40,648 research outputs found
P-Selectivity, Immunity, and the Power of One Bit
We prove that P-sel, the class of all P-selective sets, is EXP-immune, but is
not EXP/1-immune. That is, we prove that some infinite P-selective set has no
infinite EXP-time subset, but we also prove that every infinite P-selective set
has some infinite subset in EXP/1. Informally put, the immunity of P-sel is so
fragile that it is pierced by a single bit of information.
The above claims follow from broader results that we obtain about the
immunity of the P-selective sets. In particular, we prove that for every
recursive function f, P-sel is DTIME(f)-immune. Yet we also prove that P-sel is
not \Pi_2^p/1-immune
Thermodynamic Geometry and Critical Behavior of Black Holes
Based on the observations that there exists an analogy between the
Reissner-Nordstr\"om-anti-de Sitter (RN-AdS) black holes and the van der
Waals-Maxwell liquid-gas system, in which a correspondence of variables is
, we study the Ruppeiner geometry, defined as
Hessian matrix of black hole entropy with respect to the internal energy (not
the mass) of black hole and electric potential (angular velocity), for the RN,
Kerr and RN-AdS black holes. It is found that the geometry is curved and the
scalar curvature goes to negative infinity at the Davies' phase transition
point for the RN and Kerr black holes.
Our result for the RN-AdS black holes is also in good agreement with the one
about phase transition and its critical behavior in the literature.Comment: Revtex, 18 pages including 4 figure
Efficient Ultrasound Image Analysis Models with Sonographer Gaze Assisted Distillation.
Recent automated medical image analysis methods have attained state-of-the-art performance but have relied on memory and compute-intensive deep learning models. Reducing model size without significant loss in performance metrics is crucial for time and memory-efficient automated image-based decision-making. Traditional deep learning based image analysis only uses expert knowledge in the form of manual annotations. Recently, there has been interest in introducing other forms of expert knowledge into deep learning architecture design. This is the approach considered in the paper where we propose to combine ultrasound video with point-of-gaze tracked for expert sonographers as they scan to train memory-efficient ultrasound image analysis models. Specifically we develop teacher-student knowledge transfer models for the exemplar task of frame classification for the fetal abdomen, head, and femur. The best performing memory-efficient models attain performance within 5% of conventional models that are 1000× larger in size
Dielectric properties and lattice dynamics of alpha-PbO2-type TiO2: The role of soft phonon modes in pressure-induced phase transition to baddeleyite-type TiO2
Dielectric tensor and lattice dynamics of alpha-PbO2-type TiO2 have been
investigated using the density functional perturbation theory, with a focus on
responses of the vibrational frequencies to pressure. The calculated Raman
spectra under different pressures are in good agreement with available
experimental results and the symmetry assignments of the Raman peaks of
alpha-PbO2-type TiO2 are given for the first time. In addition, we identified
two anomalously IR-active soft phonon modes, B1u and B3u, respectively, around
200 cm-1 which have not been observed in high pressure experiments. Comparison
of the phonon dispersions at 0 and 10 GPa reveals that softening of phonon
modes also occurs for the zone-boundary modes. The B1u and B3u modes play an
important role in transformation from the alpha-PbO2-type phase to baddeleyite
phase. The significant relaxations of the oxygen atoms from the Ti4 plane in
the Ti2O2Ti2 complex of the baddeleyite phase are directly correlated to the
oxygen displacements along the directions given by the eigenvectors of the soft
B1u and B3u modes in the alpha-PbO2-type phase.Comment: 8 pages, 9 figure
The impact of service quality and customer satisfaction on customer's loyalty in Jordan Islamic Bank
Parameterized Algorithms for Graph Partitioning Problems
We study a broad class of graph partitioning problems, where each problem is
specified by a graph , and parameters and . We seek a subset
of size , such that is at most
(or at least) , where are constants
defining the problem, and are the cardinalities of the edge sets
having both endpoints, and exactly one endpoint, in , respectively. This
class of fixed cardinality graph partitioning problems (FGPP) encompasses Max
-Cut, Min -Vertex Cover, -Densest Subgraph, and -Sparsest
Subgraph.
Our main result is an algorithm for any problem in
this class, where is the maximum degree in the input graph.
This resolves an open question posed by Bonnet et al. [IPEC 2013]. We obtain
faster algorithms for certain subclasses of FGPPs, parameterized by , or by
. In particular, we give an time algorithm for Max
-Cut, thus improving significantly the best known time
algorithm
Testable two-loop radiative neutrino mass model based on an effective operator
A new two-loop radiative Majorana neutrino mass model is constructed from the
gauge-invariant effective operator that violates lepton number conservation by two units. The
ultraviolet completion features two scalar leptoquark flavors and a color-octet
Majorana fermion. We show that there exists a region of parameter space where
the neutrino oscillation data can be fitted while simultaneously meeting
flavor-violation and collider bounds. The model is testable through lepton
flavor-violating processes such as , , and
conversion, as well as collider searches for the scalar
leptoquarks and color-octet fermion. We computed and compiled a list of
necessary Passarino-Veltman integrals up to boxes in the approximation of
vanishing external momenta and made them available as a Mathematica package,
denoted as ANT.Comment: 42 pages, 11 figures, typo in Eq. (4.9) as well as wrong chirality
structures in Secs. 4.5 and 5.2 corrected, final results unchange
Long-Term Human Video Generation of Multiple Futures Using Poses
Predicting future human behavior from an input human video is a useful task
for applications such as autonomous driving and robotics. While most previous
works predict a single future, multiple futures with different behavior can
potentially occur. Moreover, if the predicted future is too short (e.g., less
than one second), it may not be fully usable by a human or other systems. In
this paper, we propose a novel method for future human pose prediction capable
of predicting multiple long-term futures. This makes the predictions more
suitable for real applications. Also, from the input video and the predicted
human behavior, we generate future videos. First, from an input human video, we
generate sequences of future human poses (i.e., the image coordinates of their
body-joints) via adversarial learning. Adversarial learning suffers from mode
collapse, which makes it difficult to generate a variety of multiple poses. We
solve this problem by utilizing two additional inputs to the generator to make
the outputs diverse, namely, a latent code (to reflect various behaviors) and
an attraction point (to reflect various trajectories). In addition, we generate
long-term future human poses using a novel approach based on unidimensional
convolutional neural networks. Last, we generate an output video based on the
generated poses for visualization. We evaluate the generated future poses and
videos using three criteria (i.e., realism, diversity and accuracy), and show
that our proposed method outperforms other state-of-the-art works
Entanglement measurement based on two-particle interference
We propose a simple and realizable method using a two-particle interferometer
for the experimental measurement of pairwise entanglement, assuming some prior
knowledge about the quantum state. The basic idea is that the properties of the
density matrix can be revealed by the single- and two-particle interference
patterns. The scheme can easily be implemented with polarized entangled
photons.Comment: 5 pages, 1 figur
Thermodynamic of the Ghost Dark Energy Universe
Recently, the vacuum energy of the QCD ghost in a time-dependent background
is proposed as a kind of dark energy candidate to explain the acceleration of
the Universe. In this model, the energy density of the dark energy is
proportional to the Hubble parameter , which is the Hawking temperature on
the Hubble horizon of the Friedmann-Robertson-Walker (FRW) Universe. In this
paper, we generalized this model and choice the Hawking temperature on the
so-called trapping horizon, which will coincides with the Hubble temperature in
the context of flat FRW Universe dominated by the dark energy component. We
study the thermodynamics of Universe with this kind of dark energy and find
that the entropy-area relation is modified, namely, there is an another new
term besides the area term.Comment: 8 pages, no figure
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