113 research outputs found
The Giver’s Ethical Choices—Analysis of The Giver from the Perspective of Ethical Literary Criticism
The Giver is a novel published in 1993 by Lois Lowry, an American female writer. The novel tells a story of a future community where people’s lives are arranged and regulated according to their scientific choices, but at the same time they have lost their freedom and right to choose. Since it was published, the novel has been analyzed and studied by a number of scholars, focusing on the growth of the young protagonist Jonas, the science fiction elements of the novel, and its dystopian perspective. This article analyzes both the scientific choices of the community and the Giver’s ethical choices from the perspective of ethical literary criticism. It reads carefully into the correct ethical choices made by the Giver when facing the incorrect scientific ones, revealing the benevolence, wisdom, courage, and sense of responsibility possessed by the Giver, and highlighting his value as well. The article reiterates that only the choices in line with the ethical selection criteria of “truth, goodness and beauty” of human civilization can be regarded as right ones. At the same time, the article’s research further prompts readers to reflect on the future of humanity and what to do in the face of scientific and ethical choices
Analysis of the Narrative Perspective of Katherine Mansfield’s “The Garden Party”
Katharine Mansfield is a successful female writer in the literary history of the 20th century, who marks a new period of English short stories. She uses tremendous modernistic techniques and digs deep beneath the surface of life to show the causes of human happiness and despair in her works. “The Garden Party” is one of her most famous and representative short stories. Previous studies have mostly focused on its artistic methods, themes and characters, as well as the combination of all, but there are only few studies choosing its narrative perspectives as their study topic. This paper analyzed the narrative perspective in this story, focusing on the use of nonfocalization, internal focalization and covert progression and the effects they have. It is found that the change of ways of focalization combining with covert progression in this story forms a parallel of objective description and ironic description with the plot development, adds a new group to the relationship between the former implied author and target readers, and reveals two different ways (idealistic and realistic) of understanding this story, letting readers reflect on the behaviors of the upper-middle-class people and ironically pointing out their selfish nature
Human Motion Generation: A Survey
Human motion generation aims to generate natural human pose sequences and
shows immense potential for real-world applications. Substantial progress has
been made recently in motion data collection technologies and generation
methods, laying the foundation for increasing interest in human motion
generation. Most research within this field focuses on generating human motions
based on conditional signals, such as text, audio, and scene contexts. While
significant advancements have been made in recent years, the task continues to
pose challenges due to the intricate nature of human motion and its implicit
relationship with conditional signals. In this survey, we present a
comprehensive literature review of human motion generation, which, to the best
of our knowledge, is the first of its kind in this field. We begin by
introducing the background of human motion and generative models, followed by
an examination of representative methods for three mainstream sub-tasks:
text-conditioned, audio-conditioned, and scene-conditioned human motion
generation. Additionally, we provide an overview of common datasets and
evaluation metrics. Lastly, we discuss open problems and outline potential
future research directions. We hope that this survey could provide the
community with a comprehensive glimpse of this rapidly evolving field and
inspire novel ideas that address the outstanding challenges.Comment: 20 pages, 5 figure
Block-Wise Mixed-Precision Quantization: Enabling High Efficiency for Practical ReRAM-based DNN Accelerators
Resistive random access memory (ReRAM)-based processing-in-memory (PIM)
architectures have demonstrated great potential to accelerate Deep Neural
Network (DNN) training/inference. However, the computational accuracy of analog
PIM is compromised due to the non-idealities, such as the conductance variation
of ReRAM cells. The impact of these non-idealities worsens as the number of
concurrently activated wordlines and bitlines increases. To guarantee
computational accuracy, only a limited number of wordlines and bitlines of the
crossbar array can be turned on concurrently, significantly reducing the
achievable parallelism of the architecture.
