In August, 1918, Virginia Woolf spent a quiet stretch at Asheham, the country house that she and her husband, Leonard, rented in rural Sussex. "We've been practically alone, which has a very spiritual effect upon the mind," Woolf wrote to a friend, the socialite Lady Ottoline Morrell. After six months spent in such isolation, Woolf quipped, "I should be a kind of Saint, and Leonard an undoubted prophet. We should shed virtue on people as we walked along the roads." Alas, any pretensions to holiness had been dispelled by the arrival of house guests the previous evening: "I had such a bath of the flesh that I am far from unspotted this morning.

In the realm of time series analysis, tackling the phenomenon of concept drift poses a significant challenge. Concept drift - characterized by the evolving statistical properties of time series data, affects the reliability and accuracy of conventional analysis models. This is particularly evident in co-evolving scenarios where interactions among variables are crucial. This paper presents Drift2Matrix, a novel framework that leverages kernel-induced self-representation for adaptive responses to concept drift in time series. Drift2Matrix employs a kernel-based learning mechanism to generate a representation matrix, encapsulating the inherent dynamics of co-evolving time series. This matrix serves as a key tool for identification and adaptation to concept drift by observing its temporal variations. Furthermore, Drift2Matrix effectively identifies prevailing patterns and offers insights into emerging trends through pattern evolution analysis. Our empirical evaluation of Drift2Matrix across various datasets demonstrates its effectiveness in handling the complexities of concept drift. This approach introduces a novel perspective in the theoretical domain of co-evolving time series analysis, enhancing adaptability and accuracy in the face of dynamic data environments. Co-evolving time series data analysis plays a crucial role in diverse sectors including finance, healthcare, and meteorology. Within these areas, multiple time series evolve simultaneously and interact with one another, forming complex, dynamic systems. A particularly pervasive issue is concept drift Lu et al. (2018b); Yu et al. (2024), which refers to shifts in the underlying data distribution over time, thereby undermining the effectiveness of static models. Traditional time series approaches commonly rely on the assumptions of stationarity and linear relationships. Methods such as ARIMA and VAR Box (2013), for instance, perform well in circumstances with stable and predictable dynamics. Conversely, machine learning methodologies Li et al. (2022); Wen et al. (2020), such as diverse neural network architectures Ho et al. (2022); Li et al. (2023); Yang et al. (2024), offer more flexibility but often require large amounts of data and face difficulties in terms of interpretability and adaptability, especially in dynamic contexts. The evolving study has steered the field towards more adaptive and dynamic models.

In many domains, such as healthcare, time-series data is often irregularly sampled with varying intervals between observations. This poses challenges for classical time-series models that require equally spaced data. To address this, we propose a novel time-series Transformer called Trajectory Generative Pre-trained Transformer (TrajGPT). TrajGPT employs a novel Selective Recurrent Attention (SRA) mechanism, which utilizes a data-dependent decay to adaptively filter out irrelevant past information based on contexts. By interpreting TrajGPT as discretized ordinary differential equations (ODEs), it effectively captures the underlying continuous dynamics and enables time-specific inference for forecasting arbitrary target timesteps. Experimental results demonstrate that TrajGPT excels in trajectory forecasting, drug usage prediction, and phenotype classification without requiring task-specific fine-tuning. By evolving the learned continuous dynamics, TrajGPT can interpolate and extrapolate disease risk trajectories from partially-observed time series. The visualization of predicted health trajectories shows that TrajGPT forecasts unseen diseases based on the history of clinically relevant phenotypes (i.e., contexts). Time-series representation learning plays a crucial role in various domains, as it facilitates the extraction of generalizable temporal patterns from large-scale, unlabeled data, which can then be adapted for diverse tasks (Ma et al., 2023).

