Creating a visual music interface via emotion detection by Lim, Clement Shi Hong.

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How to Hold a Grudge : From Resentment to Contentment- the Power of Grudges to Transform Your Life / by Hannah, Sophie, 1971- author 649175

“…Internationally bestselling author and grudge guru Sophie Hannah has elevated the practice of grudge-holding to a fine art--one that can improve the quality of our relationships at home, at the office, and everywhere else people might annoy or offend us. …”

Single antenna power measurements based direction finding : an FRI solution by Zhang, Song Bai

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Suction and strength of unsaturated soils by Bulolo, Sam

“…Lastly, Brazilian tensile strength tests were conducted, and a new tensile strength model was developed for both coarse and fine-grained soils.…”
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Investigation of the contribution of bioaerosol loading to indoor PM pollution on university campus in Singapore by Lee, Brandon Jia Hao

“…Fine particles could be mitigated safely for occupants with an electrostatic precipitator and future work could expound on elevated fungi concentrations in an indoor environment and fine particle mitigation strategies.…”
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Interpreting learned feedback patterns in large language models by Marks, L, Abdullah, A, Neo, C, Arike, R, Krueger, D, Torr, P, Barez, F

“…To test this, we train probes to estimate the feedback signal implicit in the activations of a fine-tuned LLM. We then compare these estimates to the true feedback, measuring how accurate the LFPs are to the fine-tuning feedback. …”

An Energy and Area Estimation Plugin for Accelerator Architecture Simulation by Wu, Wendy

“…Accelergy is a tool that provides a good general framework for fine-grained energy and area estimation. However, output from simulation tools may not be compatible with Accelergy’s expected input format, which is the case for the custom simulator Accelsim. …”
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Genetic algorithm based deep learning model adaptation for improvising the motor imagery classification by R, Vishnupriya, Robinson, Neethu, M, Ramasubba Reddy

“…Transfer learning methods such as adaptation or fine-tuning can be used on the pre-trained model to improve the performance. …”
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Soil-water characteristic curves of sands by evaporation method by Lee, Yong Wei

“…The comparison shows that low-cost resistive moisture sensors are more reliable than low-cost capacitive sensors. The SWCCs of fine and coarse sands determined from the evaporation method are comparable to the SWCCs determined from the Tempe cell method.…”
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SimSC: a simple framework for semantic correspondence with temperature learning by Li, X, Han, K, Wan, X, Prisacariu, VA

“…We discover that when fine-tuning ImageNet pre-trained backbone on the semantic matching task, L2 normalization of the feature map, a standard procedure in feature matching, produces an overly smooth matching distribution and significantly hinders the fine-tuning process. …”

Enhancing Roll Form Bending Processes through Experimentation and Informed Predictive Analysis: A Strategic Approach to Optimize Tooling by Kompella, Sarvagnya

“…Stress and strain analyses demonstrated how operational factors, such as roll settings, influence section shapes and angles, facilitating the fine-tuning of roll forming station parameters. Using a Design of Experiments (DOE) framework, the study pinpointed critical factors to improve simulation accuracy and optimize roll forming settings. …”
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Exploiting irregular parallelism to accelerate FPGA routing by Zhu, Alan Y.

“…This parallelism can be exploited on parallel architectures that provide hardware support for ordered tasks and fine-grained speculation, such as the Swarm architecture. …”
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Generating domain-specific paraphrases of questions from FAQ by Ng, Jing Rui

“…Firstly, T5 is used to fine-tune on the paraphrase dataset for the task of paraphrase generation. …”
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Red Alarm for Pre-trained Models: Universal Vulnerability to Neuron-level Backdoor Attacks by Zhang, Zhengyan, Xiao, Guangxuan, Li, Yongwei, Lv, Tian, Qi, Fanchao, Liu, Zhiyuan, Wang, Yasheng, Jiang, Xin, Sun, Maosong

“…The pre-training-then-fine-tuning paradigm has been widely used in deep learning. …”
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Technological transmission of knowledge in Neolithic northwestern China: mineralogical and chemical analyses of Yangshao and Majiayao painted ware by Spataro, M, Hein, A

“…The analyses were carried out to detect differences or similarities in the chaînes opératoires of ceramic production at the three sites, in particular, to examine the clay types selected to make the three main categories of wares for which Yangshao and Majiayao are known, namely painted fine wares, burnished fine wares, and unpainted coarse wares. …”

WiFi and vision multimodal self-supervised learning for human activity recognition by Tang, Shijie

“…In this context, we propose a novel Multimodal Pretrain fine-tune (MPF) network architecture for device-free human activity recognition using commercial WiFi-enabled IoT devices and cameras. …”
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Utilization of oil contaminated sand for green asphalt concrete production by Ng, Ye Han

“…The objective of this research is to study the feasibility of Oil Contaminated Sand (OCS) usage as a replacement of certain percentage of fine aggregates in Hot Mix Asphalt (HMA) for road construction. …”
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Properties of copper slag concrete by Zhang, Weide

“…This report investigated the mechanical properties of high strength concrete incorporating copper slag as fine aggregates. The rising need for alternative fine aggregates substitution of sand is essential due to its high materials cost and the emphasis on sustainable concrete. …”
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Neural abstractive summarization: improvements at the sequence-level by Ravaut, Mathieu

“…With enough data, pre-trained Transformer-based sequence-to-sequence models can be fine-tuned and achieve strong performance on abstractive summarization benchmarks. …”
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Leveraging linguistic knowledge to enhance low-resource NLP applications by Zhu, Zixiao

“…Specifically, 1. In the promptless fine-tuning paradigm, we first focus on fine-tuning word embeddings for task-related words, thereby enriching the conceptual knowledge available to compositional neural networks during feature learning in emotion recognition. …”
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