Addressing variability as an expansion of naturalistic lighting theory for user wellbeing

This study is an exploration into the relationship between lighting and office occupant productivity and wellbeing, attempting to better understand how enhanced naturalistic lighting and lighting control might enable an environment that affects occupants positively. To explore the possibilities of this concept, a morphological research approach has been implemented to ultimately integrate the following three major lighting developments; human affinity to nature; accommodation of physiological, functional, and psychological aspects; and acknowledgement of the inherent need for variability and evolution. This study consisted mainly of two segments. First, through the review of literature, three key lighting-oriented developments have been identified; human affinity to nature; accommodation of physiological, functional, and psychological aspects; and acknowledgement of the inherent need for variability and evolution. No lighting solution that integrates all these factors has yet been found. Second, the study introduces the concept of enhanced naturalistic lighting and its control schematic, holistically combining all three of these key developments. Future exploration of interior design implications related to enhanced naturalistic lighting and associated control systems will be discussed to clarify how such lighting systems could impact the wellbeing of the users

Contrastive Demonstration Tuning for Pre-trained Language Models

Pretrained language models can be effectively stimulated by textual prompts or demonstrations, especially in low-data scenarios. Recent works have focused on automatically searching discrete or continuous prompts or optimized verbalizers, yet studies for the demonstration are still limited. Concretely, the demonstration examples are crucial for an excellent final performance of prompt-tuning. In this paper, we propose a novel pluggable, extensible, and efficient approach named contrastive demonstration tuning, which is free of demonstration sampling. Furthermore, the proposed approach can be: (i) Plugged to any previous prompt-tuning approaches; (ii) Extended to widespread classification tasks with a large number of categories. Experimental results on 16 datasets illustrate that our method integrated with previous approaches LM-BFF and P-tuning can yield better performance. Code is available in https://github.com/zjunlp/PromptKG/tree/main/research/Demo-Tuning.Comment: Work in progres

Qwen Technical Report

Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to humans. In this work, we introduce Qwen, the first installment of our large language model series. Qwen is a comprehensive language model series that encompasses distinct models with varying parameter counts. It includes Qwen, the base pretrained language models, and Qwen-Chat, the chat models finetuned with human alignment techniques. The base language models consistently demonstrate superior performance across a multitude of downstream tasks, and the chat models, particularly those trained using Reinforcement Learning from Human Feedback (RLHF), are highly competitive. The chat models possess advanced tool-use and planning capabilities for creating agent applications, showcasing impressive performance even when compared to bigger models on complex tasks like utilizing a code interpreter. Furthermore, we have developed coding-specialized models, Code-Qwen and Code-Qwen-Chat, as well as mathematics-focused models, Math-Qwen-Chat, which are built upon base language models. These models demonstrate significantly improved performance in comparison with open-source models, and slightly fall behind the proprietary models.Comment: 59 pages, 5 figure

Ppm1b negatively regulates necroptosis through dephosphorylating ​Rip3

该研究论文发现蛋白磷酸酶Ppm1b 通过去磷酸化RIP3负调控程序性细胞坏死（necroptosis），阐明了RIP3磷酸化状态的精确调控对于细胞和机体在生理和病理状态下的存活至关重要。The auto-phosphorylation of murine ​receptor-interacting protein 3 (​Rip3) on Thr 231 and Ser 232 in the necrosome is required to trigger necroptosis. However, how ​Rip3 phosphorylation is regulated is still largely unknown. Here we identified ​protein phosphatase 1B (​Ppm1b) as a ​Rip3 phosphatase and found that ​Ppm1b restricts necroptosis in two settings: spontaneous necroptosis caused by ​Rip3 auto-phosphorylation in resting cells, and ​tumour necrosis factor-α (​TNF)-induced necroptosis in cultured cells. We revealed that ​Ppm1b selectively suppresses necroptosis through the dephosphorylation of ​Rip3, which then prevents the recruitment of ​mixed lineage kinase domain-like protein (​Mlkl) to the necrosome. We further showed that ​Ppm1b deficiency (​Ppm1bd/d) in mice enhanced ​TNF-induced death in a ​Rip3-dependent manner, and the role of ​Ppm1b in inhibiting necroptosis was evidenced by elevated ​Rip3 phosphorylation and tissue damage in the caecum of ​TNF-treated ​Ppm1bd/d mice. These data indicate that ​Ppm1b negatively regulates necroptosis through dephosphorylating ​Rip3 in vitro and in vivo

