Classical O(N) nonlinear sigma model on the half line: a study on consistent Hamiltonian description

The problem of consistent Hamiltonian structure for O(N) nonlinear sigma model in the presence of five different types of boundary conditions is considered in detail. For the case of Neumann, Dirichlet and the mixture of these two types of boundaries, the consistent Poisson brackets are constructed explicitly, which may be used, e.g. for the construction of current algebras in the presence of boundary. While for the mixed boundary conditions and the mixture of mixed and Dirichlet boundary conditions, we prove that there is no consistent Poisson brackets, showing that the mixed boundary conditions are incompatible with all nontrivial subgroups of $O((N)$.Comment: revtex4, 7pp, bibte

Safe Deep Policy Adaptation

A critical goal of autonomy and artificial intelligence is enabling autonomous robots to rapidly adapt in dynamic and uncertain environments. Classic adaptive control and safe control provide stability and safety guarantees but are limited to specific system classes. In contrast, policy adaptation based on reinforcement learning (RL) offers versatility and generalizability but presents safety and robustness challenges. We propose SafeDPA, a novel RL and control framework that simultaneously tackles the problems of policy adaptation and safe reinforcement learning. SafeDPA jointly learns adaptive policy and dynamics models in simulation, predicts environment configurations, and fine-tunes dynamics models with few-shot real-world data. A safety filter based on the Control Barrier Function (CBF) on top of the RL policy is introduced to ensure safety during real-world deployment. We provide theoretical safety guarantees of SafeDPA and show the robustness of SafeDPA against learning errors and extra perturbations. Comprehensive experiments on (1) classic control problems (Inverted Pendulum), (2) simulation benchmarks (Safety Gym), and (3) a real-world agile robotics platform (RC Car) demonstrate great superiority of SafeDPA in both safety and task performance, over state-of-the-art baselines. Particularly, SafeDPA demonstrates notable generalizability, achieving a 300% increase in safety rate compared to the baselines, under unseen disturbances in real-world experiments.Comment: 8 pages, 7 figure

Ternary Compression for Communication-Efficient Federated Learning

Learning over massive data stored in different locations is essential in many real-world applications. However, sharing data is full of challenges due to the increasing demands of privacy and security with the growing use of smart mobile devices and IoT devices. Federated learning provides a potential solution to privacy-preserving and secure machine learning, by means of jointly training a global model without uploading data distributed on multiple devices to a central server. However, most existing work on federated learning adopts machine learning models with full-precision weights, and almost all these models contain a large number of redundant parameters that do not need to be transmitted to the server, consuming an excessive amount of communication costs. To address this issue, we propose a federated trained ternary quantization (FTTQ) algorithm, which optimizes the quantized networks on the clients through a self-learning quantization factor. A convergence proof of the quantization factor and the unbiasedness of FTTQ is given. In addition, we propose a ternary federated averaging protocol (T-FedAvg) to reduce the upstream and downstream communication of federated learning systems. Empirical experiments are conducted to train widely used deep learning models on publicly available datasets, and our results demonstrate the effectiveness of FTTQ and T-FedAvg compared with the canonical federated learning algorithms in reducing communication costs and maintaining the learning performance

The origin of noncommutativity?

Consistent boundary Poisson structures for open string theory coupled to background $B$-field are considered using the new approach proposed in hep-th/0111005. It is found that there are infinitely many consistent Poisson structures, each leads to a consistent canonical quantization of open string in the presence of background $B$-field. Consequently, whether the $D$-branes to which the open string end points are attached is noncommutative or not depends on the choice of a particular Poisson structure.Comment: Revtex4, published versio

Reinforcement Learning Based Gasoline Blending Optimization: Achieving More Efficient Nonlinear Online Blending of Fuels

The online optimization of gasoline blending benefits refinery economies. However, the nonlinear blending mechanism, the oil property fluctuations, and the blending model mismatch bring difficulties to the optimization. To solve the above issues, this paper proposes a novel online optimization method based on deep reinforcement learning algorithm (DRL). The Markov decision process (MDP) expression are given considering a practical gasoline blending system. Then, the environment simulator of gasoline blending process is established based on the MDP expression and the one-year measurement data of a real-world refinery. The soft actor-critic (SAC) DRL algorithm is applied to improve the DRL agent policy by using the data obtained from the interaction between DRL agent and environment simulator. Compared with a traditional method, the proposed method has better economic performance. Meanwhile, it is more robust under property fluctuations and component oil switching. Furthermore, the proposed method maintains performance by automatically adapting to system drift.Comment: 30 pages,13 figure

Suppressed atmospheric chemical aging of cooking organic aerosol particles in wintertime conditions

