268 research outputs found
Do Narcissists Enjoy Visiting Social Networking Sites? It Depends on How Adaptive They Are
Previous evidence suggests that narcissistic people tend to visit social networking sites (SNS) frequently, but the emotions accompanying their engagement on such sites has not been a significant subject of study. Therefore, we examined the relationship between narcissism and the affective experience on SNS in two different samples. To do so, we not only examined narcissism as a whole but also distinguished between adaptive and maladaptive narcissism. Results of the two studies consistently showed that: (1) narcissism as a whole was not correlated with the SNS affective experience; (2) maladaptive narcissism was predictive of a worse affective experience on SNS; and (3) partly due to a positive correlation with self-esteem, adaptive narcissism was associated with a better SNS affective experience. In addition, these findings held with SNS activities considered in simultaneity. The present research extends our understanding of the relationship between narcissism and social networking as well as that between emotion and social networking
CTVIS: Consistent Training for Online Video Instance Segmentation
The discrimination of instance embeddings plays a vital role in associating
instances across time for online video instance segmentation (VIS). Instance
embedding learning is directly supervised by the contrastive loss computed upon
the contrastive items (CIs), which are sets of anchor/positive/negative
embeddings. Recent online VIS methods leverage CIs sourced from one reference
frame only, which we argue is insufficient for learning highly discriminative
embeddings. Intuitively, a possible strategy to enhance CIs is replicating the
inference phase during training. To this end, we propose a simple yet effective
training strategy, called Consistent Training for Online VIS (CTVIS), which
devotes to aligning the training and inference pipelines in terms of building
CIs. Specifically, CTVIS constructs CIs by referring inference the
momentum-averaged embedding and the memory bank storage mechanisms, and adding
noise to the relevant embeddings. Such an extension allows a reliable
comparison between embeddings of current instances and the stable
representations of historical instances, thereby conferring an advantage in
modeling VIS challenges such as occlusion, re-identification, and deformation.
Empirically, CTVIS outstrips the SOTA VIS models by up to +5.0 points on three
VIS benchmarks, including YTVIS19 (55.1% AP), YTVIS21 (50.1% AP) and OVIS
(35.5% AP). Furthermore, we find that pseudo-videos transformed from images can
train robust models surpassing fully-supervised ones.Comment: Accepted by ICCV 2023. The code is available at
https://github.com/KainingYing/CTVI
Effects of yeast extract supplemented in diet on growth performance, digestibility, intestinal histology, and the antioxidant capacity of the juvenile turbot (Scophthalmus maximus)
An 8-week feeding experiment was conducted on the juvenile turbot (Scophthalmus maximus) to evaluate the influence of yeast extract (YE) supplementation in the diet on growth performance, feed utilization, body composition, nutrient digestibility, intestinal histology, and antioxidant capacity. Four experimental diets were formulated with graded levels of yeast extract 0 (YE0), 1% (YE1), 3% (YE3), and 5% (YE5) and fed to turbots (initial body weight: 4.2 ± 0.1 g) with three replicates per diet and 200 fish in each replicate, respectively. The results showed that turbots fed with diets YE1 and YE3 displayed a significantly higher specific growth rate and protein efficiency rate than those fed with diets YE0 and YE5, while the feed conversion ratios in YE1 and YE3 groups were lower than those in YE0 and YE5. Fish fed with diets YE3 and YE5 showed higher body crude protein contents than those in groups YE0 and YE1. The highest apparent digestibility coefficients for dry matter and crude protein, digestive enzyme activities (trypsin, lipase, and amylase), and the height of the intestinal fold were observed in the YE3 group. YE3 treatment displayed a significantly higher superoxide dismutase (SOD) activity than the YE0 group, while the malondialdehyde (MDA) content in YE1 was significantly lower than those in YE0 and YE5. No significant difference was observed in serum physiological and biochemical parameters among all treatments. Overall, appropriate dietary supplementation of the yeast extract could improve the growth performance, digestibility, and antioxidant capacity of the juvenile turbot, and the recommended yeast extract level in the feed is 2.47%
EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Models
Large Language Models (LLMs) usually suffer from knowledge cutoff or fallacy
issues, which means they are unaware of unseen events or generate text with
incorrect facts owing to the outdated/noisy data. To this end, many knowledge
editing approaches for LLMs have emerged -- aiming to subtly inject/edit
updated knowledge or adjust undesired behavior while minimizing the impact on
unrelated inputs. Nevertheless, due to significant differences among various
knowledge editing methods and the variations in task setups, there is no
standard implementation framework available for the community, which hinders
practitioners to apply knowledge editing to applications. To address these
issues, we propose EasyEdit, an easy-to-use knowledge editing framework for
LLMs. It supports various cutting-edge knowledge editing approaches and can be
readily apply to many well-known LLMs such as T5, GPT-J, LlaMA, etc.
Empirically, we report the knowledge editing results on LlaMA-2 with EasyEdit,
demonstrating that knowledge editing surpasses traditional fine-tuning in terms
of reliability and generalization. We have released the source code on GitHub
at https://github.com/zjunlp/EasyEdit, along with Google Colab tutorials and
comprehensive documentation for beginners to get started. Besides, we present
an online system for real-time knowledge editing, and a demo video at
http://knowlm.zjukg.cn/easyedit.mp4.Comment: The project website is https://github.com/zjunlp/EasyEdi
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Technical Support Document: 50% Energy Savings for Small Office Buildings
The Technical Support Document (TSD) for 50% energy savings in small office buildings documents the analysis and results for a recommended package of energy efficiency measures (EEMs) referred to as the advanced EEMs. These are changes to a building design that will reduce energy usage. The package of advanced EEMs achieves a minimum of 50% energy savings and a construction area weighted average energy savings of 56.6% over the ANSI/ASHRAE/IESNA Standard 90.1-2004 for 16 cities which represent the full range of climate zones in the United States. The 50% goal is for site energy usage reduction. The weighted average is based on data on the building area of construction in the various climate locations. Cost-effectiveness of the EEMs is determined showing an average simple payback of 6.7 years for all 16 climate locations. An alternative set of results is provided which includes a variable air volume HVAC system that achieves at least 50% energy savings in 7 of the 16 climate zones with a construction area weighted average savings of 48.5%. Other packages of EEMs may also achieve 50% energy savings; this report does not consider all alternatives but rather presents at least one way to reach the goal. Design teams using this TSD should follow an integrated design approach and utilize additional analysis to evaluate the specific conditions of a project
Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity
This survey addresses the crucial issue of factuality in Large Language
Models (LLMs). As LLMs find applications across diverse domains, the
reliability and accuracy of their outputs become vital. We define the
Factuality Issue as the probability of LLMs to produce content inconsistent
with established facts. We first delve into the implications of these
inaccuracies, highlighting the potential consequences and challenges posed by
factual errors in LLM outputs. Subsequently, we analyze the mechanisms through
which LLMs store and process facts, seeking the primary causes of factual
errors. Our discussion then transitions to methodologies for evaluating LLM
factuality, emphasizing key metrics, benchmarks, and studies. We further
explore strategies for enhancing LLM factuality, including approaches tailored
for specific domains. We focus two primary LLM configurations standalone LLMs
and Retrieval-Augmented LLMs that utilizes external data, we detail their
unique challenges and potential enhancements. Our survey offers a structured
guide for researchers aiming to fortify the factual reliability of LLMs.Comment: 62 pages; 300+ reference
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