The knapsack problem is a classical combinatorial optimization problem widely encountered in areas such as logistics,resource allocation,and portfolio optimization.Traditional methods,including dynamic program-ming(DP...The knapsack problem is a classical combinatorial optimization problem widely encountered in areas such as logistics,resource allocation,and portfolio optimization.Traditional methods,including dynamic program-ming(DP)and greedy algorithms,have been effective in solving small problem instances but often struggle with scalability and efficiency as the problem size increases.DP,for instance,has exponential time complexity and can become computationally prohibitive for large problem instances.On the other hand,greedy algorithms offer faster solutions but may not always yield the optimal results,especially when the problem involves complex constraints or large numbers of items.This paper introduces a novel reinforcement learning(RL)approach to solve the knapsack problem by enhancing the state representation within the learning environment.We propose a representation where item weights and volumes are expressed as ratios relative to the knapsack’s capacity,and item values are normalized to represent their percentage of the total value across all items.This novel state modification leads to a 5%improvement in accuracy compared to the state-of-the-art RL-based algorithms,while significantly reducing execution time.Our RL-based method outperforms DP by over 9000 times in terms of speed,making it highly scalable for larger problem instances.Furthermore,we improve the performance of the RL model by incorporating Noisy layers into the neural network architecture.The addition of Noisy layers enhances the exploration capabilities of the agent,resulting in an additional accuracy boost of 0.2%–0.5%.The results demonstrate that our approach not only outperforms existing RL techniques,such as the Transformer model in terms of accuracy,but also provides a substantial improvement than DP in computational efficiency.This combination of enhanced accuracy and speed presents a promising solution for tackling large-scale optimization problems in real-world applications,where both precision and time are critical factors.展开更多
Cutaneous T-cell lymphomas(CTCLs)are a heterogeneous group of skin-homing non-Hodgkin lymphomas.There are limited options for effective treatment of patients with advanced-stage CTCL,leading to a poor survival rate.Ep...Cutaneous T-cell lymphomas(CTCLs)are a heterogeneous group of skin-homing non-Hodgkin lymphomas.There are limited options for effective treatment of patients with advanced-stage CTCL,leading to a poor survival rate.Epigenetics plays a pivotal role in regulating gene expression without altering the DNA sequence.Epigenetic alterations are involved in virtually all key cancerassociated pathways and are fundamental to the genesis of cancer.In recent years,the epigenetic hallmarks of CTCL have been gradually elucidated and their potential values in the diagnosis,prognosis,and therapeutic intervention have been clarified.In this review,we summarize the current knowledge of the best-studied epigenetic modifications in CTCL,including DNA methylation,histone modifications,micro RNAs,and chromatin remodelers.These epigenetic regulators are essential in the development of CTCL and provide new insights into the clinical treatments of this refractory disease.展开更多
The access of massive Internet of Things(IoT)users poses several challenges for Unmanned Aerial Vehicle(UAV)-aided communications,particularly in terms of security and reliability.This paper investigates a secure and ...The access of massive Internet of Things(IoT)users poses several challenges for Unmanned Aerial Vehicle(UAV)-aided communications,particularly in terms of security and reliability.This paper investigates a secure and robust power allocation scheme for UAV-aided IoT Non-Orthogonal Multiple Access(NOMA)downlink networks with a potential eavesdropper,considering imperfect Channel State Information(CSI).Given the noise uncertainty caused by the UAV’s mobility and the statistical channel estimation error,we formulate a robust optimization problem to maximize the total covert rate of all NOMA users,subject to covertness and rate-based reliability constraints.To solve this optimization problem,we first derive the minimum detection error rate and utilize the statistical characteristics(i.e.,the mean and variance of channel gain errors)to obtain the deterministic covertness and reliability constraints,respectively.We then prove that the problem is concave and determine the optimal power allocation algorithm using the Karush–Kuhn–Tucker conditions.Extensive numerical simulations validate the effectiveness of the proposed algorithm and demonstrate its ability to realize more secure and robust UAV-aided IoT systems.展开更多
基金supported in part by the Research Start-Up Funds of South-Central Minzu University under Grants YZZ23002,YZY23001,and YZZ18006in part by the Hubei Provincial Natural Science Foundation of China under Grants 2024AFB842 and 2023AFB202+3 种基金in part by the Knowledge Innovation Program of Wuhan Basic Research underGrant 2023010201010151in part by the Spring Sunshine Program of Ministry of Education of the People’s Republic of China under Grant HZKY20220331in part by the Funds for Academic Innovation Teams and Research Platformof South-CentralMinzu University Grant Number:XT224003,PTZ24001in part by the Career Development Fund(CDF)of the Agency for Science,Technology and Research(A*STAR)(Grant Number:C233312007).
