At a recent conference on mathematics in the age of automated proofs, mathematician and Fields Medalist Akshay Venkatesh presented “How do we talk to our students about AI?'' He quoted an email he'd received from a young student who asked, “Do you believe that mathematics is worth being studied in a world in which a machine can answer everything for you? What do you believe would be the 'job’ of a mathematician in this world?” Venkatesh framed AI as an opportunity to correct what he called an “essential gap that has opened between the practice of mathematics and our values.” Mathematician William Thurston has explained these values by writing, “mathematics is not about numbers, equations, computations, or algorithms: it is about understanding.” But Venkatesh argued that the record on this is terrible, lamenting that “for a typical paper or talk, very few of us understand it.” He is not alone in thinking that something is wrong with the current state of mathematics research.

Clustering financial assets based on return correlations is a fundamental task in portfolio optimization and statistical arbitrage. However, classical clustering methods often fall short when dealing with signed correlation structures, typically requiring lossy transformations and heuristic assumptions such as a fixed number of clusters. In this work, we apply the Graph-based Coalition Structure Generation algorithm (GCS-Q) to directly cluster signed, weighted graphs without relying on such transformations. GCS-Q formulates each partitioning step as a QUBO problem, enabling it to leverage quantum annealing for efficient exploration of exponentially large solution spaces. We validate our approach on both synthetic and real-world financial data, benchmarking against state-of-the-art classical algorithms such as SPONGE and k-Medoids. Our experiments demonstrate that GCS-Q consistently achieves higher clustering quality, as measured by Adjusted Rand Index and structural balance penalties, while dynamically determining the number of clusters. These results highlight the practical utility of near-term quantum computing for graph-based unsupervised learning in financial applications.

In the modern language sciences, the core computational operation of syntax, ' Merge ', is defined as a n operation that combines two linguistic units (e.g., ' b rown ', ' cat ') to form a categorized structure ( ' b rown cat ', a Noun Phrase) . This can then be further combined with additional linguistic units based on this categorial information, respecting non - associativity such that abstract grouping is respected . Some linguists have embraced the view that Merge is an elementary, indivisible operation that emerged in a single evolutionary step. F r om a neuro cognitive standpoint, different mental objects constructed by Merge may be supported by distinct mechanisms: (1) simple command constructions (e.g., " e at apples"); (2) the merging of adjectives and nouns ("red boat"); and (3) the merging of nouns with spatial prepositions ("laptop behind the sofa ") . Here, w e systematically investigate participants ' comprehension of sentences with increasing levels of syntactic complexity. Clustering analyses revealed behavioral evidence for three distinct structural types, which we discuss as potentially emerging at different developmental stage s and subject to selective impairment. While a Merge - based syntax may still have emerged suddenly in evolutionary time, responsible for the structured symbolic turn our species took, different cognitive mechanisms seem to underwrite the processing of various types of Merge - based objects .

With an eerie, robot-like appearance and an otherworldly glow when worn, those LED masks all over your FYP give off a science-fiction vibe. Fittingly, it was researchers with NASA who discovered the potential for medical light therapy to treat wounds, arthritis, glaucoma and other ailments in the 1990s. By the early 2000s, that LED light therapy was growing in popularity at dermatology offices where patients donned an LED mask or used similar devices to slow aging and treat acne. And now that technology has trickled down to our homes. Brands like Omnilux and Dr. Gross have popularized direct-to-consumer LED masks that are safe to use regularly in the privacy of your own home, available at a range of price points (from under 100 to nearly 500).

Some call it an employee's journey in their current workplace; others describe it as uncovering the tools and culture employees need to do their work successfully. But whatever you call the employee experience, it is driving employers even harder to better understand how they can offer a much more positive work environment -- no easy task, especially in the wake of the past 16 or so months as COVID changed the workplace. "I believe the entire dynamic and what employee experience looked like shifted since the pandemic," says Sugi Venkatesh, division vice president – HR, for Global Product and Technology on ADP's Human Resources team. Venkatesh adds that during the pandemic, ensuring a positive employee experience not only meant the workforce was engaged and taking care of customers, but it also became the only way to stay in business. "Many organizations went from employee experience being a focus area to it being the top priority, with real dollar investments," he says, adding that the paradigm shift also brought with it a need to accelerate the employee experience through technology.

Bayesian optimisation is a well-known sample-efficient method for the optimisation of expensive black-box functions. However when dealing with big search spaces the algorithm goes through several low function value regions before reaching the optimum of the function. Since the function evaluations are expensive in terms of both money and time, it may be desirable to alleviate this problem. One approach to subside this cold start phase is to use prior knowledge that can accelerate the optimisation. In its standard form, Bayesian optimisation assumes the likelihood of any point in the search space being the optimum is equal. Therefore any prior knowledge that can provide information about the optimum of the function would elevate the optimisation performance. In this paper, we represent the prior knowledge about the function optimum through a prior distribution. The prior distribution is then used to warp the search space in such a way that space gets expanded around the high probability region of function optimum and shrinks around low probability region of optimum. We incorporate this prior directly in function model (Gaussian process), by redefining the kernel matrix, which allows this method to work with any acquisition function, i.e. acquisition agnostic approach. We show the superiority of our method over standard Bayesian optimisation method through optimisation of several benchmark functions and hyperparameter tuning of two algorithms: Support Vector Machine (SVM) and Random forest.

In this setting, each pattern, represented as an n-dimensional feature vector, is associated with a discrete pattern class, or state of nature (Duda and Hart, 1973). Using available information, (e.g., a statistically representative set of labeled feature vectors

In this setting, each pattern, represented as an n-dimensional feature vector, is associated with a discrete pattern class, or state of nature (Duda and Hart, 1973). Using available information, (e.g., a statistically representative set of labeled feature vectors

In this setting, each pattern, represented as an n-dimensional feature vector, is associated with a discrete pattern class, or state of nature (Duda and Hart, 1973). Using available information, (e.g., a statistically representative set of labeled feature vectors

Robustness is a commonly bruited property of neural networks; in particular, a folk theorem in neural computation asserts that neural networks-in contexts with large interconnectivity-continue to function efficiently, albeit with some degradation, in the presence of component damage or loss. A second folk theorem in such contexts asserts that dense interconnectivity between neural elements is a sine qua non for the efficient usage of resources. These premises are formally examined in this communication in a setting that invokes the notion of the "devil"