Spin Effects in the Effective Field Theory Approach to Post-Minkowskian Conservative Dynamics

Building upon the worldline effective field theory (EFT) formalism for spinning bodies developed for the Post-Newtonian regime, we generalize the EFT approach to Post-Minkowskian (PM) dynamics to include rotational degrees of freedom in a manifestly covariant framework. We introduce a systematic procedure to compute the total change in momentum and spin in the gravitational scattering of compact objects. For the special case of spins aligned with the orbital angular momentum, we show how to construct the radial action for elliptic-like orbits using the Boundary-to-Bound correspondence. As a paradigmatic example, we solve the scattering problem to next-to-leading PM order with linear and bilinear spin effects and arbitrary initial conditions, incorporating for the first time finite-size corrections. We obtain the aligned-spin radial action from the resulting scattering data, and derive the periastron advance and binding energy for circular orbits. We also provide the (square of the) center-of-mass momentum to ${\cal O}(G^2)$, which may be used to reconstruct a Hamiltonian. Our results are in perfect agreement with the existent literature, while at the same time extend the knowledge of the PM dynamics of compact binaries at quadratic order in spins.Comment: 41 pages. 1 ancillary file (wl format

Piecewise Convex Technique for the Stability Analysis of Delayed Neural Network

On the basis of the fact that the neuron activation function is sector bounded, this paper transforms the researched original delayed neural network into a linear uncertain system. Combined with delay partitioning technique, by using the convex combination between decomposed time delay and positive matrix, this paper constructs a novel Lyapunov function to derive new less conservative stability criteria. The benefit of the method used in this paper is that it can utilize more information on slope of the activations and time delays. To illustrate the effectiveness of the new established stable criteria, one numerical example and an application example are proposed to compare with some recent results

Stochastic seismic response analysis of nonlinear structure with random parameters

In the present paper, a dimension-reduction modeling method is proposed for a dual stochastic dynamic system of non-stationary ground motion stochastic processes and stochastic structures. In the proposed method, the random variables describing the stochastic ground motions and structural parameters are respectively represented by the functions of one elementary random variable, resulting in the entire stochastic dynamic system can be represented by merely two elementary random variables. Since the number of elementary random variables needed is extremely small, the set of representative points in regard to the elementary random variables can thus be selected by number theoretical method. Benefiting from the proposed method, it can be conveniently combined with the probability density evolution method to realize the dynamic response analysis and dynamic reliability evaluation of nonlinear stochastic structures. The seismic response analysis of an eight-storey reinforced concrete frame structure with random parameters subjected to non-stationary stochastic ground motions are investigated as case studies. Numerical results fully demonstrated the effectiveness of the proposed method

Robust Linear Neural Network for Constrained Quadratic Optimization

Based on the feature of projection operator under box constraint, by using convex analysis method, this paper proposed three robust linear systems to solve a class of quadratic optimization problems. Utilizing linear matrix inequality (LMI) technique, eigenvalue perturbation theory, Lyapunov-Razumikhin method, and LaSalle’s invariance principle, some stable criteria for the related models are also established. Compared with previous criteria derived in the literature cited herein, the stable criteria established in this paper are less conservative and more practicable. Finally, a numerical simulation example and an application example in compressed sensing problem are also given to illustrate the validity of the criteria established in this paper

Phonemic Adversarial Attack against Audio Recognition in Real World

Recently, adversarial attacks for audio recognition have attracted much attention. However, most of the existing studies mainly rely on the coarse-grain audio features at the instance level to generate adversarial noises, which leads to expensive generation time costs and weak universal attacking ability. Motivated by the observations that all audio speech consists of fundamental phonemes, this paper proposes a phonemic adversarial tack (PAT) paradigm, which attacks the fine-grain audio features at the phoneme level commonly shared across audio instances, to generate phonemic adversarial noises, enjoying the more general attacking ability with fast generation speed. Specifically, for accelerating the generation, a phoneme density balanced sampling strategy is introduced to sample quantity less but phonemic features abundant audio instances as the training data via estimating the phoneme density, which substantially alleviates the heavy dependency on the large training dataset. Moreover, for promoting universal attacking ability, the phonemic noise is optimized in an asynchronous way with a sliding window, which enhances the phoneme diversity and thus well captures the critical fundamental phonemic patterns. By conducting extensive experiments, we comprehensively investigate the proposed PAT framework and demonstrate that it outperforms the SOTA baselines by large margins (i.e., at least 11X speed up and 78% attacking ability improvement)

Speak It Out: Solving Symbol-Related Problems with Symbol-to-Language Conversion for Language Models

Symbols (or more broadly, non-natural language textual representations) such as numerical sequences, molecular formulas, and table delimiters widely exist, playing important roles in various tasks such as abstract reasoning, chemical property prediction, and table question answering. Despite the impressive natural language comprehension capabilities of large language models (LLMs), their reasoning abilities for symbols remain inadequate, which could attributed to the difference between symbol representations and general natural languages. We propose symbol-to-language (S2L), a tuning-free method that enables large language models to solve symbol-related problems with information expressed in natural language. Specifically, S2L first converts the symbols involved to language-based representations, which can be implemented by prompting LLMs or leveraging external tools, then these language-based representations are integrated into the original problem via direct substitution or concatenation, serving as useful input information for LLMs. We evaluate the S2L method using both API-based (GPT-4, ChatGPT) and open-source (OpenChat) models over eight symbol-related tasks, ranging from symbol-only abstract reasoning to sentiment analysis in social media. Experimental results show that S2L consistently leads to superior performance. For example, by employing S2L for GPT-4, there can be average significant improvements of +21.9% and +9.5% for subtasks in 1D-ARC and Dyck language, respectively. Codes and data are available at https://github.com/THUNLP-MT/symbol2language.Comment: ICLR AGI Workshop 202

Mechanism of Thioesterase-Catalyzed Chain Release in the Biosynthesis of the Polyether Antibiotic Nanchangmycin

SummaryThe polyketide backbone of the polyether ionophore antibiotic nanchangmycin (1) is assembled by a modular polyketide synthase in Streptomyces nanchangensis NS3226. The ACP-bound polyketide is thought to undergo a cascade of oxidative cyclizations to generate the characteristic polyether. Deletion of the glycosyl transferase gene nanG5 resulted in accumulation of the corresponding nanchangmycin aglycone (6). The discrete thioesterase NanE exhibited a nearly 17-fold preference for hydrolysis of 4, the N-acetylcysteamine (SNAC) thioester of nanchangmycin, over 7, the corresponding SNAC derivative of the aglycone, consistent with NanE-catalyzed hydrolysis of ACP-bound nanchangmycin being the final step in the biosynthetic pathway. Site-directed mutagenesis established that Ser96, His261, and Asp120, the proposed components of the NanE catalytic triad, were all essential for thioesterase activity, while Trp97 was shown to influence the preference for polyether over polyketide substrates