Short Distance Modification of a Gravitational System and its Optical Analog

Motivated by developments in string theory, such as T-duality, it has been proposed that the geometry of spacetime should have an intrinsic minimal length associated with it. This would modify the short distance behavior of quantum systems studied on such a geometry, and an optical analog for such a short distance modification of quantum system has also been realized by using non-paraxial nonlinear optics. As general relativity can be viewed as an effective field theory obtained from string, it is expected that this would also modify the short distance behavior of general relativity. Now the Newtonian approximation is a valid short distance approximation to general relativity, and Schrodinger-Newton equation can be obtained as a non-relativistic semi-classical limit of such a theory, we will analyze the short distance modification of Schrodinger-Newton equation from an intrinsic minimal length in the geometry of spacetime. As an optical analog of the Schrodinger-Newton equation has been constructed, it is possible to optically realize this system. So, this system is important, and we will numerical analyze the solutions for this system. It will be observed that the usual Runge-Kutta method cannot be used to analyze this system. However, we will use a propose and use a new numerical method, which we will call as the two step Runge-Kutta method, for analyzing this system.Comment: 21 pages, 3 figures, 2 table

Label-Based Disentanglement Measure among Hidden Units of Deep Learning

The capability to disentangle underlying factors hidden in the observable data, thereby obtaining their abstract representations, is considered one important ingredient for the subsequent success of deep networks in various application scenarios. Recently, numerous practical measures and learning strategies have been established for disentanglement, showcasing their potential in improving the model’s explainability, controlability, and robustness. However, when the downstream tasks come to the classification issues, there is still no consensus in the community on the definition or measurement for disentanglement, and its connection to the generalization capacity remains not very clear. Aiming at this, we explore the highly non-linear effect of a specified hidden layer on the generalization capacity from an information perspective and obtain a tight bound. Upon decompsing the bound, we find that besides the unsupervised disentanglement measure term in the conventional sense, a new supervised disentanglement term also emerges with a nonnegligible effect on the generality. Consequently, a novel label-based disentanglement measure (LDM) is naturally introduced as the discrepancy between these two terms under the supervised learning settings to substitute the commonly used unsupervised disentanglement measure. The theoretical analysis reveals an inverse relationship between the defined LDM and the generalization capacity. Finally, using LDM as regularizer, the experiments show that the deep neural networks (DNNs) can effectively reduce generalization error while improving classification accuracy when noise is added to the data features or labels, which strongly supports our points

Near-real-time Earthquake-induced Fatality Estimation using Crowdsourced Data and Large-Language Models

When a damaging earthquake occurs, immediate information about casualties is critical for time-sensitive decision-making by emergency response and aid agencies in the first hours and days. Systems such as Prompt Assessment of Global Earthquakes for Response (PAGER) by the U.S. Geological Survey (USGS) were developed to provide a forecast within about 30 minutes of any significant earthquake globally. Traditional systems for estimating human loss in disasters often depend on manually collected early casualty reports from global media, a process that's labor-intensive and slow with notable time delays. Recently, some systems have employed keyword matching and topic modeling to extract relevant information from social media. However, these methods struggle with the complex semantics in multilingual texts and the challenge of interpreting ever-changing, often conflicting reports of death and injury numbers from various unverified sources on social media platforms. In this work, we introduce an end-to-end framework to significantly improve the timeliness and accuracy of global earthquake-induced human loss forecasting using multi-lingual, crowdsourced social media. Our framework integrates (1) a hierarchical casualty extraction model built upon large language models, prompt design, and few-shot learning to retrieve quantitative human loss claims from social media, (2) a physical constraint-aware, dynamic-truth discovery model that discovers the truthful human loss from massive noisy and potentially conflicting human loss claims, and (3) a Bayesian updating loss projection model that dynamically updates the final loss estimation using discovered truths. We test the framework in real-time on a series of global earthquake events in 2021 and 2022 and show that our framework streamlines casualty data retrieval, achieving speed and accuracy comparable to manual methods by USGS.Comment: 10 pages, 8 figure

