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 $(\phi, q) \leftrightarrow (V,P)$, 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

Parameterized Algorithms for Graph Partitioning Problems

We study a broad class of graph partitioning problems, where each problem is specified by a graph $G=(V,E)$, and parameters $k$ and $p$. We seek a subset $U\subseteq V$ of size $k$, such that $\alpha_1m_1 + \alpha_2m_2$ is at most (or at least) $p$, where $\alpha_1,\alpha_2\in\mathbb{R}$ are constants defining the problem, and $m_1, m_2$ are the cardinalities of the edge sets having both endpoints, and exactly one endpoint, in $U$, respectively. This class of fixed cardinality graph partitioning problems (FGPP) encompasses Max $(k,n-k)$-Cut, Min $k$-Vertex Cover, $k$-Densest Subgraph, and $k$-Sparsest Subgraph. Our main result is an $O^*(4^{k+o(k)}\Delta^k)$ algorithm for any problem in this class, where $\Delta \geq 1$ 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 $p$, or by $(k+p)$. In particular, we give an $O^*(4^{p+o(p)})$ time algorithm for Max $(k,n-k)$-Cut, thus improving significantly the best known $O^*(p^p)$ time algorithm

Testable two-loop radiative neutrino mass model based on an LLQdcQdcLLQd^cQd^c effective operator

A new two-loop radiative Majorana neutrino mass model is constructed from the gauge-invariant effective operator $L^i L^j Q^k d^c Q^l d^c \epsilon_{ik} \epsilon_{jl}$ 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 ${\mu} \to e{\gamma}$, $\mu \to eee$, and $\mu N \to eN$ 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 $H$, 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