Rainbow independent sets in graphs with maximum degree two

Given a graph $G$, let $f_{G}(n,m)$ be the minimal number $k$ such that every $k$ independent $n$-sets in $G$ have a rainbow $m$-set. Let $mathcal{D}(2)$ be the family of all graphs with maximum degree at most two. Aharoni et al. (2019) conjectured that (i) $f_G(n,n-1)=n-1$ for all graphs $Ginmathcal{D}(2)$ and (ii) $f_{C_t}(n,n)=n$ for $tge 2n+1$. Lv and Lu (2020) showed that the conjecture (ii) holds when $t=2n+1$. In this article, we show that the conjecture (ii) holds for $tgefrac{1}{3}n^2+frac{44}{9}n$. Let $C_t$ be a cycle of length $t$ with vertices being arranged in a clockwise order. An ordered set $I=(a_1,a_2,ldots,a_n)$ on $C_t$ is called a $2$-jump independent $n$-set of $C_t$ if $a_{i+1}-a_i=2pmod{t}$ for any $1le ile n-1$. We also show that a collection of 2-jump independent $n$-sets $mathcal{F}$ of $C_t$ with $|mathcal{F}|=n$ admits a rainbow independent $n$-set, i.e. (ii) holds if we restrict $mathcal{F}$ on the family of 2-jump independent $n$-sets. Moreover, we prove that if the conjecture (ii) holds, then (i) holds for all graphs $Ginmathcal{D}(2)$ with $c_e(G)le 4$, where $c_e(G)$ is the number of components of $G$ isomorphic to cycles of even lengths.

Intrinsic one-dimensional conducting channels in the Kondo insulator SmB6

Since its discovery as a Kondo insulator 50 years ago, SmB6 recently received a revival of interest due to detection of unexpected quantum oscillations in the insulating state, discovery of disorder-immune bulk transport, and proposals of correlation-driven topological physics. While recent transport results attribute the anomalous low temperature conduction to two-dimensional surface states, important alternatives, such as conduction channel residing in one-dimensional dislocation lines, have not been adequately explored. Here we study SmB6 with scanning microwave impedance microscopy and uncover evidence for conducting one-dimensional states terminating at surface step edges. These states remain conducting up to room temperature, indicating unusual robustness against scattering and an unconventional origin. Our results bring to light a heretofore undetected conduction route in SmB6 that contributes to the low temperature transport. The unique scenario of intrinsic one-dimensional conducting channels in a highly insulating correlated bulk offers a one-dimensional platform that may host exotic physics.

Multi-Density Attention Network for Loop Filtering in Video Compression

Video compression is a basic requirement for consumer and professional video applications alike. Video coding standards such as H.264/AVC and H.265/HEVC are widely deployed in the market to enable efficient use of bandwidth and storage for many video applications. To reduce the coding artifacts and improve the compression efficiency, neural network based loop filtering of the reconstructed video has been developed in the literature. However, loop filtering is a challenging task due to the variation in video content and sampling densities. In this paper, we propose a on-line scaling based multi-density attention network for loop filtering in video compression. The core of our approach lies in several aspects: (a) parallel multi-resolution convolution streams for extracting multi-density features, (b) single attention branch to learn the sample correlations and generate mask maps, (c) a channel-mutual attention procedure to fuse the data from multiple branches, (d) on-line scaling technique to further optimize the output results of network according to the actual signal. The proposed multi-density attention network learns rich features from multiple sampling densities and performs robustly on video content of different resolutions. Moreover, the online scaling process enhances the signal adaptability of the off-line pre-trained model. Experimental results show that 10.18% bit-rate reduction at the same video quality can be achieved over the latest Versatile Video Coding (VVC) standard. The objective performance of the proposed algorithm outperforms the state-of-the-art methods and the subjective quality improvement is obvious in terms of detail preservation and artifact alleviation.

Non-Blockingness Verification of Bounded Petri Nets Using Basis Reachability Graphs -- An Extended Version With Benchmarks

In this paper, we study the problem of non-blockingness verification by tapping into the basis reachability graph (BRG). Non-blockingness is a property that ensures that all pre-specified tasks can be completed, which is a mandatory requirement during the system design stage. In this paper we develop a condition of transition partition of a given net such that the corresponding conflict-increase BRG contains sufficient information on verifying non-blockingness of its corresponding Petri net. Thanks to the compactness of the BRG, our approach possesses practical efficiency since the exhaustive enumeration of the state space can be avoided. In particular, our method does not require that the net is deadlock-free.

Quantum persistent tennis racket dynamics of nanorotors

Classical rotations of asymmetric rigid bodies are unstable around the axis of intermediate momentof inertia, causing a flipping of rotor orientation. This effect, known as the tennis racket effect,quickly averages to zero in classical ensembles since the flipping period varies significantly uponapproaching the separatrix. Here, we explore the quantum rotations of rapidly spinning thermalasymmetric nanorotors and show that classically forbidden tunnelling gives rise to persistent tennisracket dynamics, in stark contrast to the classical expectation. We characterise this effect, demon-strating that quantum coherent flipping dynamics can persist even in the regime where millions ofangular momentum states are occupied. This persistent flipping offers a promising route for observ-ing and exploiting quantum effects in rotational degrees of freedom for molecules and nanoparticles.

