We study a bandit version of phase retrieval where the learner chooses actions $(A_t)_{t=1}^n$ in the $d$-dimensional unit ball and the expected reward is $\langle A_t, \theta_\star \rangle^2$ with $\theta_\star \in \mathbb R^d$ an unknown parameter vector. We prove an upper bound on the minimax cumulative regret in this problem of $\smash{\tilde \Theta(d \sqrt{n})}$, which matches known lower bounds up to logarithmic factors and improves on the best known upper bound by a factor of $\smash{\sqrt{d}}$. We also show that the minimax simple regret is $\smash{\tilde \Theta(d / \sqrt{n})}$ and that this is only achievable by an adaptive algorithm. Our analysis shows that an apparently convincing heuristic for guessing lower bounds can be misleading and that uniform bounds on the information ratio for information-directed sampling (Russo and Van Roy, 2014) are not sufficient for optimal regret.

Large-scale distributed storage systems, such as object stores, usually apply hashing-based placement and lookup methods to achieve scalability and resource efficiency. However, when object locations are determined by hash values, placement becomes inflexible, failing to optimize or satisfy application requirements such as load balance, failure tolerance, parallelism, and network/system performance. This work presents a novel solution to achieve the best of two worlds: flexibility while maintaining cost-effectiveness and scalability. The proposed method Smash is an object placement and lookup method that achieves full placement flexibility, balanced load, low resource cost, and short latency. Smash uses a recent space-efficient data structure and applies it to object-location lookups. We implement Smash as a prototype system and evaluate it in a public cloud. The analysis and experimental results show that Smash achieves full placement flexibility, fast storage operations, fast recovery from node dynamics, and lower DRAM cost (less than 60%) compared to existing hash-based solutions, such as Ceph and MapX.

Ever wanted to create a song without picking up an instrument or singing a note? Supermusic AI lets you do just that--turning simple text prompts into full-fledged tracks with vocals. Whether you dream of dropping pop anthems or producing smooth jazz, this app makes it happen in minutes. For a limited time, pay 39.97 just once for a lifetime subscription. Supermusic AI is like having a recording studio in your pocket.

We study a bandit version of phase retrieval where the learner chooses actions (A_t)_{t 1} n in the d -dimensional unit ball and the expected reward is \langle A_t, \theta_\star \rangle 2 with \theta_\star \in \mathbb R d an unknown parameter vector. We prove an upper bound on the minimax cumulative regret in this problem of \smash{\tilde \Theta(d \sqrt{n})}, which matches known lower bounds up to logarithmic factors and improves on the best known upper bound by a factor of \smash{\sqrt{d}} . We also show that the minimax simple regret is \smash{\tilde \Theta(d / \sqrt{n})} and that this is only achievable by an adaptive algorithm. Our analysis shows that an apparently convincing heuristic for guessing lower bounds can be misleading and that uniform bounds on the information ratio for information-directed sampling (Russo and Van Roy, 2014) are not sufficient for optimal regret.

One of the most important aspects of aligning with human values is the necessity for agents to autonomously make altruistic, safe, and ethical decisions, considering and caring for human well-being. Current AI extremely pursues absolute superiority in certain tasks, remaining indifferent to the surrounding environment and other agents, which has led to numerous safety risks. Altruistic behavior in human society originates from humans' capacity for empathizing others, known as Theory of Mind (ToM), combined with predictive imaginative interactions before taking action to produce thoughtful and altruistic behaviors. Inspired by this, we are committed to endow agents with considerate self-imagination and ToM capabilities, driving them through implicit intrinsic motivations to autonomously align with human altruistic values. By integrating ToM within the imaginative space, agents keep an eye on the well-being of other agents in real time, proactively anticipate potential risks to themselves and others, and make thoughtful altruistic decisions that balance negative effects on the environment. The ancient Chinese story of Sima Guang Smashes the Vat illustrates the moral behavior of the young Sima Guang smashed a vat to save a child who had accidentally fallen into it, which is an excellent reference scenario for this paper. We design an experimental scenario similar to Sima Guang Smashes the Vat and its variants with different complexities, which reflects the trade-offs and comprehensive considerations between self-goals, altruistic rescue, and avoiding negative side effects. Comparative experimental results indicate that agents are capable of prioritizing altruistic rescue while minimizing irreversible damage to the environment and making more altruistic and thoughtful decisions. This work provides a preliminary exploration of agents' autonomous alignment with human altruistic values, laying the foundation for the subsequent realization of moral and ethical AI.