文摘The knapsack problem is a classical combinatorial optimization problem widely encountered in areas such as logistics,resource allocation,and portfolio optimization.Traditional methods,including dynamic program-ming(DP)and greedy algorithms,have been effective in solving small problem instances but often struggle with scalability and efficiency as the problem size increases.DP,for instance,has exponential time complexity and can become computationally prohibitive for large problem instances.On the other hand,greedy algorithms offer faster solutions but may not always yield the optimal results,especially when the problem involves complex constraints or large numbers of items.This paper introduces a novel reinforcement learning(RL)approach to solve the knapsack problem by enhancing the state representation within the learning environment.We propose a representation where item weights and volumes are expressed as ratios relative to the knapsack’s capacity,and item values are normalized to represent their percentage of the total value across all items.This novel state modification leads to a 5%improvement in accuracy compared to the state-of-the-art RL-based algorithms,while significantly reducing execution time.Our RL-based method outperforms DP by over 9000 times in terms of speed,making it highly scalable for larger problem instances.Furthermore,we improve the performance of the RL model by incorporating Noisy layers into the neural network architecture.The addition of Noisy layers enhances the exploration capabilities of the agent,resulting in an additional accuracy boost of 0.2%–0.5%.The results demonstrate that our approach not only outperforms existing RL techniques,such as the Transformer model in terms of accuracy,but also provides a substantial improvement than DP in computational efficiency.This combination of enhanced accuracy and speed presents a promising solution for tackling large-scale optimization problems in real-world applications,where both precision and time are critical factors.
基金supported by grants from the National Natural Science Foundation of China(Grant Nos.81872214 and 81922058)。
文摘Cutaneous T-cell lymphomas(CTCLs)are a heterogeneous group of skin-homing non-Hodgkin lymphomas.There are limited options for effective treatment of patients with advanced-stage CTCL,leading to a poor survival rate.Epigenetics plays a pivotal role in regulating gene expression without altering the DNA sequence.Epigenetic alterations are involved in virtually all key cancerassociated pathways and are fundamental to the genesis of cancer.In recent years,the epigenetic hallmarks of CTCL have been gradually elucidated and their potential values in the diagnosis,prognosis,and therapeutic intervention have been clarified.In this review,we summarize the current knowledge of the best-studied epigenetic modifications in CTCL,including DNA methylation,histone modifications,micro RNAs,and chromatin remodelers.These epigenetic regulators are essential in the development of CTCL and provide new insights into the clinical treatments of this refractory disease.
基金supported in part by the National Natural Science Foundation of China(No.62403500)in part by the Hubei Provincial Natural Science Foundation,China(No.2023AFB202)+4 种基金in part by the Fundamental Research Funds for the Central Universities,South-Central Minzu University,China(No.CZQ23016)in part by the Chunhui Program of Ministry of Education(No.HZKY20220331)in part by the Research Start-up Funds of South-Central Minzu University,China(Nos.YZZ18006,YZY23001)in part by the Fund for Academic Innovation Teams and Research Platform of South-Central Minzu University,China(Nos.XTZ24003,PTZ24001)in part by the Research Matching Grant Scheme from the Research Grants Council of Hong Kong。
文摘The access of massive Internet of Things(IoT)users poses several challenges for Unmanned Aerial Vehicle(UAV)-aided communications,particularly in terms of security and reliability.This paper investigates a secure and robust power allocation scheme for UAV-aided IoT Non-Orthogonal Multiple Access(NOMA)downlink networks with a potential eavesdropper,considering imperfect Channel State Information(CSI).Given the noise uncertainty caused by the UAV’s mobility and the statistical channel estimation error,we formulate a robust optimization problem to maximize the total covert rate of all NOMA users,subject to covertness and rate-based reliability constraints.To solve this optimization problem,we first derive the minimum detection error rate and utilize the statistical characteristics(i.e.,the mean and variance of channel gain errors)to obtain the deterministic covertness and reliability constraints,respectively.We then prove that the problem is concave and determine the optimal power allocation algorithm using the Karush–Kuhn–Tucker conditions.Extensive numerical simulations validate the effectiveness of the proposed algorithm and demonstrate its ability to realize more secure and robust UAV-aided IoT systems.
