Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review

This systematic literature review comprehensively examines the application of Large Language Models (LLMs) in forecasting and anomaly detection, highlighting the current state of research, inherent challenges, and prospective future directions. LLMs have demonstrated significant potential in parsing and analyzing extensive datasets to identify patterns, predict future events, and detect anomalous behavior across various domains. However, this review identifies several critical challenges that impede their broader adoption and effectiveness, including the reliance on vast historical datasets, issues with generalizability across different contexts, the phenomenon of model hallucinations, limitations within the models' knowledge boundaries, and the substantial computational resources required. Through detailed analysis, this review discusses potential solutions and strategies to overcome these obstacles, such as integrating multimodal data, advancements in learning methodologies, and emphasizing model explainability and computational efficiency. Moreover, this review outlines critical trends that are likely to shape the evolution of LLMs in these fields, including the push toward real-time processing, the importance of sustainable modeling practices, and the value of interdisciplinary collaboration. Conclusively, this review underscores the transformative impact LLMs could have on forecasting and anomaly detection while emphasizing the need for continuous innovation, ethical considerations, and practical solutions to realize their full potential

Transdifferentiation of lung adenocarcinoma in mice with Lkb1 deficiency to squamous cell carcinoma

Lineage transition in adenocarcinoma (ADC) and squamous cell carcinoma (SCC) of non-small cell lung cancer, as implicated by clinical observation of mixed ADC and SCC pathologies in adenosquamous cell carcinoma, remains a fundamental yet unsolved question. Here we provide in vivo evidence showing the transdifferentiation of lung cancer from ADC to SCC in mice: Lkb1-deficient lung ADC progressively transdifferentiates into SCC, via a pathologically mixed mAd-SCC intermediate. We find that reduction of lysyl oxidase (Lox) in Lkb1-deficient lung ADC decreases collagen disposition and triggers extracellular matrix remodelling and upregulates p63 expression, a SCC lineage survival oncogene. Pharmacological Lox inhibition promotes the transdifferentiation, whereas ectopic Lox expression significantly inhibits this process. Notably, ADC and SCC show differential responses to Lox inhibition. Collectively, our findings demonstrate the de novo transdifferentiation of lung ADC to SCC in mice and provide mechanistic insight that may have important implications for lung cancer treatment

Isogenic Pairs of Wild Type and Mutant Induced Pluripotent Stem Cell (iPSC) Lines from Rett Syndrome Patients as In Vitro Disease Model

Rett syndrome (RTT) is an autism spectrum developmental disorder caused by mutations in the X-linked methyl-CpG binding protein 2 (MECP2) gene. Excellent RTT mouse models have been created to study the disease mechanisms, leading to many important findings with potential therapeutic implications. These include the identification of many MeCP2 target genes, better understanding of the neurobiological consequences of the loss- or mis-function of MeCP2, and drug testing in RTT mice and clinical trials in human RTT patients. However, because of potential differences in the underlying biology between humans and common research animals, there is a need to establish cell culture-based human models for studying disease mechanisms to validate and expand the knowledge acquired in animal models. Taking advantage of the nonrandom pattern of X chromosome inactivation in female induced pluripotent stem cells (iPSC), we have generated isogenic pairs of wild type and mutant iPSC lines from several female RTT patients with common and rare RTT mutations. R294X (arginine 294 to stop codon) is a common mutation carried by 5–6% of RTT patients. iPSCs carrying the R294X mutation has not been studied. We differentiated three R294X iPSC lines and their isogenic wild type control iPSC into neurons with high efficiency and consistency, and observed characteristic RTT pathology in R294X neurons. These isogenic iPSC lines provide unique resources to the RTT research community for studying disease pathology, screening for novel drugs, and testing toxicology

Baichuan 2: Open Large-scale Language Models

Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing the need for extensive feature engineering. However, most powerful LLMs are closed-source or limited in their capability for languages other than English. In this technical report, we present Baichuan 2, a series of large-scale multilingual language models containing 7 billion and 13 billion parameters, trained from scratch, on 2.6 trillion tokens. Baichuan 2 matches or outperforms other open-source models of similar size on public benchmarks like MMLU, CMMLU, GSM8K, and HumanEval. Furthermore, Baichuan 2 excels in vertical domains such as medicine and law. We will release all pre-training model checkpoints to benefit the research community in better understanding the training dynamics of Baichuan 2.Comment: Baichuan 2 technical report. Github: https://github.com/baichuan-inc/Baichuan

Electro-optic Modulation Programmable Optical Frequency Comb based on Deep Learning