Cooking organic aerosol (COA) is one of the major constituents of particulate matter in urban areas. COA is oxidized by atmospheric oxidants such as ozone, changing its physical, chemical and toxicological properties. However, atmospheric chemical lifetimes of COA and its tracers such as oleic acid are typically longer than those that have been estimated by laboratory studies. We tackled the issue by considering temperature. Namely, we hypothesize that increased viscosity of COA at ambient temperature accounts for its prolonged atmospheric chemical lifetimes in wintertime. Laboratory-generated COA particles from cooking oil were exposed to ozone in an aerosol flow tube reactor for the temperature range of −20 to 35 °C. The pseudo-second-order chemical reaction rate constants (k2) were estimated from the experimental data by assuming a constant ozone concentration in the flow tube. The estimated values of k2 decreased by orders of magnitude for lower temperatures. The temperature dependence in k2 was fit well by considering the diffusion-limited chemical reaction mechanism. The result suggested that increased viscosity was likely the key factor to account for the decrease in chemical reactivity at the reduced temperature range, though the idea will still need to be verified by temperature-dependent viscosity data in the future. In combination with the observed global surface temperature, the atmospheric chemical lifetimes of COA were estimated to be much longer in wintertime (&gt; 1 h) than in summertime (a few minutes) for temperate and boreal regions. Our present study demonstrates that the oxidation lifetimes of COA particles will need to be parameterized as a function of temperature in the future for estimating environmental impacts and fates of this category of particulate matter.</p

COVID-19 and bilingual children’s home language environment: Digital media, socioeconomic status, and language status

Input is considered crucial in bilingual children’s language development. This is especially true for bilingual children’s mother tongue language learning given its common reduction in input opportunities due to the dominance of one language within society, as seen in countries and regions from Wales to Singapore. Previous studies tend to focus on the quantity and quality of conventional active communication and resources (e.g., speaking and reading with parents) on bilingual children’s language development, and substantially, fewer studies have explored this topic from the perspective of digital media. However, the COVID-19 pandemic has accentuated the critical role of digital media in various aspects of life, including bilingual children’s home language environment. Thus, to holistically understand bilingual children’s daily language input patterns, it is imperative to explore both their conventional and digital media input resources. The current study focuses on English-Mandarin bilingual children in Singapore and would like to explore (1) whether their conventional and digital media language environments have been affected by the COVID-19 pandemic and (2) whether the societal status of a language and familial socioeconomic status (SES) would affect bilingual children’s conventional and digital media input. Survey data from 162 parents of English-Mandarin bilingual preschoolers (3 to 6 years old) were used to explore the two research questions. Two online parental questionnaires were employed for data collection. One-way repeated-measures MANOVA and path models were used to address the questions. The results indicated that input patterns from nuclear family members had not been affected by COVID-19; however, the amount and frequency of conventional and digital media materials and activities increased significantly since COVID-19. Higher-SES families possessed more conventional materials and conducted conventional activities more often, while lower-SES families possessed more digital media materials. Both conventional and digital media materials and activities were richer in English than in Mandarin. Higher-SES families perceived digital media usage for learning to be of less importance than lower-SES families. The implications for early bilingual learning following COVID-19 are discussed

A Highly Controllable Electrochemical Anodization Process to Fabricate Porous Anodic Aluminum Oxide Membranes

Due to the broad applications of porous alumina nanostructures, research on fabrication of anodized aluminum oxide (AAO) with nanoporous structure has triggered enormous attention. While fabrication of highly ordered nanoporous AAO with tunable geometric features has been widely reported, it is known that its growth rate can be easily affected by the fluctuation of process conditions such as acid concentration and temperature during electrochemical anodization process. To fabricate AAO with various geometric parameters, particularly, to realize precise control over pore depth for scientific research and commercial applications, a controllable fabrication process is essential. In this work, we revealed a linear correlation between the integrated electric charge flow throughout the circuit in the stable anodization process and the growth thickness of AAO membranes. With this understanding, we developed a facile approach to precisely control the growth process of the membranes. It was found that this approach is applicable in a large voltage range, and it may be extended to anodization of other metal materials such as Ti as well.Hong Kong Research Grant Council [612113]; Hong Kong Innovation Technology Commission [ITS/362/14FP]; Fundamental Research Project of Shenzhen Science &amp; Technology Foundation [JCYJ20130402164725025]; National Natural Science Foundation of China [61574005]; Priority Academic Program Development of Jiangsu Higher Education Institutions [PAPD]SCI(E)[email protected]; [email protected]

Generation of rotational ground state HD+^+ ions in an ion trap using a resonance-enhanced threshold photoionization process

We report a method for producing ultracold HD+ molecular ions populated in a rotational ground state in an ion trap based on [2+1'] resonance-enhanced threshold photoionization (RETPI) and sympathetic cooling with the laser-cooled Be$^+$ ions. The effect of electric field of the ion trap on the RETPI process of neutral HD molecules and the blackbody radiation (BBR) on the population evolution of rotational states of the generated polar HD+ ions have been studied. The initial rotational ground state population of HD$^+$ ions is 0.93(12). After the cumulation time of 5 s, the rotational ground state population is reduced to 0.77(8) due to the BBR coupling. This method of generating ultracold state-selected HD$^+$ ions is beneficial for the studies in precision rovibrational spectroscopy, state-controlled cold chemical reaction, and quantum logic spectroscopy.Comment: accepted by PRA, 7 pages, 7 figure