81 research outputs found
A Study on the Curriculum Setting and Characteristics of the Undergraduate Philosophy Major at Oxford University
The philosophy faculty at Oxford University is ancient and stately, with profound cultural background and a good tradition of philosophical concept of education and training target, which influences the philosophical education in Britain and even in the whole world. By cultivating the studentsâ ability of reading, logical thinking and critical thinking, it encourages students to correctly understand the world and use the knowledge effectively to solve various practical problems. This article tries to sort out the development of undergraduatesâ education of philosophy at Oxford University, to analyze the curriculum setting of philosophy in the latest ten years, and to summarize the characteristics of philosophy education
A Dataset of Open-Domain Question Answering with Multiple-Span Answers
Multi-span answer extraction, also known as the task of multi-span question
answering (MSQA), is critical for real-world applications, as it requires
extracting multiple pieces of information from a text to answer complex
questions. Despite the active studies and rapid progress in English MSQA
research, there is a notable lack of publicly available MSQA benchmark in
Chinese. Previous efforts for constructing MSQA datasets predominantly
emphasized entity-centric contextualization, resulting in a bias towards
collecting factoid questions and potentially overlooking questions requiring
more detailed descriptive responses. To overcome these limitations, we present
CLEAN, a comprehensive Chinese multi-span question answering dataset that
involves a wide range of open-domain subjects with a substantial number of
instances requiring descriptive answers. Additionally, we provide established
models from relevant literature as baselines for CLEAN. Experimental results
and analysis show the characteristics and challenge of the newly proposed CLEAN
dataset for the community. Our dataset, CLEAN, will be publicly released at
zhiyiluo.site/misc/clean_v1.0_ sample.json
The Exploration of Philosophy Teaching Organization Form at Oxford University
Philosophy educational goals of Oxford University are that by reading and training regularly to enlighten studentsâ mind, cultivate their reading ability, critical thinking and logical thinking ability, etc.. At Oxford University, cultivating studentsâ ability depends on rich teaching organizational forms. Teaching organizational forms affects the teaching quality and efficiency directly, and relates to the realization of educational objectives. Oxford University philosophy teaching takes various teaching organizational forms, which are lectures, tutorial system, seminars and presentations. These teaching organizational forms not only make Oxford University achieve its educational goals, but also become its features enjoying a worldwide reputation. This paper attempts to elaborate and analyze the four teaching organizational forms, to grasp its specific implementation process, characteristics as well as values, and to reflect on its enlightenments for Chinese university teaching organizational forms
Revealing the two-dimensional electronic structure and anisotropic superconductivity in a natural van der Waals superlattice (PbSe)NbSe
Van der Waals superlattices are important for tailoring the electronic
structures and properties of layered materials. Here we report the
superconducting properties and electronic structure of a natural van der Waals
superlattice (PbSe)NbSe. Anisotropic superconductivity with a
transition temperature = 5.6 0.1 K, which is higher than monolayer
NbSe, is revealed by transport measurements on high-quality samples.
