A new problem in string searching

We describe a substring search problem that arises in group presentation simplification processes. We suggest a two-level searching model: skip and match levels. We give two timestamp algorithms which skip searching parts of the text where there are no matches at all and prove their correctness. At the match level, we consider Harrison signature, Karp-Rabin fingerprint, Bloom filter and automata based matching algorithms and present experimental performance figures.Comment: To appear in Proceedings Fifth Annual International Symposium on Algorithms and Computation (ISAAC'94), Lecture Notes in Computer Scienc

Does human imitate successful behaviors immediately?

The emergence and abundance of cooperation in animal and human societies is a challenging puzzle to evolutionary biology. Over the past decades, various mechanisms have been suggested which are capable of supporting cooperation. Imitation dynamics, however, are the most representative microscopic rules of human behaviors on studying these mechanisms. Their standard procedure is to choose the agent to imitate at random from the population. In the spatial version this means a random agent from the neighborhood. Hence, imitation rules do not include the possibility to explore the available strategies, and then they have the possibility to reach a homogeneous state rapidly when the population size is small. To prevent evolution stopping, theorists allow for random mutations in addition to the imitation dynamics. Consequently, if the microscopic rules involve both imitation and mutation, the frequency of agents switching to the more successful strategy must be higher than that of them transiting to the same target strategy via mutation dynamics. Here we show experimentally that the frequency of switching to successful strategy approximates to that of mutating to the same strategy. This suggests that imitation might play an insignificant role on the behaviors of human decision making. In addition, our experiments show that the probabilities of agents mutating to different target strategies are significantly distinct. The actual mutation theories cannot give us an appropriate explanation to the experimental results. Hence, we argue that the mutation dynamics might have evolved for other reasons

Could the 21-cm absorption be explained by the dark matter suggested by 8^8Be transitions?

The stronger than expected 21-cm absorption was observed by EDGES recently, and another anomaly of $^8$Be transitions would be signatures of new interactions. These two issues may be related to each other, e.g., pseudoscalar $A$ mediated fermionic millicharged dark matter (DM), and the 21-cm absorption could be induced by photon mediated scattering between MeV millicharged DM and hydrogen. This will be explored in this paper. For fermionic millicharged DM $\bar{\chi} \chi$ with masses in a range of $2 m_A < 2 m_{\chi} < 3 m_A$, the p-wave annihilation $\bar{\chi} \chi \to A A$ would be dominant during DM freeze-out. The s-wave annihilation $\bar{\chi} \chi$ $\to A, \gamma$ $\to e^+ e^-$ is tolerant by constraints from CMB and the 21-cm absorption. The millicharged DM can evade constraints from direct detection experiments. The process of $K^+ \to \pi^+ \pi^0$ with the invisible decay $\pi^0 \to \bar{\chi} \chi$ could be employed to search for the millicharged DM, and future high intensity $K^+$ sources, such as NA62, will do the job.Comment: 6 pages, 2 figures, the accepted version, EPJ

Measurement of the linear thermo-optical coefficient of Ga0.51_{0.51}In0.49_{0.49}P using photonic crystal nanocavities

Ga$_{0.51}$In$_{0.49}$P is a promising candidate for thermally tunable nanophotonic devices due to its low thermal conductivity. In this work we study its thermo-optical response. We obtain the linear thermo-optical coefficient $dn/dT=2.0\pm0.3\cdot 10^{-4}\,\rm{K}^{-1}$ by investigating the transmission properties of a single mode-gap photonic crystal nanocavity.Comment: 7 pages, 4 figure

FPGA-Based CNN Inference Accelerator Synthesized from Multi-Threaded C Software

A deep-learning inference accelerator is synthesized from a C-language software program parallelized with Pthreads. The software implementation uses the well-known producer/consumer model with parallel threads interconnected by FIFO queues. The LegUp high-level synthesis (HLS) tool synthesizes threads into parallel FPGA hardware, translating software parallelism into spatial parallelism. A complete system is generated where convolution, pooling and padding are realized in the synthesized accelerator, with remaining tasks executing on an embedded ARM processor. The accelerator incorporates reduced precision, and a novel approach for zero-weight-skipping in convolution. On a mid-sized Intel Arria 10 SoC FPGA, peak performance on VGG-16 is 138 effective GOPS

Shape Memory Effect of Keratin Fibers

In the past thousands of years, keratin fibers were only considered as textile fibers for excellent fiber performances, such as high strength, acceptable elasticity, good thermal insulation, etc. Only recently, some indications have been obtained that keratin fiber may be a smart natural material that may subvert people’s perception of this matter. The smart attribute displays shape memory effects (SMEs) responsive to many types of stimuli including water, heat, coupled water-heat, redox agents, UV light, etc. These smart functions of keratin fibers are found to be the result of three structural components: crystals, hydrogen bonds (HBs), and disulfide bonds (DBs) among intra- and inter-keratin macromolecules. In this chapter, keratin fibers (such as camel hair) were employed for investigating their SMEs under five types of stimuli, in which the HBs, DBs, and crystals were characterized separately, as well as the fiber shape fixation and recovery ratios, respectively. The whole test results indicated that keratin hair fiber is a type of shape memory polymer and the related SME depends on the contents of the HBs, DBs, and crystalline phase inside the hair

Fuzzy PD Control of Networked Control Systems Based on CMAC Neural Network

The network and plant can be regarded as a controlled time-varying system because of the random induced delay in the networked control systems. The cerebellar model articulation controller (CMAC) neural network and a PD controller are combined to achieve the forward feedback control. The PD controller parameters are adjusted adaptively by fuzzy reasoning mechanism, which can optimize the control effect by reducing the uncertainty caused by the network-induced delay. Finally, the simulations show that the control method proposed can improve the performance effectively