Towards Privacy-aware Task Allocation in Social Sensing based Edge Computing Systems

With the advance in mobile computing, Internet of Things, and ubiquitous wireless connectivity, social sensing based edge computing (SSEC) has emerged as a new computation paradigm where people and their personally owned devices collect sensor measurements from the physical world and process them at the edge of the network. This paper focuses on a privacy-aware task allocation problem where the goal is to optimize the computation task allocation in SSEC systems while respecting the users customized privacy settings. It introduces a novel Game-theoretic Privacy-aware Task Allocation (G-PATA) framework to achieve the goal. G-PATA includes (i) a bottom-up game-theoretic model to generate the maximum payoffs at end devices while satisfying the end users privacy settings; (ii) a top-down incentive scheme to adjust the rewards for the tasks to ensure that the task allocation decisions made by end devices meet the Quality of Service (QoS) requirements of the applications. Furthermore, the framework incorporates an efficient load balancing and iteration reduction component to adapt to the dynamic changes in status and privacy configurations of end devices. The G-PATA framework was implemented on a real-world edge computing platform that consists of heterogeneous end devices (Jetson TX1 and TK1 boards, and Raspberry Pi3). We compare G-PATA with state-of-the-art task allocation schemes through two real-world social sensing applications. The results show that G-PATA significantly outperforms existing approaches under various privacy settings (our scheme achieved as much as 47% improvements in delay reduction for the application and 15% more payoffs for end devices compared to the baselines.).

Verification of Nonblockingness in Bounded Petri Nets With Minimax Basis Reachability Graphs

This paper proposes a semi-structural approach to verify the nonblockingness of a Petri net. We construct a structure, called minimax basis reachability graph (minimax-BRG): it provides an abstract description of the reachability set of a net while preserving all information needed to test if the net is blocking. We prove that a bounded deadlock-free Petri net is nonblocking if and only if its minimax-BRG is unobstructed, which can be verified by solving a set of integer constraints and then examining the minimax-BRG. For Petri nets that are not deadlock-free, one needs to determine the set of deadlock markings. This can be done with an approach based on the computation of maximal implicit firing sequences enabled by the markings in the minimax-BRG. The approach we developed does not require the construction of the reachability graph and has wide applicability.

Phase-field modelling of the effect of density change on solidification revisited: Model development and analytical solutions for single component materials

In this paper the development of a physically consistent phase-field theory of solidification shrinkage is presented. The coarse-grained hydrodynamic equations are derived directly from the N-body Hamiltonian equations in the framework of statistical physics, while the constitutive relations are developed in the framework of the standard Phase-field Theory, by following the variational formalism and the principles of non-equilibrium thermodynamics. To enhance the numerical practicality of the model, quasi-incompressible hydrodynamic equations are derived, where sound waves are absent (but density change is still possible), and therefore the time scale of solidification is accessible in numerical simulations. The model development is followed by a comprehensive mathematical analysis of the equilibrium and propagating 1-dimensional solid-liquid interfaces for different density-phase couplings. It is shown, that the fluid flow decelerates/accelerates the solidification front in case of shrinkage/expansion of the solid compared to the case when no density contrast is present between the phases. Furthermore, such a free energy construction is proposed, in which the equilibrium planar phase-field interface is independent from the density-phase coupling, and the equilibrium interface represents an exact propagating planar interface solution of the quasi-incompressible hydrodynamic equations. Our results are in excellent agreement with previous theoretical predictions.

Cascaded LSTMs based Deep Reinforcement Learning for Goal-driven Dialogue

This paper proposes a deep neural network model for joint modeling Natural Language Understanding (NLU) and Dialogue Management (DM) in goal-driven dialogue systems. There are three parts in this model. A Long Short-Term Memory (LSTM) at the bottom of the network encodes utterances in each dialogue turn into a turn embedding. Dialogue embeddings are learned by a LSTM at the middle of the network, and updated by the feeding of all turn embeddings. The top part is a forward Deep Neural Network which converts dialogue embeddings into the Q-values of different dialogue actions. The cascaded LSTMs based reinforcement learning network is jointly optimized by making use of the rewards received at each dialogue turn as the only supervision information. There is no explicit NLU and dialogue states in the network. Experimental results show that our model outperforms both traditional Markov Decision Process (MDP) model and single LSTM with Deep Q-Network on meeting room booking tasks. Visualization of dialogue embeddings illustrates that the model can learn the representation of dialogue states.

3-Lie algebra $A_{omega}^{delta}$-modules and induced modules

In this paper, we define the induced modules of Lie algebra ad$(B)$ associated with a 3-Lie algebra $B$-module, and study the relation between 3-Lie algebra $A_{omega}^{delta}$-modules and induced modules of inner derivation algebra ad$(A_{omega}^{delta})$. We construct two infinite dimensional intermediate series modules of 3-Lie algebra $A_{omega}^{delta}$, and two infinite dimensional modules $(V, psi_{lambdamu})$ and $(V, phi_{mu})$ of the Lie algebra ad$(A_{omega}^{delta})$, and prove that only $(V, psi_{lambda0})$ and $(V, psi_{lambda1})$ are induced modules.