Whether we're living in the age of Peak TV or Trough TV, one thing is clear: There's too much TV. Thankfully, not every show has to be watched in its entirety. One of the best things about television is its serialized nature, the continuous thread that strings viewers along from one episode to the next. It's a cliché that prestige television is the new novel precisely because of the way that many dramas develop their characters and plots over many hours of storytelling. But an older virtue of TV is its brevity--the way a scenario can be introduced and resolved within the space of an hour, or half that--and some of the best episodes are less like chapters in a long-running novel than like short stories or short films. There's been no shortage of debate about this question, but for our purposes, we're defining it simply as an episode that stands up on its own, whether or not you've seen the rest of the show. Some are "bottle episodes," which typically confine a small cast to one location to save money. Some are "departure episodes," in which a show abandons its usual format or style to suddenly become, say, silent, animated, a musical, or about a minor character it was never about before. But not all bottle episodes and departure episodes are stand-alones, and vice versa. It's for this reason that you won't find Breaking Bad's celebrated "Fly" on this list: It may be a bottle episode, but it doesn't stand alone, because the best thing about it--how the housefly is a metaphor for everything else going on in the series--is comprehensible only to those who have watched the show. These are English-language selections, and, out of fairness, we have limited ourselves to one episode per series, although some shows are full of stellar contenders. Use these picks--arranged in chronological order, with an admitted bias toward our most recent, and best, era of television--to populate your streaming queue with a feast of bite-sized morsels, each of which could double as either a snackable introduction to a new show or a satisfying meal in itself. If movies made Alfred Hitchcock a name, TV made him a brand. The master of suspense embraced the burgeoning medium in 1955 with Alfred Hitchcock Presents (later renamed The Alfred Hitchcock Hour), an anthology series whose entries began and ended the same way: the titular celebrity providing context to a unique half-hour thriller, typically an adaption of a short story by an esteemed author (John Cheever, Ray Bradbury, many others).

South Korean internet service provider SK Broadband and Netflix have announced they will end all lawsuits with each other following a dispute over whether Netflix should pay for costs from increased network traffic and maintenance work. SK Broadband and parent SK Telecom said in a joint statement with Netflix on Monday that they had agreed on a partnership to release joint products and seek ways to use artificial intelligence (AI) products being developed by SK. "Moving forward, SK Broadband and Netflix will end all disputes with the signing of today's partnership, and collaborate as partners for the future," the statement said. Spokespeople for Netflix and SK Broadband said both had withdrawn their lawsuits. The two sides have been in a legal dispute since 2020 over whether content providers that generate large volumes of traffic should pay for network use, or whether that would go against the principle of "net neutrality" and lead to higher costs for consumers.

User-generated texts available on the web and social platforms are often long and semantically challenging, making them difficult to annotate. Obtaining human annotation becomes increasingly difficult as problem domains become more specialized. For example, many health NLP problems require domain experts to be a part of the annotation pipeline. Thus, it is crucial that we develop low-resource NLP solutions able to work with this set of limited-data problems. In this study, we employ Abstract Meaning Representation (AMR) graphs as a means to model low-resource Health NLP tasks sourced from various online health resources and communities. AMRs are well suited to model online health texts as they can represent multi-sentence inputs, abstract away from complex terminology, and model long-distance relationships between co-referring tokens. AMRs thus improve the ability of pre-trained language models to reason about high-complexity texts. Our experiments show that we can improve performance on 6 low-resource health NLP tasks by augmenting text embeddings with semantic graph embeddings. Our approach is task agnostic and easy to merge into any standard text classification pipeline. We experimentally validate that AMRs are useful in the modeling of complex texts by analyzing performance through the lens of two textual complexity measures: the Flesch Kincaid Reading Level and Syntactic Complexity. Our error analysis shows that AMR-infused language models perform better on complex texts and generally show less predictive variance in the presence of changing complexity.

False news has received attention from both the general public and the scholarly world. Such false information has the ability to affect public perception, giving nefarious groups the chance to influence the results of public events like elections. Anyone can share fake news or facts about anyone or anything for their personal gain or to cause someone trouble. Also, information varies depending on the part of the world it is shared on. Thus, in this paper, we have trained a model to classify fake and true news by utilizing the 1876 news data from our collected dataset. We have preprocessed the data to get clean and filtered texts by following the Natural Language Processing approaches. Our research conducts 3 popular Machine Learning (Stochastic gradient descent, Na\"ive Bayes, Logistic Regression,) and 2 Deep Learning (Long-Short Term Memory, ASGD Weight-Dropped LSTM, or AWD-LSTM) algorithms. After we have found our best Naive Bayes classifier with 56% accuracy and an F1-macro score of an average of 32%.