【Objective】To meet the diverse application demands for high-performance Optical Frequency Comb (OFC), especially in terms of independently adjustable parameters like bandwidth, flatness, central wavelength, and spectral line spacing, a method based on secondary coupled Radio Frequency (RF) signals to drive a single Dual Drive Mach-Zehnder Modulator (DDMZM) for OFC generation is proposed.【Methods】Utilizing a single multiplier to generate the secondary RF coupled signals not only increases the number of comb lines produced by the OFC but also offers the advantages of a simple structure and low cost. Additionally, to further enhance the optimization efficiency and performance of the OFC, a deep learning-based inverse design and analysis approach is adopted.【Results】The study shows that the inverse design based on the constructed cascaded network can identify the corresponding parameters for the target OFC in less than one second. This rapid parameter determination method enables programmability of the number of comb lines, OFC power, and line spacing. It can also generate a 13-line OFC with a flatness of 1.769 dB. This efficient design method provides robust support for the rapid preparation and application of OFCs.【Conclusion】The proposed solution in this study demonstrates significant advantages in OFC generation technology, particularly in performance, flexibility, and optimization efficiency. The method of generating OFC through DDMZM driven by secondary coupled RF signals not only simplifies the system structure and reduces costs but also significantly improves design efficiency through the reverse design approach of deep learning. These characteristics make this solution suitable for a wide range of applications, especially in scenarios requiring quick, efficient, and flexible adjustment of OFC parameters

Facile design of PTFE-kaolin-based ternary nanocomposite as a hydrophobic and high corrosion-barrier coating

Superhydrophobic nanostructured coatings are a promising technology in construction engineering. This study developed a hydrophobic film through a simple mixing method, utilizing kaolin and polytetrafluoroethylene as additive particles, 1H,1H,2H,2H-perfluoro-decyl triethoxysilane as a modifier, and epoxide resin and polyamide curing agent as adhesives. By controlling variables, it was determined that the C1P0.2EP coating immersed in a 3.5 wt% NaCl solution for 1, 3, and 7 days exhibited the maximum impedance radii of 47,373, 20,334, and 1,982 Ω·cm2, respectively. It also demonstrated the highest Bode modulus values, the largest E corr, and the smallest I corr. Furthermore, after 300 h in a salt spray chamber with a 3.5 wt% NaCl solution, the C1P0.2EP coating showed no rust spots or bubbles, demonstrating its excellent corrosion resistance. Moreover, wear resistance tests and self-cleaning experiments were conducted on the C1P0.2EP coating. The results showed that after 100 friction cycles, the surface exhibited no visible scratches, and the contact angle of the coating decreased by only 4°. Additionally, neither soil particles nor dirty water adhered to the coating, indicating that the C1P0.2EP hydrophobic coating possesses not only excellent corrosion resistance but also superior wear resistance and self-cleaning capabilities

Ultrashort echo time magnetization transfer (UTE-MT) imaging and modeling: magic angle independent biomarkers of tissue properties.

MRI biomarkers such as T2 , T2 * and T1rho have been widely used, but are confounded by the magic angle effect. The purpose of this study is to investigate the use of the two-dimensional ultrashort echo time magnetization transfer (UTE-MT) sequence for potential magic angle independent MR biomarkers. Magnetization transfer was investigated in cadaveric Achilles tendon samples using the UTE-MT sequence at five MT powers and five frequency offsets ranging from 2 to 50 kHz. The protocol was applied at five sample orientations ranging from 0 to 90° relative to the B0 field. The results were analyzed with a two-pool quantitative MT model. Multiple TE data were also acquired and mono-exponential T2 * was calculated for each orientation. Macromolecular proton fractions and exchange rates derived from UTE-MT modeling did not appreciably change between the various orientations, whereas the T2 * relaxation time demonstrated up to a sixfold increase from 0° to 55°. The UTE-MT technique with two-pool modeling shows promise as a clinically compatible technique that is resistant to the magic angle effect. This method provides information on the macromolecular proton pool that cannot be directly obtained by other methods, including regular UTE techniques

Pursuit Path Planning for Multiple Unmanned Ground Vehicles Based on Deep Reinforcement Learning

Path planning plays a crucial role in the execution of pursuit tasks for multiple unmanned ground vehicles (multi-UGVs). Although existing popular path-planning methods can achieve the pursuit goals, they suffer from some drawbacks such as long computation time and excessive path inflection points. To address these issues, this paper combines gradient descent and deep reinforcement learning (DRL) to solve the problem of excessive path inflection points from a path-smoothing perspective. In addition, the prioritized experience replay (PER) method is incorporated to enhance the learning efficiency of DRL. By doing so, the proposed model integrates PER, gradient descent, and a multiple-agent double deep Q-learning network (PER-GDMADDQN) to enable the path planning and obstacle avoidance capabilities of multi-UGVs. Experimental results demonstrate that the proposed PER-GDMADDQN yields superior performance in the pursuit problem of multi-UGVs, where the training speed and smoothness of the proposed method outperform other popular algorithms. As a result, the proposed method enables satisfactory path planning for multi-UGVs