"I'm absolutely a Luddite," the author and columnist Brian Merchant said the other day at an outdoor café in Brooklyn. He has long, brown hair and a goatee, and was wearing a plaid shirt over a T-shirt that read "The Luddites Were Right." On the chair next to him sat an HP printer. Merchant feels that the original Luddites, early-nineteenth-century cloth-makers who raided British factories and destroyed the new machines that were replacing them, have been getting a bad rap lately. Modern people tend to see them as fools who didn't appreciate the benefits of technology.

NASA has spent seven years trying to prevent Bennu -- an asteroid taller than the Empire State Building and named after ancient Egypt's fiery bird-god -- from crashing cataclysmically into Earth. While Bennu's chances of impact are just 1-in-2,700, more than five times a person's chance of being struck by lightning, NASA's team nevertheless has categorized it as one of the two'most hazardous known asteroids.' In a worst-case scenario, the roughly 510-meter wide, carbon-based behemoth would smash into Earth with 1,200 megatons of energy: 24 times the power of the largest nuclear bomb ever detonated (the Soviet Union's'Tsar Bomba'). If it happens, Bennu's impact would unleash its 1.2 gigaton impact 159 years from this Sunday, on September 24, 2182. While Bennu is nowhere near the size of the dino-killing, six-mile across space rock that hit the Yucatan 66 million years ago, astronomers believe that the asteroid'could cause continental devastation if it became an Earth impactor.'

SMASH is an innovative, intersectoral, career-development training program for outstanding postdoctoral researchers from any where in the world, centered on developing cutting-edge machine learning applications for science and humanities.

This thematic area relates to the'medicine of the future', principally the customisation of healthcare, with medical decisions, treatments, practises, or products being tailored to the individual patients, instead of a one‐drug‐ fits‐all model. Preventive or therapeutic interventions can then be targeted at those who will benefit, sparing expense and side effects for those who will not. Data analytics, including data mining and machine learning, is an integral part of the precision medicine model, e.g., in the discovery of new predictive or prognostic biomarkers or subgroups of patients. The number of papers reporting advances in this field are on almost an exponential rise since 2010 with Aaron Ciechanover, a Nobel Prize winner in Chemistry 2004, branding personalised medicine the "third revolution" of drug research. Neurodegenerative diseases, including Alzheimer's dementia (AD) and Parkinson's disease (PD), are caused by the progressive loss of structure or function of neurons.

SMASH is an innovative, intersectoral, career-development training program for outstanding postdoctoral researchers, co-funded by the Marie Skłodowska-Curie Actions COFUND program. SMASH is open to researchers around the world who are interested in developing cutting-edge machine learning applications for science and humanities. During the five years of the SMASH project (2023-28) and over three planned calls, SMASH aims to hire 50 individuals for 2-year full-time postdoctoral contracts with highly attractive conditions. Fellows will be hosted by five leading Slovenian research institutions: the University of Nova Gorica, the University of Ljubljana, the Jožef Stefan Institute, the Institute of Information Science*, and the Slovenian Environment Agency*. Applicants should propose ambitious research projects in one of SMASH's five key research areas, that are centered on the use of cutting-edge machine learning, or more broadly, artificial intelligence techniques, to address some of the world's most challenging questions in: Applicants should choose the SMASH host institution and supervisor with whom they will coordinate the project proposal preparation.