Multidimensional scaling visualizes dissimilarities among objects and reduces data dimensionality. While many methods address symmetric proximity data, asymmetric and especially three-way proximity data (capturing relationships across multiple occasions) remain underexplored. Recent developments, such as the h-plot, enable the analysis of asymmetric and non-reflexive relationships by embedding dissimilarities in a Euclidean space, allowing further techniques like archetypoid analysis to identify representative extreme profiles. However, no existing methods extract archetypal profiles from three-way asymmetric proximity data. This work extends the h-plot methodology to three-way proximity data under both symmetric and asymmetric, conditional and unconditional frameworks. The proposed approach offers several advantages: intuitive interpretability through a unified Euclidean representation; an explicit, eigenvector-based analytical solution free from local minima; scale invariance under linear transformations; computational efficiency for large matrices; and a straightforward goodness-of-fit evaluation. Furthermore, it enables the identification of archetypal profiles and clustering structures for three-way asymmetric proximities. Its performance is compared with existing models for multidimensional scaling and clustering, and illustrated through a financial application. All data and code are provided to facilitate reproducibility.

It's a crushing diagnosis for everyone involved. Jules's wife, Maria, told me that once it was official, she and Jules left the doctor's office gripping each other in floods of tears. Their lives were turned upside down. Four and a half years later, Jules cannot move his limbs, and a tracheostomy has left him unable to speak. "To say this diagnosis has been devastating is an understatement," says Joyce, who has bulbar MND--she can still move her limbs but struggles to speak and swallow.

It's an analog ache that is oddly satisfying in a nostalgic way. In the last few days, I've held a pen and written more words for a much longer time than I have ever done in years. As I pushed myself to handwrite large parts of this review to spend more time with the 2024 Kindle Scribe's stylus and note-taking tools, I started to feel a sensation I hadn't remembered since my teens. I often feel the urge to jot down thoughts and lists, but I never really wanted to spend longer than 15 minutes writing. And yet, Amazon's new AI features for the Kindle Scribe seem to cater more to those who labor over essays or missives that they ultimately need to share with others.

The authors explore the relation between the trajectory of Gradient Descent (GD) initiated in the origin and the regularization path for l2-regularized minimization of the same objective. They first study the continuous-time setting where GD is replaced Gradient Flow, assuming that the objective is smooth and strongly convex. The main result (Theorem 1 whose proof I have verified) is as follows: under the appropriate scaling between the time t in GD and the inverse regularization parameter \eta, the two trajectories do not diverge much. This result is obtained by quantifying the shrinkage of the gradients as t and eta tend to infinity. In the continuous-time setting, the authors manage to reduce this task to formulating and solving certain ODEs.

After months of speculation and a torturous wait for fans, Apple has confirmed its next unveiling event on September 9. Dubbed'It's Glowtime', Apple will launch its next smartphone, the iPhone 16, and its numerous variants during the event at Apple Park in Cupertino, California. But as the 10th anniversary of the tech giant's first ever smartwatch approaches, it may just mark the occasion with a very special new product. Apple is preparing to release the'Apple Watch X' which could feature a radical redesign, according to a leaker. Akin to the iPhone X in 2017 that celebrated a decade of the iPhone, Apple Watch X could mark 10 years since the world glimpsed the first Apple Watch. The claim comes from respected Apple tipster Mark Gurman based in California, who says Apple is'preparing a big update' in his weekly newsletter.

Our friends from across the pond have been known to struggle with British sarcasm on occasion. But improved Anglo-American relations may be on the horizon, as experts have developed a device that can detect when someone is being sarcastic. A team from the University of Groningen have created an algorithm that analyses someone's speech to work out if they are using irony. It works by examining the pitch, talking rate and energy in speech, and then transcribing the speech into text for it to be analysed further for language cues. 'We extracted acoustic parameters such as pitch, speaking rate, and energy from speech, then used Automatic Speech Recognition to transcribe the speech into text for sentiment analysis,' author Xiyuan Gao said.

