Most models of decision-making in neuroscience assume an infinite horizon, which yields an optimal solution that integrates evidence up to a fixed decision threshold; however, under most experimental as well as naturalistic behavioral settings, the decision has to be made before some finite deadline, which is often experienced as a stochastic quantity, either due to variable external constraints or internal timing uncertainty. In this work, we formulate this problem as sequential hypothesis testing under a stochastic horizon. We use dynamic programming tools to show that, for a large class of deadline distributions, the Bayes-optimal solution requires integrating evidence up to a threshold that declines monotonically over time. We use numerical simulations to illustrate the optimal policy in the special cases of a fixed deadline and one that is drawn from a gamma distribution.

Initiatives using machine learning cannot be treated in the same manner as projects involving conventional software. It's imperative to move quickly so that you can test things, fix issues and test them again. In other words, you must be able to fail quickly – and do so early on in the process. Waiting until later in this process to find issues can end up being very expensive and time-consuming. When developing software using the traditional method, you use decision logic.

Regeneron isn't the only company eager to derive more value from its data. Despite the enormous amounts of data they collect and the amount of capital they invest in data management solutions, business leaders are still not benefitting from their data. According to IDC research, 83% of CEOs want their organizations to be more data driven, but they struggle with the cultural and technological changes needed to execute an effective data strategy. In response, many organizations, including Regeneron, are turning to a new form of data architecture as a modern approach to data management. In fact, by 2024, more than three-quarters of current data lake users will be investing in this type of hybrid "data lakehouse" architecture to enhance the value generated from their accumulated data, according to Matt Aslett, a research director with Ventana Research.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Israel acknowledged on Friday that an inscription in clay found in the country's south bearing the name of Darius the Great, ruler of the ancient Persian Empire, was not authentic. The shard of pottery in question was discovered by a passerby last December and caused a sensation as the first mention of sixth century B.C. empire builder to appear in Israel. After the news broke earlier this week, an expert in ancient Aramaic inscriptions approached the Israel Antiquities Authority to explain that she herself had actually etched those words onto the ancient fragment.

Lyric-to-melody generation is an important task in songwriting, and is also quite challenging due to its unique characteristics: the generated melodies should not only follow good musical patterns, but also align with features in lyrics such as rhythms and structures. These characteristics cannot be well handled by neural generation models that learn lyric-to-melody mapping in an end-to-end way, due to several issues: (1) lack of aligned lyric-melody training data to sufficiently learn lyric-melody feature alignment; (2) lack of controllability in generation to better and explicitly align the lyric-melody features. In this paper, we propose Re-creation of Creations (ROC), a new paradigm for lyric-to-melody generation. ROC generates melodies according to given lyrics and also conditions on user-designated chord progression. It addresses the above issues through a generation-retrieval pipeline. Specifically, our paradigm has two stages: (1) creation stage, where a huge amount of music fragments generated by a neural melody language model are indexed in a database through several key features (e.g., chords, tonality, rhythm, and structural information); (2) re-creation stage, where melodies are re-created by retrieving music fragments from the database according to the key features from lyrics and concatenating best music fragments based on composition guidelines and melody language model scores. ROC has several advantages: (1) It only needs unpaired melody data to train melody language model, instead of paired lyric-melody data in previous models. (2) It achieves good lyric-melody feature alignment in lyric-to-melody generation. Tested by English and Chinese lyrics, ROC outperforms previous neural based lyric-to-melody generation models on both objective and subjective metrics.

Scientists in many fields got little attention over the last two years as the world focused on the emergency push to develop vaccines and treatments for COVID-19. But labs and researchers remained busy, and this year they've reported a dizzying series of major discoveries and achievements. Scientists at the Lawrence Livermore National Laboratory in California announced in December that they had produced the first fusion reaction that created more energy than was used to start it. The long-elusive achievement marked a major breakthrough in harnessing the process that fuels the sun. "This milestone moves us one significant step closer" to "powering our society" with zero-carbon fusion energy, Energy Secretary Jennifer Granholm said.

From a pig heart being successfully transplanted into a human, to being able to redirect an asteroid on a collision course with Earth, there have been all manner of weird and wonderful scientific discoveries in 2022. They include the human genome finally been mapped after two decades, the unearthing of Africa's oldest known dinosaur, and the release of the first ever image of a supermassive black hole at the heart of our Milky Way galaxy. There was also the alarming discovery that microplastics are everywhere – including in us – and the hugely-anticipated first images from the world's most powerful space telescope James Webb, which will peer back to the dawn of the universe. Here, MailOnline looks at 10 of the most interesting advances this year. The year began with a bang scientifically when just a week into it a dying man became the first patient in the world to get a heart transplant from a genetically-modified pig.

We are at a crossroads in the study of Heliophysics. On one hand we operate in the same paradigm that has guided the field over the past couple of decades, ruled by the triumvirate of data, theory, and simulations. On the other hand, we are beginning to recognize that powerful new opportunities for scientific discovery are possible through increased data volume and sophisticated methods to explore these data. The emergence of the hyperconnected digital society and the massive quantities of data it generates has led to new analysis capabilities that scale well to the solar-terrestrial environment. Heliophysics is squarely positioned to benefit from the emerging field of data science [1].

In the past decade, storied brands like meal-replacement Huel and men's grooming company Harry's built multibillion-dollar retail businesses by using social media and digital-first advertising to sell directly to consumers online, without the need for middlemen. These brands were exemplars of a new form of retail, called direct-to-consumer (DTC). The global pandemic only accelerated this trend, with many high-street stores being forced to close and to keep driving sales by going direct to shoppers online. Some brands successfully navigated the transition, like outdoor pizza oven maker Ooni, whose sales exploded during lockdown, with annual revenue up from £13.7 million ($167 million) in 2019 to £52.7 million in 2020. Shoppers also adapted--around 60 percent purchased from a direct-to-consumer brand at least once in 2021.

If scientific discovery is one of the main driving forces of human progress, insight is the fuel for the engine, which has long attracted behavior-level research to understand and model its underlying cognitive process. However, current tasks that abstract scientific discovery mostly focus on the emergence of insight, ignoring the special role played by domain knowledge. In this concept paper, we view scientific discovery as an interplay between $thinking \ out \ of \ the \ box$ that actively seeks insightful solutions and $thinking \ inside \ the \ box$ that generalizes on conceptual domain knowledge to keep correct. Accordingly, we propose Mindle, a semantic searching game that triggers scientific-discovery-like thinking spontaneously, as infrastructure for exploring scientific discovery on a large scale. On this basis, the meta-strategies for insights and the usage of concepts can be investigated reciprocally. In the pilot studies, several interesting observations inspire elaborated hypotheses on meta-strategies, context, and individual diversity for further investigations.