Not until I picked up Tony Tulathimutte's "Rejection" did I realize how fun it could be to read a book about a bunch of huge fucking losers. It sucks for them, the inept, lonely, self-obsessed, self-righteous, self-imprisoned protagonists of these linked stories, but it's a thrill for the sickos among us, the king being Tulathimutte, who gives loserdom its own rancid carnival. Tulathimutte understands the project--both his own and that of his characters--with diagnostic, comprehensive hyper-precision; as you behold his parade of marketplace failure and personal pathology, he's ten steps ahead of any reaction you could muster. Thus, you simply surrender to the sick pleasure of watching humiliating people humiliate themselves, as when a clammy self-styled feminist ally gets shut down by a girl and goes, "Grrr, friend-zoned again!" while shaking his fists at the ceiling, then creates a dating profile that includes the line "Unshakably serious about consent. These are two of the mildest ...

Keeping a poker face had never struck me as much of a feat--until I had to keep one. My pulse quickened, my cheeks felt flushed, and my eyes were desperate to dart and size up the pot. What had been a mediocre hand was transformed, after the flop came down, into something spectacular: every card from seven to jack--a straight. All that remained was to play it cool and build up my cash prize. The bets started small, and then grew. The next two cards looked innocuous enough.

"Hum," Helen Phillips's third novel, begins with a needle being drawn, steadily and irreversibly, across a woman named May's face. She is participating in a paid experiment in "adversarial tech," undergoing a procedure that will ever so slightly alter her features, making her harder for surveillance cameras to identify. As the book opens, May is mid-op, the needle advancing its "slender and relentless line of penetration" across her temple, toward the skin of her eyelid. What lies on the other side of the surgery? "Some sort of transformation, undeniable but undetectable," Phillips writes.

One upshot of the current glut of streaming platforms is a flood of programming to fill them: something for every attention span, something to plug every potential gap of viewer inactivity that might render a certain streaming service irrelevant while some other service pulls ahead. And so stories get told and retold. The romantic comedies begin to feel the same. The dating reality shows rely (often successfully, it must be said) on the same dramatic tricks. Another consequence of this, for better or worse, is that the stories being told are pulling from more immediate memory.

Since the release of the original CheXpert paper five years ago, CheXpert has become one of the most widely used and cited clinical AI datasets. The emergence of vision language models has sparked an increase in demands for sharing reports linked to CheXpert images, along with a growing interest among AI fairness researchers in obtaining demographic data. To address this, CheXpert Plus serves as a new collection of radiology data sources, made publicly available to enhance the scaling, performance, robustness, and fairness of models for all subsequent machine learning tasks in the field of radiology. CheXpert Plus is the largest text dataset publicly released in radiology, with a total of 36 million text tokens, including 13 million impression tokens. To the best of our knowledge, it represents the largest text de-identification effort in radiology, with almost 1 million PHI spans anonymized. It is only the second time that a large-scale English paired dataset has been released in radiology, thereby enabling, for the first time, cross-institution training at scale. All reports are paired with high-quality images in DICOM format, along with numerous image and patient metadata covering various clinical and socio-economic groups, as well as many pathology labels and RadGraph annotations. We hope this dataset will boost research for AI models that can further assist radiologists and help improve medical care. Data is available at the following URL: https://stanfordaimi.azurewebsites.net/datasets/5158c524-d3ab-4e02-96e9-6ee9efc110a1 Models are available at the following URL: https://github.com/Stanford-AIMI/chexpert-plus

Last month, a new dating app called Volar launched in New York City, with the promise "We go on blind dates. So you don't have to." To sign up, you enter your name and phone number, then submit yourself to a brief interview with a chatbot matchmaker. When I made an account, Volar's bot asked what line of work I was in. "I'm a book critic," I replied.

The Greenworks GRV-5011 is a robot vacuum-mop hybrid. When the vacuum returns to its dock after cleaning, a secondary vacuum empties out the dust bin and places the contents into a self-sealing bag. This robot also has a mop attachment located on the bottom-rear section of the unit along with with a 350ml water tank. For people who are interested in smart technology, the Greenworks robot vacuum is compatible with a free app that allows users to start, stop, schedule, and direct the robot to clean specific areas. The self-sealing bag prevents dirt from escaping when you empty it.