Colloquium

We address the question of whether a given article is the output of a generative language model or perhaps includes some significant edits by a different author, possibly a human. For this problem, we develop a detection method that involves many perplexity tests for the origin of individual sentences, combining these multiple tests into a global test of significance using Higher Criticism (HC). As a by-product, we can identify sentences or other text chunks suspected as generated by a different mechanism than the language model.

Developing Artificial Intelligence systems require access to masses of data. This notion is common knowledge for computer engineers and data analysts. Data regulation is essential to ensure our safety, privacy, and ownership rights. Regulating the amount of data, the quality, and the priority with which organizations can access data are paramount. However, as with other areas of law, regulation could result in unwarranted results. One impact of data regulation is incentivizing the usage of low-quality data that often demonstrates bias.

Fetal MRI has the potential to complement US imaging and improve fetal development assessment by providing more accurate volumetric information about the fetal structures. However, volumetric measurements require manual delineation of the fetal structures, which is time consuming, annotator-dependent, and error-prone.

מערכות בריאות בעולם הולכות ומפנימות שהסטטוס קוו אינו בר קיימא, ושנדרש עיצוב מחדש של שירותי הבריאות בעידן הנוכחי.

ההתקדמות הטכנולוגית המואצת מייצרת הזדמנויות אך המערכות הנוקשות מתקשות להסתגל.

Internet traffic classification has been intensively studied over the past decade due to its importance for traffic engineering and cyber security. However, identifying the type of a network flow or a specific application become harder in recent years due to the use of encryption, e.g., by VPN and Tor.

The potential of the Extract Method refactoring for maintaining high-quality software is well-recognized. It can be useful by itself, or as a building block in larger refactoring tasks.

כיצד המיתו גליליאו, דקארט, וניוטון את החומרים ששלטו במדע של ימי הביניים - כדי לפנות את הבמה לעולם שנשלט על ידי חוקי טבע אוניברסליים - שבחשבון אחרון אינן אלא נוסחאות.

A decision tree (DT) is one of the most popular and efficient techniques in data mining. Specifically, in the clinical domain, DTs have been widely used thanks to their relatively easy explainable nature, efficient computation time, and relatively accurate predictions. However, some DT constriction algorithms may produce a large tree-size structure which is difficult to understand and often leads to misclassification of data in the testing process due to poor generalization.

Deep Learning (DL) has shown great promise in the unsupervised task of clustering. That said, while in classical (i.e., non-deep) clustering the benefits of the nonparametric approach are well known, most deep-clustering methods are parametric: namely, they require a predefined and fixed number of clusters, denoted by K. When K is unknown, however, using model-selection criteria to choose its optimal value might become computationally expensive, especially in DL as the training process would have to be repeated numerous times.

I will give a short introduction to generative diffusion models and present their performance compared to other generative models (including GANs). Moreover, I will present two additional works: