2,113 research outputs found
Boundary two-parameter eight-state supersymmetric fermion model and Bethe ansatz solution
The recently introduced two-parameter eight-state
supersymmetric fermion model is extended to include boundary terms. Nine
classes of boundary conditions are constructed, all of which are shown to be
integrable via the graded boundary quantum inverse scattering method. The
boundary systems are solved by using the coordinate Bethe ansatz and the Bethe
ansatz equations are given for all nine cases.Comment: 11 pages, RevTex; some typos correcte
N-(4-Chlorophenyl)-4-(2-oxocyclopentyl)butyramide
In the title compound, C15H18ClNO2, the amide group is coplanar with the chlorophenyl group, making a dihedral angle of 1.71 (12)°. The cyclopentanone ring adopts a twist conformation. A weak intramolecular C—H⋯O hydrogen bond is observed. Molecules are linked into cyclic centrosymmetric dimers by paired N—H⋯O hydrogen bonds
Plateau in Monotonic Linear Interpolation -- A "Biased" View of Loss Landscape for Deep Networks
Monotonic linear interpolation (MLI) - on the line connecting a random
initialization with the minimizer it converges to, the loss and accuracy are
monotonic - is a phenomenon that is commonly observed in the training of neural
networks. Such a phenomenon may seem to suggest that optimization of neural
networks is easy. In this paper, we show that the MLI property is not
necessarily related to the hardness of optimization problems, and empirical
observations on MLI for deep neural networks depend heavily on biases. In
particular, we show that interpolating both weights and biases linearly leads
to very different influences on the final output, and when different classes
have different last-layer biases on a deep network, there will be a long
plateau in both the loss and accuracy interpolation (which existing theory of
MLI cannot explain). We also show how the last-layer biases for different
classes can be different even on a perfectly balanced dataset using a simple
model. Empirically we demonstrate that similar intuitions hold on practical
networks and realistic datasets.Comment: ICLR 202
Understanding Edge-of-Stability Training Dynamics with a Minimalist Example
Recently, researchers observed that gradient descent for deep neural networks
operates in an ``edge-of-stability'' (EoS) regime: the sharpness (maximum
eigenvalue of the Hessian) is often larger than stability threshold 2/
(where is the step size). Despite this, the loss oscillates and
converges in the long run, and the sharpness at the end is just slightly below
. While many other well-understood nonconvex objectives such as matrix
factorization or two-layer networks can also converge despite large sharpness,
there is often a larger gap between sharpness of the endpoint and . In
this paper, we study EoS phenomenon by constructing a simple function that has
the same behavior. We give rigorous analysis for its training dynamics in a
large local region and explain why the final converging point has sharpness
close to . Globally we observe that the training dynamics for our
example has an interesting bifurcating behavior, which was also observed in the
training of neural nets.Comment: 53 pages, 19 figure
Discovery of the transient magnetar 3XMM J185246.6+003317 near supernova remnant Kesteven 79 with XMM-Newton
We report the serendipitous discovery with XMM-Newton that 3XMM
J185246.6+003317 is an 11.56 s X-ray pulsar located 1' away from the southern
boundary of supernova remnant Kes 79. The spin-down rate of 3XMM
J185246.6+003317 is s s, which, together with the
long period P=11.558714(2) s, indicates a dipolar surface magnetic field of
Myr, and a spin-down
luminosity of erg s. The X-ray spectrum of the
source is best-fitted with a resonant Compton scattering model, and can be also
adequately described by a blackbody model. The observations covering a seven
month span from 2008 to 2009 show variations in the spectral properties of the
source, with the luminosity decreasing from erg s to
erg s, along with a decrease of the blackbody
temperature from keV to keV. The X-ray luminosity
of the source is higher than its spin-down luminosity, ruling out rotation as a
power source. The combined timing and spectral properties, the non-detection of
any optical or infrared counterpart, together with the lack of detection of the
source in archival X-ray data prior to the 2008 XMM-Newton observation, point
to this source being a newly discovered transient low-B magnetar undergoing an
outburst decay during the XMM-Newton observations. The non-detection by Chandra
in 2001 sets an upper limit erg s to the quiescent
luminosity of 3XMM J185246.6+003317. Its period is the longest among currently
known transient magnetars. The foreground absorption toward 3XMM
J185246.6+003317 is similar to that of Kes 79, suggesting a similar distance of
7.1 kpc.Comment: 7 pages, 4 figures, 1 table; updated to match the published versio
Impact of high-frequency pumping on anomalous finite-size effects in three-dimensional topological insulators
Lowering of the thickness of a thin-film three-dimensional topological
insulator down to a few nanometers results in the gap opening in the spectrum
of topologically protected two-dimensional surface states. This phenomenon,
which is referred to as the anomalous finite-size effect, originates from
hybridization between the states propagating along the opposite boundaries. In
this work, we consider a bismuth-based topological insulator and show how the
coupling to an intense high-frequency linearly polarized pumping can further be
used to manipulate the value of a gap. We address this effect within recently
proposed Brillouin-Wigner perturbation theory that allows us to map a
time-dependent problem into a stationary one. Our analysis reveals that both
the gap and the components of the group velocity of the surface states can be
tuned in a controllable fashion by adjusting the intensity of the driving field
within an experimentally accessible range and demonstrate the effect of
light-induced band inversion in the spectrum of the surface states for high
enough values of the pump.Comment: 6 pages, 3 figure
A Hyper-pixel-wise Contrastive Learning Augmented Segmentation Network for Old Landslide Detection Using High-Resolution Remote Sensing Images and Digital Elevation Model Data
As a harzard disaster, landslide often brings tremendous losses to humanity,
so it's necessary to achieve reliable detection of landslide. However, the
problems of visual blur and small-sized dataset cause great challenges for old
landslide detection task when using remote sensing data. To reliably extract
semantic features, a hyper-pixel-wise contrastive learning augmented
segmentation network (HPCL-Net) is proposed, which augments the local salient
feature extraction from the boundaries of landslides through HPCL and fuses the
heterogeneous infromation in the semantic space from High-Resolution Remote
Sensing Images and Digital Elevation Model Data data. For full utilization of
the precious samples, a global hyper-pixel-wise sample pair queues-based
contrastive learning method, which includes the construction of global queues
that store hyper-pixel-wise samples and the updating scheme of a momentum
encoder, is developed, reliably enhancing the extraction ability of semantic
features. The proposed HPCL-Net is evaluated on a Loess Plateau old landslide
dataset and experiment results show that the model greatly improves the
reliablity of old landslide detection compared to the previous old landslide
segmentation model, where mIoU metric is increased from 0.620 to 0.651,
Landslide IoU metric is increased from 0.334 to 0.394 and F1-score metric is
increased from 0.501 to 0.565
Dichlorido(η6-toluene)[tris(4-methoxyphenyl)phosphine]ruthenium(II)
In the title compound, [RuCl2(C7H8)(C21H21O3P)], the RuII atom possesses a pseudo-octahedral geometry and the metrical parameters around the metallic core compare well with those of similar three-legged-piano-stool complexes
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