非线性物理破解神经编码机制

信息在神经系统中以脉冲点序列的形式传输和处理，神经系统如何通过脉冲点序列对所表达的信息进行编码，一直是一个谜．人们曾普遍认为，神经可能通过发放率（单位时间发放脉冲出现的次数）对信息编码，也有人猜测，神经可通过脉冲点序列的时序编码．由于神经发放的随机性，这就使得任何编码机制都面临着被表达信息的确定性与表达该信息的信号的随机性的矛盾．通过神经非线性随机动力学模型，文章作者发现，神经点序列的发放率对点序列的时序信息传输的影响，揭示了神经点序列时序信息在神经非线性传输中的随机共振特征．由此预期，并进一步通过听觉心理物理实验观察到，在一定条件下噪声对听觉的增强作用．从而通过非线性物理揭示了听觉时序编码机制的存在以及在时序编码中随机性噪声的积极作用．     

一种基于OTF稳定性的波前编码相位板优化方法

提出了一种简单的可用于光学系统中子午弧矢方向白光OTF稳定性评价的波前编码相位板参量优化方法.该方法仅以标准偏差来评价OTF在目标景深范围内的稳定性,并且结合自适应模拟退火算法在参量空间内优化得到相位板的最佳参量.使用该方法优化得到的相位板参量,可以大幅度提高光学系统的景深,并且可以获得更为清晰、稳定的成像.对应用波前编码技术前后光学系统的成像性能作了比较,并且考察了优化参量的容差性.     

A novel super-FEC code based on concatenated code for high-speed long-haul optical communication systems

The structures of the novel super forward error correction (Super-FEC) code type based on the concatenated code for high-speed long-haul optical communication systems are studied in this paper. The Reed-Solomon (RS) (255, 239) + Bose-Chaudhuri-Hocguenghem (BCH) (1023, 963) concatenated code is presented after the characteristics of the concatenated code and the two Super-FEC code type presented in ITU-T G.975.1 have theoretically been analyzed, the simulation result shows that this novel code type, compared with the RS (255, 239) + convolutional-self-orthogonal-code (CSOC) (k₀/n₀ = 6/7, J = 8) code in ITU-T G.975.1, has a lower redundancy and better error-correction capabilities, and its net coding gain (NCG) at the third iteration is 0.57 dB more than that of RS (255, 239) + CSOC (k₀/n₀ = 6/7, J = 8) code in ITU-T G.975.1 at the third iteration for the bit error rate (BER) of 10⁻¹². Therefore, the novel code type can better be used in long-haul, larger capacity and higher bit-rate optical communication systems. Furthermore, the design and implementation of the novel concatenated code type are also discussed.     

波前编码系统成像特性的空间域分析

波前编码技术在大幅度地增加光学系统景深的同时,可以抑制各种离焦类型的像差。目前波前编码系统的成像特性分析大多是基于空间频率域的分析,在应用稳定相法导出三次型相位板波前编码系统的点扩展函数近似解析式的基础之上,描述了点扩展函数的边界、带宽与振荡等特性,分析了波前编码系统对离焦、像散、彗差等常见像差的敏感性,从空间域进一步分析了波前编码系统的成像特性。     

A coding method for a sequent calculus of propositional logic

The paper deals with a coding method for a sequent calculus of the propositional logic. The method is based on the sequent calculus. It allows us to determine if a formula is derivable in the calculus without constructing a derivation tree. The main advantage of the coding method is its compactness in comparison with derivation trees of the sequent calculus. The coding method can be used as a decision procedure for the propositional logic.     

Quantum Superdense Coding Based on Coherent States in Cavity QED

A physical scheme for the implementation of quantum superdense coding has been proposed in Cavity QED. The detuned interaction between Λ-type three-level atoms and coherent fields constitute the main superdense coding process. The quantum superdense coding can be realized in an easier way, and the atoms are not excited during the whole process, so the effect of atomic decay is eliminated naturally.     

Multi-Class Cost-Constrained Random Coding for Correlated Sources over the Multiple-Access Channel

This paper studies a generalized version of multi-class cost-constrained random-coding ensemble with multiple auxiliary costs for the transmission of N correlated sources over an N-user multiple-access channel. For each user, the set of messages is partitioned into classes and codebooks are generated according to a distribution depending on the class index of the source message and under the constraint that the codewords satisfy a set of cost functions. Proper choices of the cost functions recover different coding schemes including message-dependent and message-independent versions of independent and identically distributed, independent conditionally distributed, constant-composition and conditional constant composition ensembles. The transmissibility region of the scheme is related to the Cover-El Gamal-Salehi region. A related family of correlated-source Gallager source exponent functions is also studied. The achievable exponents are compared for correlated and independent sources, both numerically and analytically.     

Trade-offs between Error Exponents and Excess-Rate Exponents of Typical Slepian–Wolf Codes

Typical random codes (TRCs) in a communication scenario of source coding with side information in the decoder is the main subject of this work. We study the semi-deterministic code ensemble, which is a certain variant of the ordinary random binning code ensemble. In this code ensemble, the relatively small type classes of the source are deterministically partitioned into the available bins in a one-to-one manner. As a consequence, the error probability decreases dramatically. The random binning error exponent and the error exponent of the TRCs are derived and proved to be equal to one another in a few important special cases. We show that the performance under optimal decoding can be attained also by certain universal decoders, e.g., the stochastic likelihood decoder with an empirical entropy metric. Moreover, we discuss the trade-offs between the error exponent and the excess-rate exponent for the typical random semi-deterministic code and characterize its optimal rate function. We show that for any pair of correlated information sources, both error and excess-rate probabilities exponential vanish when the blocklength tends to infinity.     

Discovering Higher-Order Interactions Through Neural Information Decomposition

If regularity in data takes the form of higher-order functions among groups of variables, models which are biased towards lower-order functions may easily mistake the data for noise. To distinguish whether this is the case, one must be able to quantify the contribution of different orders of dependence to the total information. Recent work in information theory attempts to do this through measures of multivariate mutual information (MMI) and information decomposition (ID). Despite substantial theoretical progress, practical issues related to tractability and learnability of higher-order functions are still largely unaddressed. In this work, we introduce a new approach to information decomposition—termed Neural Information Decomposition (NID)—which is both theoretically grounded, and can be efficiently estimated in practice using neural networks. We show on synthetic data that NID can learn to distinguish higher-order functions from noise, while many unsupervised probability models cannot. Additionally, we demonstrate the usefulness of this framework as a tool for exploring biological and artificial neural networks.     

基于五离子最大纠缠态的优化隐形传态和超密集编码

我们提出了一个制备五离子最大纠缠态的方案,并利用该纠缠态实现任意两量子比特的隐形传送和超密集编码。超密集编码的信息容量满足Holevo bound,这意味着通过发送三个量子比特可以传送五个经典比特的信息。