As the capabilities of artificial agents improve, they are being increasingly deployed to service multiple diverse objectives and stakeholders. However, the composition of these objectives is often performed ad hoc, with no clear justification. This paper takes a normative approach to multi-objective agency: from a set of intuitively appealing axioms, it is shown that Markovian aggregation of Markovian reward functions is not possible when the time preference (discount factor) for each objective may vary. It follows that optimal multi-objective agents must admit rewards that are non-Markovian with respect to the individual objectives. To this end, a practical non-Markovian aggregation scheme is proposed, which overcomes the impossibility with only one additional parameter for each objective. This work offers new insights into sequential, multi-objective agency and intertemporal choice, and has practical implications for the design of AI systems deployed to serve multiple generations of principals with varying time preference.

PROSPECT provides value to proteomics and machine learning researchers by including several high-quality annotations and by being accessible in terms of format and structure for applying machinelearning.

Romance and parenthood feel remote in Ukraine: 'I haven't had a date since before the war' Sitting in a wine bar in Kyiv on a Saturday night, Daria, 34, opens a dating app, scrolls, then puts her phone away. After spending more than a decade in committed relationships she's been single for a long time. I haven't had a proper date since before the war, she says. Four years of war have forced Ukrainians to rethink nearly every aspect of daily life. Increasingly that includes decisions about relationships and parenthood - and these choices are, in turn, shaping the future of a country in which both marriage and birth rates are falling.

Proteomics is the interdisciplinary field focusing on the large-scale study of proteins. Proteins essentially organize and execute all functions within organisms. Today, the bottom-up analysis approach is the most commonly used workflow, where proteins are digested into peptides and subsequently analyzed using Tandem Mass Spectrometry (MS/MS). MS-based proteomics has transformed various fields in life sciences, such as drug discovery and biomarker identification. Today, proteomics is entering a phase where it is helpful for clinical decision-making. Computational methods are vital in turning large amounts of acquired raw MS data into information and, ultimately, knowledge.

There are growing fears of an imminent stock market crash – one that will transform from a dip to a dive when euphoric headlines about the wonders of artificial intelligence begin to wane. Shares in US tech stocks have fallen in recent weeks and the prospect is that a flood of negative numbers will become the norm before the month is out. It could be 2000 all over again, and just like the bursting of the dotcom bubble it may be ugly, with investors junking businesses that once looked good on paper but now resemble a huge liability. Jerome Powell, the Federal Reserve chair, is one of the policymakers tasked with keeping the wolf from the door. Speaking on Friday at the annual Jackson Hole gathering of central bank governors in Wyoming, he tried to calm nerves.

Following up on our earlier study in [J. Bardhan et al., Machine learning-enhanced search for a vectorlike singlet B quark decaying to a singlet scalar or pseudoscalar, Phys. Rev. D 107 (2023) 115001; arXiv:2212.02442], we investigate the LHC prospects of pair-produced vectorlike $B$ quarks decaying exotically to a new gauge-singlet (pseudo)scalar field $Φ$ and a $b$ quark. After the electroweak symmetry breaking, the $Φ$ decays predominantly to $gg/bb$ final states, leading to a fully hadronic $2b+4j$ or $6b$ signature. Because of the large Standard Model background and the lack of leptonic handles, it is a difficult channel to probe. To overcome the challenge, we employ a hybrid deep learning model containing a graph neural network followed by a deep neural network. We estimate that such a state-of-the-art deep learning analysis pipeline can lead to a performance comparable to that in the semi-leptonic mode, taking the discovery (exclusion) reach up to about $M_B=1.8\:(2.4)$ TeV at HL-LHC when $B$ decays fully exotically, i.e., BR$(B \to bΦ) = 100\%$.

Large Language Models (LLMs) are increasingly used in decision-making scenarios that involve risk assessment, yet their alignment with human economic rationality remains unclear. In this study, we investigate whether LLMs exhibit risk preferences consistent with human expectations across different personas. Specifically, we assess whether LLM-generated responses reflect appropriate levels of risk aversion or risk-seeking behavior based on individual's persona. Our results reveal that while LLMs make reasonable decisions in simplified, personalized risk contexts, their performance declines in more complex economic decision-making tasks. To address this, we propose an alignment method designed to enhance LLM adherence to persona-specific risk preferences. Our approach improves the economic rationality of LLMs in risk-related applications, offering a step toward more human-aligned AI decision-making.