Steganography integration into a low-bit rate speech codec

Low bit-rate speech codecs have been widely used in audio communications like VoIP and mobile communications, so that steganography in low bit-rate audio streams would have broad applications in practice. In this paper, the authors propose a new algorithm for steganography in low bit-rate VoIP audio streams by integrating information hiding into the process of speech encoding. The proposed algorithm performs data embedding while pitch period prediction is conducted during low bit-rate speech encoding, thus maintaining synchronization between information hiding and speech encoding. The steganography algorithm can achieve high quality of speech and prevent detection of steganalysis, but also has great compatibility with a standard low bit-rate speech codec without causing further delay by data embedding and extraction. Testing shows, with the proposed algorithm, the data embedding rate of the secret message can attain 4 bits / frame (133.3 bits / second)

Deep Reinforcement Learning-Assisted Federated Learning for Robust Short-term Utility Demand Forecasting in Electricity Wholesale Markets

Short-term load forecasting (STLF) plays a significant role in the operation of electricity trading markets. Considering the growing concern of data privacy, federated learning (FL) is increasingly adopted to train STLF models for utility companies (UCs) in recent research. Inspiringly, in wholesale markets, as it is not realistic for power plants (PPs) to access UCs' data directly, FL is definitely a feasible solution of obtaining an accurate STLF model for PPs. However, due to FL's distributed nature and intense competition among UCs, defects increasingly occur and lead to poor performance of the STLF model, indicating that simply adopting FL is not enough. In this paper, we propose a DRL-assisted FL approach, DEfect-AwaRe federated soft actor-critic (DearFSAC), to robustly train an accurate STLF model for PPs to forecast precise short-term utility electricity demand. Firstly. we design a STLF model based on long short-term memory (LSTM) using just historical load data and time data. Furthermore, considering the uncertainty of defects occurrence, a deep reinforcement learning (DRL) algorithm is adopted to assist FL by alleviating model degradation caused by defects. In addition, for faster convergence of FL training, an auto-encoder is designed for both dimension reduction and quality evaluation of uploaded models. In the simulations, we validate our approach on real data of Helsinki's UCs in 2019. The results show that DearFSAC outperforms all the other approaches no matter if defects occur or not

PokerGPT: An End-to-End Lightweight Solver for Multi-Player Texas Hold'em via Large Language Model

Poker, also known as Texas Hold'em, has always been a typical research target within imperfect information games (IIGs). IIGs have long served as a measure of artificial intelligence (AI) development. Representative prior works, such as DeepStack and Libratus heavily rely on counterfactual regret minimization (CFR) to tackle heads-up no-limit Poker. However, it is challenging for subsequent researchers to learn CFR from previous models and apply it to other real-world applications due to the expensive computational cost of CFR iterations. Additionally, CFR is difficult to apply to multi-player games due to the exponential growth of the game tree size. In this work, we introduce PokerGPT, an end-to-end solver for playing Texas Hold'em with arbitrary number of players and gaining high win rates, established on a lightweight large language model (LLM). PokerGPT only requires simple textual information of Poker games for generating decision-making advice, thus guaranteeing the convenient interaction between AI and humans. We mainly transform a set of textual records acquired from real games into prompts, and use them to fine-tune a lightweight pre-trained LLM using reinforcement learning human feedback technique. To improve fine-tuning performance, we conduct prompt engineering on raw data, including filtering useful information, selecting behaviors of players with high win rates, and further processing them into textual instruction using multiple prompt engineering techniques. Through the experiments, we demonstrate that PokerGPT outperforms previous approaches in terms of win rate, model size, training time, and response speed, indicating the great potential of LLMs in solving IIGs

Large Foundation Models for Power Systems

Foundation models, such as Large Language Models (LLMs), can respond to a wide range of format-free queries without any task-specific data collection or model training, creating various research and application opportunities for the modeling and operation of large-scale power systems. In this paper, we outline how such large foundation model such as GPT-4 are developed, and discuss how they can be leveraged in challenging power and energy system tasks. We first investigate the potential of existing foundation models by validating their performance on four representative tasks across power system domains, including the optimal power flow (OPF), electric vehicle (EV) scheduling, knowledge retrieval for power engineering technical reports, and situation awareness. Our results indicate strong capabilities of such foundation models on boosting the efficiency and reliability of power system operational pipelines. We also provide suggestions and projections on future deployment of foundation models in power system applications.Comment: Code available at https://github.com/chennnnnyize/LLM_PowerSystem

Beyond Worst-case Attacks: Robust RL with Adaptive Defense via Non-dominated Policies

In light of the burgeoning success of reinforcement learning (RL) in diverse real-world applications, considerable focus has been directed towards ensuring RL policies are robust to adversarial attacks during test time. Current approaches largely revolve around solving a minimax problem to prepare for potential worst-case scenarios. While effective against strong attacks, these methods often compromise performance in the absence of attacks or the presence of only weak attacks. To address this, we study policy robustness under the well-accepted state-adversarial attack model, extending our focus beyond only worst-case attacks. We first formalize this task at test time as a regret minimization problem and establish its intrinsic hardness in achieving sublinear regret when the baseline policy is from a general continuous policy class, $\Pi$. This finding prompts us to \textit{refine} the baseline policy class $\Pi$ prior to test time, aiming for efficient adaptation within a finite policy class \Tilde{\Pi}, which can resort to an adversarial bandit subroutine. In light of the importance of a small, finite \Tilde{\Pi}, we propose a novel training-time algorithm to iteratively discover \textit{non-dominated policies}, forming a near-optimal and minimal \Tilde{\Pi}, thereby ensuring both robustness and test-time efficiency. Empirical validation on the Mujoco corroborates the superiority of our approach in terms of natural and robust performance, as well as adaptability to various attack scenarios.Comment: International Conference on Learning Representations (ICLR) 2024, spotligh

Alcohol Intake and Abnormal Expression of Brf1 in Breast Cancer.

Breast cancer is the most common malignant disease of females. Overall, one woman in every nine will get breast cancer at some time in her life. Epidemiological studies have indicated that alcohol consumption has most consistently been associated with breast cancer risk. However, the mechanism of alcohol-associated breast cancer remains to be addressed. Little is known about the effects of alcohol consumption on Brf1 (TFIIIB-related factor 1) expression and RNA Pol III gene (RNA polymerase III-dependent gene) transcription, which are responsible for protein synthesis and tightly linked to cell proliferation, cell transformation, and tumor development. Emerging evidences have indicated that alcohol induces deregulation of Brf1 and Pol III genes to cause the alterations of cell phenotypes and tumor formation. In this paper, we summarize the progresses regarding alcohol-caused increase in the expression of Brf1 and Pol III genes and analysis of its molecular mechanism of breast cancer. As the earlier and accurate diagnosis approach of breast cancer is not available yet, exploring the molecular mechanism and identifying the biomarker of alcohol-associated breast cancer are especially important. Recent studies have demonstrated that Brf1 is overexpressed in most ER+ (estrogen receptor positive) cases of breast cancer and the change in cellular levels of Brf1 reflects the therapeutic efficacy and prognosis of this disease. It suggests that Brf1 may be a potential diagnosis biomarker and a therapeutic target of alcohol-associated breast cancer