Spin models of markets inspired by physics models of magnetism, as the Ising model, allow for the study of the collective dynamics of interacting agents in a market. The number of possible states has been mostly limited to two (buy or sell) or three options. However, herding effects of competing stocks and the collective dynamics of a whole market may escape our reach in the simplest models. Here I study a q-spin Potts model version of a simple Ising market model to represent the dynamics of a stock market index in a spin model. As a result, a self-organized gain-loss asymmetry in the time series of an index variable composed of stocks in this market is observed.

After an overview of the modular analysis and reconstruction framework Marlin an introduction on the functionality of the Marlin-based reconstruction package MarlinReco is given. This package includes a full set of modules for event reconstruction based on the Particle Flow approach. The status of the software is reviewed and recent results using this software package for event reconstruction are presented.

A large volume TPC is a leading candidate for the central tracking detector at a future high energy linear collider. To improve the resolution a new readout based on micro-pattern gas detectors is being developed. Measurements of the spatial resolution of cosmic-ray tracks in a GEM TPC are presented. We find that the resolution suffers if the readout pads are too wide with respect to the charge distribution at the readout plane due to insufficient charge sharing. For narrow pads of 2 x 6 mm**2 we measure a resolution of 100 micometer at short drift distances in the absence of an axial magnetic field. The dependence of the spatial resolution as a function of drift distance allows the determination of the underlying electron statistics. Our results show that the present technique uses about half the statistical power available from the number of primary electrons. The track angle effect is observed as expected.

Portfolio traders strive to identify dynamic portfolio allocation schemes so that their total budgets are efficiently allocated through the investment horizon. This study proposes a novel portfolio trading strategy in which an intelligent agent is trained to identify an optimal trading action by using deep Q-learning. We formulate a Markov decision process model for the portfolio trading process, and the model adopts a discrete combinatorial action space, determining the trading direction at prespecified trading size for each asset, to ensure practical applicability. Our novel portfolio trading strategy takes advantage of three features to outperform in real-world trading. First, a mapping function is devised to handle and transform an initially found but infeasible action into a feasible action closest to the originally proposed ideal action. Second, by overcoming the dimensionality problem, this study establishes models of agent and Q-network for deriving a multi-asset trading strategy in the predefined action space. Last, this study introduces a technique that has the advantage of deriving a well-fitted multi-asset trading strategy by designing an agent to simulate all feasible actions in each state. To validate our approach, we conduct backtests for two representative portfolios and demonstrate superior results over the benchmark strategies.

The detrending moving average (DMA) algorithm is a widely used technique to quantify the long-term correlations of non-stationary time series and the long-range correlations of fractal surfaces, which contains a parameter $θ$ determining the position of the detrending window. We develop multifractal detrending moving average (MFDMA) algorithms for the analysis of one-dimensional multifractal measures and higher-dimensional multifractals, which is a generalization of the DMA method. The performance of the one-dimensional and two-dimensional MFDMA methods is investigated using synthetic multifractal measures with analytical solutions for backward ($θ=0$), centered ($θ=0.5$), and forward ($θ=1$) detrending windows. We find that the estimated multifractal scaling exponent $τ(q)$ and the singularity spectrum $f(α)$ are in good agreement with the theoretical values. In addition, the backward MFDMA method has the best performance, which provides the most accurate estimates of the scaling exponents with lowest error bars, while the centered MFDMA method has the worse performance. It is found that the backward MFDMA algorithm also outperforms the multifractal detrended fluctuation analysis (MFDFA). The one-dimensional backward MFDMA method is applied to analyzing the time series of Shanghai Stock Exchange Composite Index and its multifractal nature is confirmed.

This paper studies an infinite horizon optimal tracking portfolio problem using capital injection in incomplete market models. The benchmark process is modelled by a geometric Brownian motion with zero drift driven by some unhedgeable risk. The relaxed tracking formulation is adopted where the fund account compensated by the injected capital needs to outperform the benchmark process at any time, and the goal is to minimize the cost of the discounted total capital injection. When model parameters are known, we formulate the equivalent auxiliary control problem with reflected state dynamics, for which the classical solution of the HJB equation with Neumann boundary condition is obtained explicitly. When model parameters are unknown, we introduce the exploratory formulation for the auxiliary control problem with entropy regularization and develop the continuous-time q-learning algorithm in models of reflected diffusion processes. In some illustrative numerical example, we show the satisfactory performance of the q-learning algorithm.

Translocation of a single stranded DNA through genetically engineered $α$-hemolysin channels with positively charged walls is studied. It is predicted that transport properties of such channels are dramatically different from neutral wild type $α$-hemolysin channel. We assume that the wall charges compensate the fraction $x$ of the bare charge $q_{b}$ of the DNA piece residing in the channel. Our prediction are as follows (i) At small concentration of salt the blocked ion current decreases with $x$. (ii) The effective charge $q$ of DNA piece, which is very small at $x = 0$ (neutral channel) grows with $x$ and at $x=1$ reaches $q_{b}$. (iii) The rate of DNA capture by the channel exponentially grows with $x$. Our theory is also applicable to translocation of a double stranded DNA in narrow solid state nanopores with positively charged walls.

We study the ultrasensitivity of multisite binding processes where ligand molecules can bind to several binding sites, considering more particularly recent models involving complex chemical reactions in phosphorylation systems such as allosteric phosphorylation processes, or substrate-catalyst chain reactions and nucleosome mediated cooperativity. New statistics based formulas for the Hill coefficient and the effective Hill coefficient are provided and necessary conditions for a system to be ultrasensitive are exhibited. We then assume that the binding process is described by a density dependent birth and death process. We provide precise large deviation results for the steady state distribution of the process, and show that switch-like ultrasensitive responses are strongly related to the multi-stability of the associated dynamical system. Ultrasensitivity occurs if and only if the entropy of the dynamical system has more than one global minimum for some critical ligand concentration. In this case, the Hill coefficient is proportional to the number of binding sites, and the systems is highly ultrasensitive. We also discuss the interpretation of an extension $I_q$ of the effective Hill coefficient $I_{0.9}$ for which we recommend the computation of a broad range of values of $q$ instead of just the standard one corresponding to the 10% to 90% variation in the dose-response. It is shown that this single choice can sometimes mislead the conclusion by not detecting ultrasensitivity. This new approach allows a better understanding of multisite ultrasensitive systems and provides new tools for the design of such systems.

In 1968, Sorin Comorosan first reported a phenomenon wherein irradiation of the substrate of an enzyme reaction, in the crystalline state, for a specific number of seconds could lead to an enhanced aqueous solution reaction rate for the enzyme(up to 30%). Dependence on crystal irradiation time was found to be oscillatory with a fixed period. The basis for this unusual phenomenon has remained a mystery. Previously unreported experimental results are presented which demonstrate, for the LDH / pyruvate reaction, that the identity of the crystalline material irradiated is, largely, inconsequential. It is proposed here that the irradiation procedure drives oscillatory reactions involving atmospheric gases adsorbed on the crystals and that these photoproducts, or related dark-reaction species, when dissolved, function as enzyme cofactors.

We show that the choice of the sign of the hopping matrix in our impurity band model for disordered III-V diluted magnetic semiconductors [PRL 87, 107293 (2000); cond-mat/0111045] is justified: with this choice, the impurity band is placed inside the gap and it has a mobility edge, as expected for a disordered system. The other sign choice, suggested in cond-mat/0111504, leads to an unphysical description of the occupied states of the impurity band (extremely long tail, no mobility edge, no bulk ferromagnetism).