Addressing variability as an expansion of naturalistic lighting theory for user wellbeing

This study is an exploration into the relationship between lighting and office occupant productivity and wellbeing, attempting to better understand how enhanced naturalistic lighting and lighting control might enable an environment that affects occupants positively. To explore the possibilities of this concept, a morphological research approach has been implemented to ultimately integrate the following three major lighting developments; human affinity to nature; accommodation of physiological, functional, and psychological aspects; and acknowledgement of the inherent need for variability and evolution. This study consisted mainly of two segments. First, through the review of literature, three key lighting-oriented developments have been identified; human affinity to nature; accommodation of physiological, functional, and psychological aspects; and acknowledgement of the inherent need for variability and evolution. No lighting solution that integrates all these factors has yet been found. Second, the study introduces the concept of enhanced naturalistic lighting and its control schematic, holistically combining all three of these key developments. Future exploration of interior design implications related to enhanced naturalistic lighting and associated control systems will be discussed to clarify how such lighting systems could impact the wellbeing of the users.</p

Nonuniformly-Rotating Ship Refocusing in SAR Imagery Based on the Bilinear Extended Fractional Fourier Transform

Nonuniformly-rotating ship refocusing is very significant in the marine surveillance of satellite synthetic aperture radar (SAR). The majority of ship imaging algorithms is based on the inverse SAR (ISAR) technique. On the basis of the ISAR technique, several parameter estimation algorithms were proposed for nonuniformly rotating ships. But these algorithms still have problems on cross-terms and noise suppression. In this paper, a refocusing algorithm for nonuniformly rotating ships based on the bilinear extended fractional Fourier transform (BEFRFT) is proposed. The ship signal in a range bin can be modeled as a multicomponent cubic phase signal (CPS) after motion compensation. BEFRFT is a bilinear extension of fractional Fourier transform (FRFT), which can estimate the chirp rates and quadratic chirp rates of CPSs. Furthermore, BEFRFT has excellent performances on cross-terms and noise suppression. The results of simulated data and Gaofen-3 data verify the effectiveness of BEFRFT

MSR2N: Multi-Stage Rotational Region Based Network for Arbitrary-Oriented Ship Detection in SAR Images

In synthetic aperture radar (SAR) images, ships are often arbitrary-oriented and densely arranged in complex backgrounds, posing enormous challenges for ship detection. However, most existing methods detect ships with horizontal bounding boxes, which leads to the redundancy of detected regions. Furthermore, the high Intersection-over-Union (IoU) between two horizontal bounding boxes of densely arranged ships can cause missing detection. In this paper, a multi-stage rotational region based network (MSR2N) is proposed to solve the above problems. In MSR2N, the rotated bounding boxes, which can reduce background noise and prevent missing detection caused by high IoUs, are utilized to represent ship regions. MSR2N consists of three modules: feature pyramid network (FPN), rotational region proposal network (RRPN), and multi-stage rotational detection network (MSRDN). First of all, the FPN is applied to combine high-resolution features with semantically strong features. Second, in RRPN, a rotation-angle-dependent strategy is employed to generate multi-angle anchors which can represent arbitrary-oriented ship regions more felicitously than horizontal anchors. Finally, the MSRDN with three sub-networks is proposed to regress proposals of ship regions stage by stage. Meanwhile, the incrementally increasing IoU thresholds are selected for resampling positive and negative proposals in sequential stages of MSRDN, which eliminates close false positive proposals successively. With the above characteristics, MSR2N is more suitable and robust for ship detection in SAR images. The experimental results on SAR ship detection dataset (SSDD) show that the MSR2N has achieved state-of-the-art performance