The split-step backward Euler method for linear stochastic delay differential equations

AbstractIn this paper, the numerical approximation of solutions of linear stochastic delay differential equations (SDDEs) in the Itô sense is considered. We construct split-step backward Euler (SSBE) method for solving linear SDDEs and develop the fundamental numerical analysis concerning its strong convergence and mean-square stability. It is proved that the SSBE method is convergent with strong order γ=12 in the mean-square sense. The conditions under which the SSBE method is mean-square stable (MS-stable) and general mean-square stable (GMS-stable) are obtained. Some illustrative numerical examples are presented to demonstrate the order of strong convergence and the mean-square stability of the SSBE method

G2PTL: A Pre-trained Model for Delivery Address and its Applications in Logistics System

Text-based delivery addresses, as the data foundation for logistics systems, contain abundant and crucial location information. How to effectively encode the delivery address is a core task to boost the performance of downstream tasks in the logistics system. Pre-trained Models (PTMs) designed for Natural Language Process (NLP) have emerged as the dominant tools for encoding semantic information in text. Though promising, those NLP-based PTMs fall short of encoding geographic knowledge in the delivery address, which considerably trims down the performance of delivery-related tasks in logistic systems such as Cainiao. To tackle the above problem, we propose a domain-specific pre-trained model, named G2PTL, a Geography-Graph Pre-trained model for delivery address in Logistics field. G2PTL combines the semantic learning capabilities of text pre-training with the geographical-relationship encoding abilities of graph modeling. Specifically, we first utilize real-world logistics delivery data to construct a large-scale heterogeneous graph of delivery addresses, which contains abundant geographic knowledge and delivery information. Then, G2PTL is pre-trained with subgraphs sampled from the heterogeneous graph. Comprehensive experiments are conducted to demonstrate the effectiveness of G2PTL through four downstream tasks in logistics systems on real-world datasets. G2PTL has been deployed in production in Cainiao's logistics system, which significantly improves the performance of delivery-related tasks

Development of a Reliable and Accessible Caregiving Language Model (CaLM)

Unlike professional caregivers, family caregivers often assume this role without formal preparation or training. Because of this, there is an urgent need to enhance the capacity of family caregivers to provide quality care. Large language models can potentially be used as a foundation technology for supporting caregivers as educational tools or as adjunct to care. This study aimed to develop a reliable Caregiving Language Model (CaLM) by using FMs and a caregiving knowledge base, develop an accessible CaLM using a small FM that requires fewer computing resources, and evaluate the performance of the model compared to a large FM. We developed CaLM using the Retrieval Augmented Generation (RAG) framework combined with FM fine-tuning for improving the quality of FM answers by grounding the model on a caregiving knowledge base. We used two small FMs as candidates for the FM of CaLM (LLaMA-2 and Falcon with 7B parameters) and larger FM GPT-3.5 as a benchmark. We developed the caregiving knowledge base by gathering various types of documents from the Internet. In this study, we focused on caregivers of individuals with Alzheimer's Disease Related Dementias. We evaluated the models' performance using the benchmark metrics commonly used in evaluating language models and their reliability to provide accurate references with the answers. The RAG framework improved the performance of all FMs used in this study across all measures. As expected, the large FM performed better than small FMs across all metrics. The most interesting result is that small fine-tuned FMs with RAG performed significantly better than GPT 3.5 across all metrics. The fine-tuned LLaMA-2 small FM performed better than GPT 3.5 (even with RAG) in returning references with the answers. The study shows that reliable and accessible CaLM can be developed by using small FMs with a knowledge base specific to the caregiving domain

Accelerating voxelwise annotation of cross-sectional imaging through AI collaborative labeling with quality assurance and bias mitigation