As AI-bots continue to gain popularity due to their human-like traits and the intimacy they offer to users, their societal impact inevitably expands. This leads to the rising necessity for comprehensive studies to fully understand AI-bots and reveal their potential opportunities, drawbacks, and overall societal impact. With that in mind, this research conducted an extensive investigation into ChatGPT3, a renowned AI bot, aiming to assess the temporal reliability of its personality profile. Psychological questionnaires were administered to the chatbot on two separate occasions, followed by a comparison of the responses to human normative data. The findings revealed varying levels of agreement in chatbot's responses over time, with some scales displaying excellent agreement while others demonstrated poor agreement. Overall, Davinci-003 displayed a socially desirable and pro-social personality profile, particularly in the domain of communion. However, the underlying basis of the chatbot's responses-whether driven by conscious self reflection or predetermined algorithms-remains uncertain.

Several scientific communities are currently facing a replication crisis, wherein it has proven difficult or impossible for researchers to independently verify the results of previously published studies. Failures to replicate large swaths of experimental work (Camerer et al., 2018; Nosek et al., 2015; Begley and Ellis, 2012; Baker, 2016) have come in fields like psychology or medicine, that focus on what Hofman et al. (2021) call explanatory modeling, where the goal is to identify and estimate causal effects (e.g., is there an effect of X on Y, and if so, how large is it?). While there are many different factors that can contribute to unreliable findings in explanatory modeling, the combination of small-scale experiments involving noisy measurements and the (mis)use of null hypothesis significance testing (NHST) has received a great deal of attention in recent years. Under these conditions, researchers can mistake idiosyncratic patterns in noise for true effects, resulting in unreliable findings that do not replicate upon further investigation (Button et al., 2013; Loken and Gelman, 2017; Meehl, 1990; Simmons et al., 2011). More generally, some forms of data-dependent decision making (e.g., about how to define research questions or hypotheses, how to filter or transform data, how to model data, what tests to run, etc.) can lead to similar problems regardless of the specifics of the methods (Gelman and Loken, 2013). What about other fields, such as machine learning and data science, that focus less on explanation and more on predictive modeling, defined in Hofman et al. (2021) as directly forecasting outcomes (e.g., how well can an outcome Y be predicted using all available features X?) without necessarily focusing on isolating individual causal effects? Predictive modeling is typically done by testing (out-of-sample) predictions on large-scale datasets, and hence--unlike explanatory modeling--involves neither small experiments nor misuse of significance testing. With advances in the fields of statistics and machine learning (ML) we have seen remarkable performance gains in predictive modeling over the last decade, for both traditional ML tasks and for scientific applications. The same methods that have been shown to achieve at or above human-level performance on tasks like playing chess, classifying images, or understanding natural language (Zhang et al.,

My relationship with my cat is less that of pet and owner than it is hostage-taker and hostage. Four-year-old Vlada spends every night sleeping peacefully in my arms like a teddy bear. Then, too soon after dawn, her demeanor abruptly changes: She bites my hands, legs, and neck, and meows in my face with a force that can only be described as belligerent. "Stop shouting at me," I tell her. After I have dutifully dispensed her morning tin of Applaws, Vlada is appeased.

Social media plays a significant role in boosting the fashion industry, where a massive amount of fashion-related posts are generated every day. In order to obtain the rich fashion information from the posts, we study the task of social media fashion knowledge extraction. Fashion knowledge, which typically consists of the occasion, person attributes, and fashion item information, can be effectively represented as a set of tuples. Most previous studies on fashion knowledge extraction are based on the fashion product images without considering the rich text information in social media posts. Existing work on fashion knowledge extraction in social media is classification-based and requires to manually determine a set of fashion knowledge categories in advance. In our work, we propose to cast the task as a captioning problem to capture the interplay of the multimodal post information. Specifically, we transform the fashion knowledge tuples into a natural language caption with a sentence transformation method. Our framework then aims to generate the sentence-based fashion knowledge directly from the social media post. Inspired by the big success of pre-trained models, we build our model based on a multimodal pre-trained generative model and design several auxiliary tasks for enhancing the knowledge extraction. Since there is no existing dataset which can be directly borrowed to our task, we introduce a dataset consisting of social media posts with manual fashion knowledge annotation. Extensive experiments are conducted to demonstrate the effectiveness of our model.