Generation and discrimination of aviation product failure modes based on text mining

Aviation product failure text data has the characteristics of complex format and highly professional algorithms that requires interpretability. A fault mode generation and identification technology based on text mining method is proposed. The automatic generation and discrimination of field fault modes of aviation products are realized through the step-by-step task processing such as data management, clustering classification and outlier detection of fault text data. The technology is conducted with experiment verification of actual field fault data. The results show that the proposed technology can meet the requirements of algorithm agility and interpretability for fault text data at the current stage, effectively overcome the problems of relatively small fault text data volume, complex data preprocessing, and multiple steps involved, effectively improve the efficiency of fault text data processing, assist the generation of field fault mode libraries, and guide the fault analysis and application of design, support and other processes

A Learning-Based Assembly Sequence Planning Method Using Neural Combinatorial Optimization With Satisfactory Generalization Ability

This paper proposes a specific and effective real-time sequence planning method using robot manipulators to complete complex assembly tasks. Many previous studies developed different traversal methods to obtain the optimal assembly sequence. Besides, a number of algorithms were proposed to enhance flexibility when the conditions or rules were changed in various sequence optimization problems. However, these state-of-the-art (STOA) methods necessarily require modifications when task details are changed. Consequently, to further improve the generalization ability and improve the performance of the sequence optimization, a neural combinatorial optimization algorithm combined with a self-learning strategy is proposed for assembly sequence planning. In addition, obstacle avoidance and the non-collision constraints between workpieces in the assembly process are considered. According to the experiment results, the new method is superior to the STOA methods in terms of optimization efficiency. More importantly, the proposed method has satisfactory generalization ability for different assembly tasks. Note to Practitioners - This paper studies assembly sequence planning problems for different real-world applications in industrial and home service fields. Many assembly sequence planning solutions have been widely utilized before. However, the generalization ability of the previous methods is not satisfactory since the re-adjust process is required when the workpiece number or collision condition changes in different tasks. Motivated by the above reasons, this paper develops a learning-based assembly sequence planning solution to resolve complex assembly problems without parameter re-adjustment processes. Users can directly apply the developed workpiece identification and localization method to obtain the sensing information. Then, the newly designed collision-free cost function should be programmed as the core of the assembly sequence optimization. Next, the proposed neural combinatorial optimization (NCO) with the sensing information and target configuration as inputs can provide the optimal assembly sequence by self-learning. The learned NCO-based method can be directly applied to diverse planning tasks, even with different workpiece numbers. Users can also refer to the experimental examples in this paper for the extension of the proposed method to their own applications

Novel phthalimides regulating PD-1/PD-L1 interaction as potential immunotherapy agents

Programmed cell death 1(PD-1)/programmed cell death ligand 1(PD-L1) have emerged as one of the most promising immune checkpoint targets for cancer immunotherapy. Despite the inherent advantages of small-molecule inhibitors over antibodies, the discovery of small-molecule inhibitors has fallen behind that of antibody drugs. Based on docking studies between small molecule inhibitor and PD-L1 protein, changing the chemical linker of inhibitor from a flexible chain to an aromatic ring may improve its binding capacity to PD-L1 protein, which was not reported before. A series of novel phthalimide derivatives from structure-based rational design was synthesized. P39 was identified as the best inhibitor with promising activity, which not only inhibited PD-1/PD-L1 interaction (IC50 = 8.9 nmol/L), but also enhanced killing efficacy of immune cells on cancer cells. Co-crystal data demonstrated that P39 induced the dimerization of PD-L1 proteins, thereby blocking the binding of PD-1/PD-L1. Moreover, P39 exhibited a favorable safety profile with a LD50 &gt; 5000 mg/kg and showed significant in vivo antitumor activity through promoting CD8+ T cell activation. All these data suggest that P39 acts as a promising small chemical inhibitor against the PD-1/PD-L1 axis and has the potential to improve the immunotherapy efficacy of T-cells.</p