Angle-resolved photoemission spectroscopy (ARPES) measurements reveal the
two-dimensional electronic structure and a charge transfer of 0.43 electrons
per NbSe unit cell from the blocking PbSe layer. In addition,
polarization-dependent ARPES measurements reveal a significant circular
dichroism with opposite contrast at K and K' valleys, suggesting a significant
spin-orbital coupling and distinct orbital angular momentum. Our work suggests
natural van der Waals superlattice as an effective pathway for achieving
intriguing properties distinct from both the bulk and monolayer samples.Comment: 8 pages, 4 figure
A Fast Radio Burst Discovered in FAST Drift Scan Survey
We report the discovery of a highly dispersed fast radio burst (FRB), FRB 181123, from an analysis of ~1500 hr of drift scan survey data taken using the Five-hundred-meter Aperture Spherical radio Telescope (FAST). The pulse has three distinct emission components, which vary with frequency across our 1.0â1.5 GHz observing band. We measure the peak flux density to be... (See full abstract in article)
Multitask Fine Tuning on Pretrained Language Model for Retrieval-Based Question Answering in Automotive Domain
Retrieval-based question answering in the automotive domain requires a model to comprehend and articulate relevant domain knowledge, accurately understand user intent, and effectively match the required information. Typically, these systems employ an encoderâretriever architecture. However, existing encoders, which rely on pretrained language models, suffer from limited specialization, insufficient awareness of domain knowledge, and biases in user intent understanding. To overcome these limitations, this paper constructs a Chinese corpus specifically tailored for the automotive domain, comprising questionâanswer pairs, document collections, and multitask annotated data. Subsequently, a pretrainingâmultitask fine-tuning framework based on masked language models is introduced to integrate domain knowledge as well as enhance semantic representations, thereby yielding benefits for downstream applications. To evaluate system performance, an evaluation dataset is created using ChatGPT, and a novel retrieval task evaluation metric called mean linear window rank (MLWR) is proposed. Experimental results demonstrate that the proposed system (based on BERTbase), achieves accuracies of 77.5% and 84.75% for Hit@1 and Hit@3, respectively, in the automotive domain retrieval-based question-answering task. Additionally, the MLWR reaches 87.71%. Compared to a system utilizing a general encoder, the proposed multitask fine-tuning strategy shows improvements of 12.5%, 12.5%, and 28.16% for Hit@1, Hit@3, and MLWR, respectively. Furthermore, when compared to the best single-task fine-tuning strategy, the enhancements amount to 0.5%, 1.25%, and 0.95% for Hit@1, Hit@3, and MLWR, respectively
A Convolutional Sequence-to-Sequence Attention Fusion Framework for Commonsense Causal Reasoning
Commonsense causal reasoning is the process of understanding the causal dependency between common events or actions. Traditionally, it was framed as a selection problem. However, we cannot obtain enough candidates and need more flexible causes (or effects) in many scenarios, such as causal-based QA problems. Thus, the ability to generate causes (or effects) is an important problem. In this paper, we propose a causal attention mechanism that leverages external knowledge from CausalNet, followed by a novel fusion mechanism that combines global causal dependency guidance from the causal attention with local causal dependency obtained through multi-layer soft attention within the CNN seq2seq architecture. Experimental results consistently demonstrate the superiority of the proposed framework, achieving BLEU-1 scores of 20.06 and 36.94, BLEU-2 scores of 9.98 and 27.78, and human-evaluated accuracy rates of 35% and 52% for two evaluation datasets, outperforming all other baselines across all metrics on both evaluation datasets
Distributed Cross-Domain Optimization for Software Defined Industrial Internet of Things
As a promising paradigm, the Industrial Internet of Things (IIoT) provides a wide range of intelligent services through the interconnection and interaction of heterogeneous networks. The quality of these services depends on how the bandwidth is shared among different flows. Hence, it is critical to design a flexible flow control strategy in multi-region management scenarios. In this paper, we establish a flow optimization model based on the IIoT networks managed by multiple Software-Defined Networking (SDN) controllers. Specifically, it jointly optimizes the real-time delivery, route selection, and constrained resource allocation to maximize the total utilities of domains. Since the topology and resources within each domain are kept secret, the problem model belongs to a multi-block problem with coupling constraints, which is difficult to be solved directly. To this end, we first decompose the problem into several intra-domain subproblems, which can be solved in parallel. By considering the inter-domain communication problem, we then introduce the slack variables to implement the interaction among domains. Finally, we design a distributed Proximal Symmetric Alternating Direction Method of Multipliers (Prox-SADMM) algorithm to solve the above joint optimization problem. Through numerical simulations, we investigate the impact of data timeliness, multi-path routing, and resource constraints on the rate utility. The performance analysis confirms that the Prox-SADMM algorithm can be well applied to large-scale networks and provides guidance to set appropriate parameter values according to the realistic requirements of IIoT networks
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