Did you know: Representative Data We'd like to point out some key statistics from our last post on small study sizes. First of all, the question this article is trying to respond is about the prevalence and extent of small study effects in diagnostic imaging. Reach out to us to know how you can have quick access to millions of diverse medical imaging data and avoid data bias: https://lnkd.in/gVwPPXUB

Dina El-Kassaby, a spokeswoman for Meta, Facebook's parent company, said that the deposition did not mean that the company was failing at security or data access issues. "Our systems are sophisticated and it shouldn't be a surprise that no single company engineer can answer every question about where each piece of user information is stored," she said. "We've built one of the most comprehensive privacy programs to oversee data use across our operations and to carefully manage and protect people's data. We have made -- and continue making -- significant investments to meet our privacy commitments and obligations, including extensive data controls."

Document-level information extraction (IE) tasks have recently begun to be revisited in earnest using the end-to-end neural network techniques that have been successful on their sentence-level IE counterparts. Evaluation of the approaches, however, has been limited in a number of dimensions. In particular, the precision/recall/F1 scores typically reported provide few insights on the range of errors the models make. We build on the work of Kummerfeld and Klein (2013) to propose a transformation-based framework for automating error analysis in document-level event and (N-ary) relation extraction. We employ our framework to compare two state-of-the-art document-level template-filling approaches on datasets from three domains; and then, to gauge progress in IE since its inception 30 years ago, vs. four systems from the MUC-4 (1992) evaluation.

As political attitudes have diverged ideologically in the United States, political speech has diverged lingusitically. The ever-widening polarization between the US political parties is accelerated by an erosion of mutual understanding between them. We aim to make these communities more comprehensible to each other with a framework that probes community-specific responses to the same survey questions using community language models CommunityLM. In our framework we identify committed partisan members for each community on Twitter and fine-tune LMs on the tweets authored by them. We then assess the worldviews of the two groups using prompt-based probing of their corresponding LMs, with prompts that elicit opinions about public figures and groups surveyed by the American National Election Studies (ANES) 2020 Exploratory Testing Survey. We compare the responses generated by the LMs to the ANES survey results, and find a level of alignment that greatly exceeds several baseline methods. Our work aims to show that we can use community LMs to query the worldview of any group of people given a sufficiently large sample of their social media discussions or media diet.

Washington – Twitter whistleblower Peiter Zatko told the U.S. Congress on Tuesday that the platform ignored his security concerns, in testimony that came as company shareholders greenlit Elon Musk's $44 billion takeover deal. Nearly 99% of the votes cast by stock owners endorsed the agreement with Musk to sell him the tech firm for $54.20 per share, Twitter said in a release. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites. If this does not resolve the issue or you are unable to add the domains to your allowlist, please see this support page.

Some other iOS social apps, including LinkedIn and Snapchat, also use custom browsers but don't appear to inject similar code, according to Krause's analysis tool, which he made available to the public. Twitter, Reddit and others use Apple's browser, they confirmed, which prevents apps from observing people's activity after they open outside links. A spokeswoman for Twitter said the company switched to Apple's tool in part to protect user privacy.

Extreme heat that exhausted California's overworked electric grid on Labor Day had knocked out one of Twitter's main data centers in Sacramento, according to a report. While Twitter avoided a shutdown on Sept. 5 by leaning on its other data centers in Portland, Ore., and Atlanta during the outage to keep its systems running, a company executive warned that if another center were lost, some users would have been unable to access the social media platform, according to an internal memo obtained by CNN. Temperatures in Sacramento on Labor Day broke a daily record of 114 degrees, punching thermometers up to 116 by the afternoon. To power their online services to users, tech companies such as Twitter, Google, or Meta lean on data centers that can demand heavy loads of power and often generate large amounts of heat, requiring cooling systems to keep things running. As climate change continues to heat the planet, Twitter's outage underscores how such extreme weather impacts the online systems that billions of people rely on daily.

Multimodal sentiment analysis and depression estimation are two important research topics that aim to predict human mental states using multimodal data. Previous research has focused on developing effective fusion strategies for exchanging and integrating mind-related information from different modalities. Some MLP-based techniques have recently achieved considerable success in a variety of computer vision tasks. Inspired by this, we explore multimodal approaches with a feature-mixing perspective in this study. To this end, we introduce CubeMLP, a multimodal feature processing framework based entirely on MLP. CubeMLP consists of three independent MLP units, each of which has two affine transformations. CubeMLP accepts all relevant modality features as input and mixes them across three axes. After extracting the characteristics using CubeMLP, the mixed multimodal features are flattened for task predictions. Our experiments are conducted on sentiment analysis datasets: CMU-MOSI and CMU-MOSEI, and depression estimation dataset: AVEC2019. The results show that CubeMLP can achieve state-of-the-art performance with a much lower computing cost.

Social media platforms have become a place where users collaborate, share their ideas and also have conflicts (Hansson et al., 2019; Hansson and Ludwig, 2019). With 126 million active daily users (Shaban, 2019), Twitter is the dominant microblogging platform on which users discuss a breadth of subjects and even play a role in influencing current trends. Users on Twitter post short and often informal messages (tweets) in which they share information and project opinions and sentiments about what is going on in the world. Twitter has been a major platform for sharing scholarly articles, and many researchers have used it to develop various metrics for scholarly articles (Haustein, 2019). Other social media platforms like Facebook and Weibo have also been sources to study online users' responses (Kou et al., 2017). Social media platforms have become a hub where users express their opinions and emotions related to multiple fields of interest (Chatterjee et al., 2019). Researchers have studied the sentiments and emotions associated with research articles on these platforms (Freeman et al., 2019, 2020).

Emotion cause pair extraction (ECPE), as one of the derived subtasks of emotion cause analysis (ECA), shares rich inter-related features with emotion extraction (EE) and cause extraction (CE). Therefore EE and CE are frequently utilized as auxiliary tasks for better feature learning, modeled via multi-task learning (MTL) framework by prior works to achieve state-of-the-art (SoTA) ECPE results. However, existing MTL-based methods either fail to simultaneously model the specific features and the interactive feature in between, or suffer from the inconsistency of label prediction. In this work, we consider addressing the above challenges for improving ECPE by performing two alignment mechanisms with a novel A^2Net model. We first propose a feature-task alignment to explicitly model the specific emotion-&cause-specific features and the shared interactive feature. Besides, an inter-task alignment is implemented, in which the label distance between the ECPE and the combinations of EE&CE are learned to be narrowed for better label consistency. Evaluations of benchmarks show that our methods outperform current best-performing systems on all ECA subtasks. Further analysis proves the importance of our proposed alignment mechanisms for the task.