However, the limited field-of-view (FoV) of the microscopic camera necessitates camera motion to capture a broader workspace environment. In this work, we propose an autonomous robotic control method to constrain a robot-held camera within a designated FoV. Furthermore, we model the camera extrinsics as part of the kinematic model and use camera measurements coupled with a U-Net based tool tracking to adapt the complete robotic model during task execution. As a proof-of-concept demonstration, the proposed framework was evaluated in a bi-manual setup, where the microscopic camera was controlled to view a tool moving in a pre-defined trajectory. The proposed method allowed the camera to stay 99.5% of the time within the real FoV, compared to 48.1% without the proposed adaptive control.

People can use various tools, such as high-fidelity players and streaming apps, to enjoy In this work, we introduce a novel NLP task: assessing music at any time. Listeners can simply go the positive and risky messages of a music online, press the PLAY button, and find themselves item. We study the messages that a music item invigorated after a bad day. However, this easy access conveys from five significant dimensions regarding also raises concerns that children and adolescents appropriateness: Positive Messages, Violence, may have a higher chance of being exposed to Substance Consumption, Sex, and Consumerism risky content.

Large language models (LLMs) have the potential to transform our lives and work through the content they generate, known as AI-Generated Content (AIGC). To harness this transformation, we need to understand the limitations of LLMs. Here, we investigate the bias of AIGC produced by seven representative LLMs, including ChatGPT and LLaMA. We collect news articles from The New York Times and Reuters, both known for their dedication to provide unbiased news. We then apply each examined LLM to generate news content with headlines of these news articles as prompts, and evaluate the gender and racial biases of the AIGC produced by the LLM by comparing the AIGC and the original news articles. We further analyze the gender bias of each LLM under biased prompts by adding gender-biased messages to prompts constructed from these news headlines. Our study reveals that the AIGC produced by each examined LLM demonstrates substantial gender and racial biases. Moreover, the AIGC generated by each LLM exhibits notable discrimination against females and individuals of the Black race. Among the LLMs, the AIGC generated by ChatGPT demonstrates the lowest level of bias, and ChatGPT is the sole model capable of declining content generation when provided with biased prompts.

Citations in scholarly work serve the essential purpose of acknowledging and crediting the original sources of knowledge that have been incorporated or referenced. Depending on their surrounding textual context, these citations are used for different motivations and purposes. Large Language Models (LLMs) could be helpful in capturing these fine-grained citation information via the corresponding textual context, thereby enabling a better understanding towards the literature. Furthermore, these citations also establish connections among scientific papers, providing high-quality inter-document relationships and human-constructed knowledge. Such information could be incorporated into LLMs pre-training and improve the text representation in LLMs. Therefore, in this paper, we offer a preliminary review of the mutually beneficial relationship between LLMs and citation analysis. Specifically, we review the application of LLMs for in-text citation analysis tasks, including citation classification, citation-based summarization, and citation recommendation. We then summarize the research pertinent to leveraging citation linkage knowledge to improve text representations of LLMs via citation prediction, network structure information, and inter-document relationship. We finally provide an overview of these contemporary methods and put forth potential promising avenues in combining LLMs and citation analysis for further investigation.

Abstract--Perceptual processes are frequently multi-modal. This is the case of haptic perception. Such data set would be useful to conduct the I. T is well known that human perception is often multisensory where different sources of information accessed This observation motivated us to create a multi-modal through different sensory modalities are merged and integrated data set comprising the signals created when a bare finger by the brain. This integration process is thought to increase the explored varied textured surfaces. The measured signals were robustness of the perception of the properties of objects in the stereoscopic images of the surface, the position and speed of face of uncertainty, to resolve ambiguities, and to contribute the fingertip in images coordinates, the load applied by the to the perceptual stability of sensory scenes [1]-[4].

Understanding the framing of political issues is of paramount importance as it significantly shapes how individuals perceive, interpret, and engage with these matters. While prior research has independently explored framing within news media and by social media users, there remains a notable gap in our comprehension of the disparities in framing political issues between these two distinct groups. To address this gap, we conduct a comprehensive investigation, focusing on the nuanced distinctions both qualitatively and quantitatively in the framing of social media and traditional media outlets concerning a series of American Supreme Court rulings on affirmative action, student loans, and abortion rights. Our findings reveal that, while some overlap in framing exists between social media and traditional media outlets, substantial differences emerge both across various topics and within specific framing categories. Compared to traditional news media, social media platforms tend to present more polarized stances across all framing categories. Further, we observe significant polarization in the news media's treatment (i.e., Left vs. Right leaning media) of affirmative action and abortion rights, whereas the topic of student loans tends to exhibit a greater degree of consensus. The disparities in framing between traditional and social media platforms carry significant implications for the formation of public opinion, policy decision-making, and the broader political landscape.