Elad and Ben-Ari, Rami and Hakim, Tal and Bronstein, Alexander}, booktitle = {2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)}, isbn = {9781728150246}, issn = {2473-9944}, location = {Seoul, Korea (South)}, publisher = {IEEE}, title = {{Learning to detect and retrieve objects from unlabeled videos}}, doi = {10.1109/iccvw.2019.00567}, year = {2020}, } @inproceedings{18259, abstract = {Example synthesis is one of the leading methods to tackle the problem of few-shot learning, where only a small number of samples per class are available. However, current synthesis approaches only address the scenario of a single category label per image. In this work, we propose a novel technique for synthesizing samples with multiple labels for the (yet unhandled) multi-label few-shot classification scenario. We propose to combine pairs of given examples in feature space, so that the resulting synthesized feature vectors will correspond to examples whose label sets are obtained through certain set operations on the label sets of the corresponding input pairs. Thus, our method is capable of producing a sample containing the intersection, union or set-difference of labels present in two input samples. As we show, these set operations generalize to labels unseen during training. This enables performing augmentation on examples of novel categories, thus, facilitating multi-label few-shot classifier learning. We conduct numerous experiments showing promising results for the label-set manipulation capabilities of the proposed approach, both directly (using the classification and retrieval metrics), and in the context of performing data augmentation for multi-label few-shot learning. We propose a benchmark for this new and challenging task and show that our method compares favorably to all the common baselines.}, author = {Alfassy, Amit and Karlinsky, Leonid and Aides, Amit and Shtok, Joseph and Harary, Sivan and Feris, Rogerio and Giryes, Raja and Bronstein, Alexander}, booktitle = {2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, isbn = {9781728132945}, issn = {2575-7075}, location = {Long Beach, CA, United States}, publisher = {IEEE}, title = {{Laso: Label-set operations networks for multi-label few-shot learning}}, doi = {10.1109/cvpr.2019.00671}, year = {2020}, } @article{22054, abstract = {We consider the Korteweg–de Vries equation with white noise initial data, posed on the whole real line, and prove the almost sure existence of solutions. Moreover, we show that the solutions obey the group property and follow a white noise law at all times, past or future. As an offshoot of our methods, we also obtain a new proof of the existence of solutions and the invariance of white noise measure in the torus setting.}, author = {Killip, Rowan and Murphy, Jason and Visan, Monica}, issn = {1432-1297}, journal = {Inventiones mathematicae}, number = {1}, pages = {203--282}, publisher = {Springer Nature}, title = {{Invariance of white noise for KdV on the line}}, doi = {10.1007/s00222-020-00964-9}, volume = {222}, year = {2020}, }