Massive high-quality data, both pre-training raw texts and post-training annotations, have been carefully prepared to incubate advanced large language models (LLMs). In contrast, for information extraction (IE), pre-training data, such as BIO-tagged sequences, are hard to scale up. We show that IE models can act as free riders on LLM resources by reframing next-token \emph{prediction} into \emph{extraction} for tokens already present in the context. Specifically, our proposed next tokens extraction (NTE) paradigm learns a versatile IE model, \emph{Cuckoo}, with 102.6M extractive data converted from LLM's pre-training and post-training data. Under the few-shot setting, Cuckoo adapts effectively to traditional and complex instruction-following IE with better performance than existing pre-trained IE models. As a free rider, Cuckoo can naturally evolve with the ongoing advancements in LLM data preparation, benefiting from improvements in LLM training pipelines without additional manual effort.

In the humanities, it is rarely possible to resort to proof. Humanities are not built on the formulation of hypotheses and their proof or refutation. It is a field where different ways of describing its material (e.g., artistic culture) compete [Harpham, 2013]. Therefore, the question of text authorship is so important for humanists; it remains one of the few questions in the humanities that can be formulated as falsifiable and sometimes verifiable hypotheses. This is an area where humanists find themselves in a situation very similar to that in which representatives of the sciences usually exist. Consequently, this is the rhetorical resource that humanists can use in the struggle for resources in science and for symbolic capital in the scientific field.

Cuckoos infiltrate the nests of other birds with similar-looking eggs, but drongos have evolved a highly effective way to snuff out the imposters. Their ability to recognise the uniquely patterned marks of their own eggs, like a signature, means they may reject up to 94 per cent of cuckoo eggs. Instead of caring for their own offspring, African cuckoos (Cuculus gularis) lay a single egg in the nests of fork-tailed drongos (Dicrurus adsimilis), tossing out a drongo egg to match the original clutch count. If the young cuckoo is adopted and hatches, it immediately pushes out the remaining drongo eggs to become its hosts' only charge. Jess Lund at the University of Cape Town, South Africa, and her colleagues gathered 192 eggs – including 26 that had been laid by cuckoos – from fork-tailed drongo nests in the forests of southern Zambia.

With help from machine learning software, computer scientists may have unmasked the identity of Q, the founder of the QAnon movement. In a sprawling report published on Saturday, The New York Times shared the findings of two independent teams of forensic linguists who claim they've identified Paul Furber, a South African software developer who was one of the first to draw attention to the conspiracy theory, as the original writer behind Q. They say Arizona congressional candidate Ron Watkins also wrote under the pseudonym, first by collaborating with Furber and then later taking over the account when it eventually moved to post on his father's 8chan message board. The two teams of Swiss and French researchers used different methodologies to come to the same conclusion. The Swiss one, made up of two researchers from startup OrphAnalytics, used software to break down Q's missives into patterns of three-character sequences.

Nowadays, we are immersed in tens of newly-proposed evolutionary and swam-intelligence metaheuristics, which makes it very difficult to choose a proper one to be applied on a specific optimization problem at hand. On the other hand, most of these metaheuristics are nothing but slightly modified variants of the basic metaheuristics. For example, Differential Evolution (DE) or Shuffled Frog Leaping (SFL) are just Genetic Algorithms (GA) with a specialized operator or an extra local search, respectively. Therefore, what comes to the mind is whether the behavior of such newly-proposed metaheuristics can be investigated on the basis of studying the specifications and characteristics of their ancestors. In this paper, a comprehensive evaluation study on some basic metaheuristics i.e. Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Teaching-Learning-Based Optimization (TLBO), and Cuckoo Optimization algorithm (COA) is conducted, which give us a deeper insight into the performance of them so that we will be able to better estimate the performance and applicability of all other variations originated from them. A large number of experiments have been conducted on 20 different combinatorial optimization benchmark functions with different characteristics, and the results reveal to us some fundamental conclusions besides the following ranking order among these metaheuristics, {ABC, PSO, TLBO, GA, COA} i.e. ABC and COA are the best and the worst methods from the performance point of view, respectively. In addition, from the convergence perspective, PSO and ABC have significant better convergence for unimodal and multimodal functions, respectively, while GA and COA have premature convergence to local optima in many cases needing alternative mutation mechanisms to enhance diversification and global search.

Superhuman powers, shared memories, and the end of all disease are promising hopes coming out of brain-computer interface technology research, but what happens if an AI-human hybrid becomes infected with malware? "I teach you the Übermensch. Man is something to be surpassed. What have ye done to surpass man?"Friederich Nietzsche, "Thus Spoke Zarathustra" Have you ever found yourself passing by a restaurant, a place of business, or somewhere out in nature and you smell something familiar and immediately you are whisked away in time and space to a place in your memory that seems as real as the day you first experienced it? This happens automatically, without thinking, and things that were forgotten for years or even decades suddenly come flooding in.

Unlike the Greek sorceress it resembles in name, Midea Group doesn't have to be a prophet to realize it can no longer rest on cheap Chinese labor and low-end technology. That's why this top Chinese appliance manufacturer is willing to splash out for a German robotics giant. Shenzhen-listed Midea wants to lift its stake in Kuka to at least 30% from the current 13.5%, for a price that would value the German high-end robotics maker's shares at 5.2 billion. That investment may trigger an open offer for all of Kuka's shares....

A* search is a fundamental topic in artificial intelligence. Recently, the general purpose computation on graphics processing units (GPGPU) has been widely used to accelerate numerous computational tasks. In this paper, we propose the first parallel variant of the A* search algorithm such that the search process of an agent can be accelerated by a single GPU processor in a massively parallel fashion. Our experiments have demonstrated that the GPU-accelerated A* search is efficient in solving multiple real-world search tasks, including combinatorial optimization problems, pathfinding and game solving. Compared to the traditional sequential CPU-based A* implementation, our GPU-based A* algorithm can achieve a significant speedup by up to 45x on large-scale search problems.