Backgroundprecision-medicine quantitative tools for cross-sectional imaging require painstaking labeling of targets that vary considerably in volume, prohibiting scaling of data annotation efforts and supervised training to large datasets for robust and generalizable clinical performance. A straight-forward time-saving strategy involves manual editing of AI-generated labels, which we call AI-collaborative labeling (AICL). Factors affecting the efficacy and utility of such an approach are unknown. Reduction in time effort is not well documented. Further, edited AI labels may be prone to automation bias.PurposeIn this pilot, using a cohort of CTs with intracavitary hemorrhage, we evaluate both time savings and AICL label quality and propose criteria that must be met for using AICL annotations as a high-throughput, high-quality ground truth.Methods57 CT scans of patients with traumatic intracavitary hemorrhage were included. No participant recruited for this study had previously interpreted the scans. nnU-net models trained on small existing datasets for each feature (hemothorax/hemoperitoneum/pelvic hematoma; n = 77–253) were used in inference. Two common scenarios served as baseline comparison- de novo expert manual labeling, and expert edits of trained staff labels. Parameters included time effort and image quality graded by a blinded independent expert using a 9-point scale. The observer also attempted to discriminate AICL and expert labels in a random subset (n = 18). Data were compared with ANOVA and post-hoc paired signed rank tests with Bonferroni correction.ResultsAICL reduced time effort 2.8-fold compared to staff label editing, and 8.7-fold compared to expert labeling (corrected p < 0.0006). Mean Likert grades for AICL (8.4, SD:0.6) were significantly higher than for expert labels (7.8, SD:0.9) and edited staff labels (7.7, SD:0.8) (corrected p < 0.0006). The independent observer failed to correctly discriminate AI and human labels.ConclusionFor our use case and annotators, AICL facilitates rapid large-scale curation of high-quality ground truth. The proposed quality control regime can be employed by other investigators prior to embarking on AICL for segmentation tasks in large datasets

Non-selective language activation in L2 lexical inference and text comprehension: Comparing skilled and less-skilled readers

Non-selective language activation refers to the automatic co-activation of L1 and L2 information. In L2 reading, the activated L1 information can be utilized to different degrees to facilitate lexical inference and text comprehension. The current study examined the contributions of L1-L2 translation and lexical inference to text comprehension. Hierarchical regression models showed that in general, lexical inference contributed to text comprehension over L1-L2 translation. The results indicated that L2 learners did not use activated L1 information mechanically. That is because successful lexical inference incorporates learners’ ability to strategically utilize contextual information and integrate word meanings to update the context. The study further classified the participants into two groups using k-means cluster. Among the less skilled group of participants, L1-L2 translation was related to both lexical inference and text comprehension. However, lexical inference was not significantly related to text comprehension. Among the more skilled group of participants, lexical inference predicted text comprehension only after school, grade to start English learning, and L1-L2 translation were controlled for. The results of the two groups demonstrated that while L1 information was utilized in both groups, strategic and effective usage of information in two languages differentiated skilled L2 readers from less skilled L2 readers

Design and Application of Queue-Buffer Communication Model in Pneumatic Conveying

U ovom radu je opisana javna rasvjeta, odnosno njena funkcija i svi osnovni elementi. Potom je projektirana rasvjeta parkirališta ispred zgrada fakulteta te je izveden proračun utvrđivanja rasvijetljenosti tog parkirališta. Izveden je izračun isplativosti otočnog fotonaponskog sustava te su iznosi uspoređeni sa cijenom električne energije uzetom iz elektroenergetske mreže i električnom energijom dobivenom iz aktivnog fotonaponskog sustava. Opisan je način instalacije pametne rasvjete na projektirano parkiralište.This paper describes public street lightning system, the function of public street lightning system and all the basic elements of public street lightning. Street lightning of a parking is designed and a calculation of illumination distribution was made for the designed parking. The calculation of profitability of off-grid photovoltaic system was made and the results were compared with the price of electrical energy taken from the installed power grid and with electrical energy received from the active photovoltaic system. The paper contains an application of smart street lightning on a designed parking