While the constraints on parallelism limit the efficiency of the
accelerators, they also provide a new opportunity for fine-grained
mixed-precision quantization. To enable efficient DNN inference on practical
ReRAM-based accelerators, we propose an algorithm-architecture co-design
framework called \underline{B}lock-\underline{W}ise mixed-precision
\underline{Q}uantization (BWQ). At the algorithm level, BWQ-A introduces a
mixed-precision quantization scheme at the block level, which achieves a high
weight and activation compression ratio with negligible accuracy degradation.
We also present the hardware architecture design BWQ-H, which leverages the
low-bit-width models achieved by BWQ-A to perform high-efficiency DNN inference
on ReRAM devices. BWQ-H also adopts a novel precision-aware weight mapping
method to increase the ReRAM crossbar's throughput. Our evaluation demonstrates
the effectiveness of BWQ, which achieves a 6.08x speedup and a 17.47x energy
saving on average compared to existing ReRAM-based architectures.Comment: 12 pages, 13 figure
Beyond the Obvious: Evaluating the Reasoning Ability In Real-life Scenarios of Language Models on Life Scapes Reasoning Benchmark~(LSR-Benchmark)
This paper introduces the Life Scapes Reasoning Benchmark (LSR-Benchmark), a
novel dataset targeting real-life scenario reasoning, aiming to close the gap
in artificial neural networks' ability to reason in everyday contexts. In
contrast to domain knowledge reasoning datasets, LSR-Benchmark comprises
free-text formatted questions with rich information on real-life scenarios,
human behaviors, and character roles. The dataset consists of 2,162 questions
collected from open-source online sources and is manually annotated to improve
its quality. Experiments are conducted using state-of-the-art language models,
such as gpt3.5-turbo and instruction fine-tuned llama models, to test the
performance in LSR-Benchmark. The results reveal that humans outperform these
models significantly, indicating a persisting challenge for machine learning
models in comprehending daily human life
Xiezhi: An Ever-Updating Benchmark for Holistic Domain Knowledge Evaluation
New Natural Langauge Process~(NLP) benchmarks are urgently needed to align
with the rapid development of large language models (LLMs). We present Xiezhi,
the most comprehensive evaluation suite designed to assess holistic domain
knowledge. Xiezhi comprises multiple-choice questions across 516 diverse
disciplines ranging from 13 different subjects with 220,000 questions and
accompanied by Xiezhi-Specialty and Xiezhi-Interdiscipline, both with 15k
questions. We conduct evaluation of the 47 cutting-edge LLMs on Xiezhi. Results
indicate that LLMs exceed average performance of humans in science,
engineering, agronomy, medicine, and art, but fall short in economics,
jurisprudence, pedagogy, literature, history, and management. We anticipate
Xiezhi will help analyze important strengths and shortcomings of LLMs, and the
benchmark is released in https://github.com/MikeGu721/XiezhiBenchmark .Comment: Under review of NeurIPS 202
Optimizing DUS testing for Chimonanthus praecox using feature selection based on a genetic algorithm
Chimonanthus praecox is a famous traditional flower in China with high ornamental value. It has numerous varieties, yet its classification is highly disorganized. The distinctness, uniformity, and stability (DUS) test enables the classification and nomenclature of various species; thus, it can be used to classify the Chimonanthus varieties. In this study, flower traits were quantified using an automatic system based on pattern recognition instead of traditional manual measurement to improve the efficiency of DUS testing. A total of 42 features were quantified, including 28 features in the DUS guidelines and 14 new features proposed in this study. Eight algorithms were used to classify wintersweet, and the random forest (RF) algorithm performed the best when all features were used. The classification accuracy of the outer perianth was the highest when the features of the different parts were used for classification. A genetic algorithm was used as the feature selection algorithm to select a set of 22 reduced core features and improve the accuracy and efficiency of the classification. Using the core feature set, the classification accuracy of the RF model improved to 99.13%. Finally, K-means was used to construct a pedigree cluster tree of 23 varieties of wintersweet; evidently, wintersweet was clustered into a single class, which can be the basis for further study of genetic relationships among varieties. This study provides a novel method for DUS detection, variety identification, and pedigree analysis
- …