Quantum operations with indefinite time direction

The fundamental dynamics of quantum particles is neutral with respect to the arrow of time. And yet, our experiments are not: we observe quantum systems evolving from the past to the future, but not the other way round. A fundamental question is whether it is in principle possible to conceive operations that probe quantum processes in the backward direction, from the future to the past, or in more general combinations of the forward and the backward direction. To answer this question, we characterize the largest set of bidirectional quantum processes, and we show that quantum theory is compatible with the existence of operations that interact with these processes in an indefinite time direction. As an explicit example, we construct an operation that adds quantum control to the time direction of an unknown dynamics. This operation, called the quantum time flip, exhibits an information-theoretic advantage over all possible operations with definite time direction, even including operations with indefinite causal order. It can realised probabilistically using quantum teleportation, and can be reproduced experimentally with photonic systems, shedding light on exotic scenarios in which the arrow of time is subject to quantum indefiniteness.Comment: 6 + 21 pages, 4 figures, new results adde

Continual Learning of Natural Language Processing Tasks: A Survey

Continual learning (CL) is an emerging learning paradigm that aims to emulate the human capability of learning and accumulating knowledge continually without forgetting the previously learned knowledge and also transferring the knowledge to new tasks to learn them better. This survey presents a comprehensive review of the recent progress of CL in the NLP field. It covers (1) all CL settings with a taxonomy of existing techniques. Besides dealing with forgetting, it also focuses on (2) knowledge transfer, which is of particular importance to NLP. Both (1) and (2) are not mentioned in the existing survey. Finally, a list of future directions is also discussed

Differentiable Frank-Wolfe Optimization Layer

Differentiable optimization has received a significant amount of attention due to its foundational role in the domain of machine learning based on neural networks. The existing methods leverages the optimality conditions and implicit function theorem to obtain the Jacobian matrix of the output, which increases the computational cost and limits the application of differentiable optimization. In addition, some non-differentiable constraints lead to more challenges when using prior differentiable optimization layers. This paper proposes a differentiable layer, named Differentiable Frank-Wolfe Layer (DFWLayer), by rolling out the Frank-Wolfe method, a well-known optimization algorithm which can solve constrained optimization problems without projections and Hessian matrix computations, thus leading to a efficient way of dealing with large-scale problems. Theoretically, we establish a bound on the suboptimality gap of the DFWLayer in the context of l1-norm constraints. Experimental assessments demonstrate that the DFWLayer not only attains competitive accuracy in solutions and gradients but also consistently adheres to constraints. Moreover, it surpasses the baselines in both forward and backward computational speeds

“Share for bargaining?”: A willingness model based on privacy computing theory

The use of mobile coupons to share for bargaining has become an important marketing method for merchants in the field of e-commerce. However, there are still some shortcomings in the existing research on consumers’ willingness to share mobile coupons. First of all, the use and sharing of mobile coupons are analyzed separately. Secondly, most of theories and models in this domain derive from the field of knowledge. Lastly, the influence of different platforms on consumers’ willingness to share are not considered. Therefore, this paper explores the influencing factors of consumers’ willingness to share mobile coupons in different platform scenarios from the perspective of privacy computing, and proposes six hypotheses to construct a structural equation model. Further analysis of 270 valid questionnaires obtained under five scenarios shows that users’ perceived economic benefits and perceived social benefits have a significant positive impact on users’ willingness to share for bargaining, users’ perceived privacy risks have no significant impact on users’ willingness to share for bargaining, and users’ perceived social risks have a significant negative impact on users’ willingness to share for bargaining. Low share for bargaining links will weaken the negative impact of perceived social risk on sharing willingness

Computationally Efficient Solution of Inverse Problem Using Bayesian Global Optimization Approach

Models have parameters that need to be determined from experimental observations. The problem of determining these parameters is known as the inverse problem or the model calibration problem. Solving inverse problems can be ubiquitously difficult because when the models involved are computationally expensive, one can only make a limited number of simulations. This work addresses the issue of solving an inverse problem with a limited data budget. Towards this end, we pose the inverse problem as the problem of minimizing a loss function that measures the discrepancy between model predictions and experimental measurements. Then, we employ Bayesian global optimization (BGO) to actively select the most informative simulations until either the expected improvement falls below a user defined threshold or our computational budget has been exhausted. We apply our results to the problem of estimating the kinetic rate coefficients modeling the catalytic conversion of nitrate to nitrogen using real experimental data

Deep Learning based method for Fire Detection

Fire accidents have become increasingly frequent and have profound effects on today’s society, leading to injuries, fatalities, and significant economic losses. It is crucial to develop effective and early fire detection systems that can promptly detect and prevent fire disasters. Machine learning and computer vision provide a promising solution for the early detection of fires, mitigating potential risks and enhancing safety measures. In this study, we present an extensive and comprehensive fire dataset, surpassing existing datasets in terms of both scale and diversity. This dataset enables robust and thorough training of fire detection models and serves as a benchmark for evaluating future fire detection systems. The core of our fire detection system is the state-of-the-art Yolov5 model, known for its simplicity, speed, and efficiency in object detection tasks. We demonstrate the effectiveness of our proposed model with promising results, achieving an average F1 score of 0.77 and an [email protected] score of approximately 0.77. These metrics reflect the model’s capability to accurately detect fires across various scenarios. Moreover, we take our research further by focusing on the deployment of the trained model to the cloud. The cloud deployment aspect enhances the practicality and accessibility of our fire detection system, making it more scalable and efficient. Furthermore, it opens up avenues for future advancements and integration with other smart technologies, contributing to the development of smarter and safer environments. Overall, this work contributes to the advancement of fire detection systems, offering a robust dataset, a powerful detection model, and an efficient cloud deployment approach. With this research, we aim to foster a safer and more secure environment by reducing the risks posed by fire accidents and enabling timely and effective fire prevention measures

Filamentation and inhibition of prokaryotic CTP synthase with ligands

Cytidine triphosphate synthase (CTPS) plays a pivotal role in the de novo synthesis of cytidine triphosphate (CTP), a fundamental building block for RNA and DNA that is essential for life. CTPS is capable of directly binding to all four nucleotide triphosphates: adenine triphosphate, uridine triphosphate, CTP, and guanidine triphosphate. Furthermore, CTPS can form cytoophidia in vivo and metabolic filaments in vitro, undergoing regulation at multiple levels. CTPS is considered a potential therapeutic target for combating invasions or infections by viral or prokaryotic pathogens. Utilizing cryo‐electron microscopy, we determined the structure of Escherichia coli CTPS (ecCTPS) filament in complex with CTP, nicotinamide adenine dinucleotide (NADH), and the covalent inhibitor 6‐diazo‐5‐oxo‐ l‐norleucine (DON), achieving a resolution of 2.9 Å. We constructed a phylogenetic tree based on differences in filament‐forming interfaces and designed a variant to validate our hypothesis, providing an evolutionary perspective on CTPS filament formation. Our computational analysis revealed a solvent‐accessible ammonia tunnel upon DON binding. Through comparative structural analysis, we discern a distinct mode of CTP binding of ecCTPS that differs from eukaryotic counterparts. Combining biochemical assays and structural analysis, we determined and validated the synergistic inhibitory effects of CTP with NADH or adenine on CTPS. Our results expand our comprehension of the diverse regulatory aspects of CTPS and lay a foundation for the design of specific inhibitors targeting prokaryotic CTPS