It was previously thought that children younger than 7 couldn't find efficient solutions to complex problems, but new research suggests that much earlier, children can happen upon known sorting algorithms used by computer scientists Complex problem-solving may arise earlier in a child's development than previously thought Children as young as 4 years old are capable of finding efficient solutions to complex problems, such as independently inventing sorting algorithms developed by computer scientists. The scientists behind the finding say these skills emerge far earlier than previously thought, and should force a rethink of developmental psychology. Take control of your brain's master switch to optimise how you think Experiments carried out by Swiss psychologist Jean Piaget and widely popularised in the 1960s asked children to physically sort a collection of sticks into length order, a task Piaget called seriation. His tests revealed until around age 7, children applied no structured strategies; they approached the problem in messy ways through trial and error. But new research by Huiwen Alex Yang and his colleagues at University of California, Berkeley, shows a minority of even 4-year-old children can develop algorithmic solutions to the same task, and by 5 years old more than a quarter are capable of the same thing.

As the use of large language models (LLMs) for code generation becomes more prevalent in software development, it is critical to enhance both the efficiency and correctness of the generated code. Existing methods and models primarily focus on the correctness of LLM-generated code, ignoring efficiency. In this work, we present Effi-Code, an approach to enhancing code generation in LLMs that can improve both efficiency and correctness. We introduce a Self-Optimization process based on Overhead Profiling that leverages open-source LLMs to generate a high-quality dataset of correct and efficient code samples. This dataset is then used to fine-tune various LLMs. Our method involves the iterative refinement of generated code, guided by runtime performance metrics and correctness checks. Extensive experiments demonstrate that models fine-tuned on the Effi-Code show significant improvements in both code correctness and efficiency across task types. For example, the pass@1 of DeepSeek-Coder-6.7B-Instruct generated code increases from \textbf{43.3\%} to \textbf{76.8\%}, and the average execution time for the same correct tasks decreases by \textbf{30.5\%}. Effi-Code offers a scalable and generalizable approach to improving code generation in AI systems, with potential applications in software development, algorithm design, and computational problem-solving. The source code of Effi-Code was released in \url{https://github.com/huangd1999/Effi-Code}.

Hidden Markov Models (HMMs) are powerful tools for modeling sequential data, where the underlying states evolve in a stochastic manner and are only indirectly observable. Traditional HMM approaches are well-established for linear sequences, and have been extended to other structures such as trees. In this paper, we extend the framework of HMMs on trees to address scenarios where the tree-like structure of the data includes coupled branches -- a common feature in biological systems where entities within the same lineage exhibit dependent characteristics. We develop a dynamic programming algorithm that efficiently solves the likelihood, decoding, and parameter learning problems for tree-based HMMs with coupled branches. Our approach scales polynomially with the number of states and nodes, making it computationally feasible for a wide range of applications and does not suffer from the underflow problem. We demonstrate our algorithm by applying it to simulated data and propose self-consistency checks for validating the assumptions of the model used for inference. This work not only advances the theoretical understanding of HMMs on trees but also provides a practical tool for analyzing complex biological data where dependencies between branches cannot be ignored.

We study the problem of learning the objective functions or constraints of a multiobjective decision making model, based on a set of sequentially arrived decisions. In particular, these decisions might not be exact and possibly carry measurement noise or are generated with the bounded rationality of decision makers. In this paper, we propose a general online learning framework to deal with this learning problem using inverse multiobjective optimization. More precisely, we develop two online learning algorithms with implicit update rules which can handle noisy data. Numerical results show that both algorithms can learn the parameters with great accuracy and are robust to noise.

Each BUS image is fed into the trained generative network to obtain the boundary of the tumor, and then 13 statistical features from that boundary are computed: fractal dimension, lacunarity, convex hull, convexity, circularity, area, perimeter, centroid, minor and major axis length, smoothness, Hu moments (6) and central moments (order 3 and below). Exhaustive Feature Selection (EFS) algorithm is used to select the best set of features. The EFS algorithm indicates that the fractal dimension, lacunarity, convex hull, and centroid are the 4 optimal features. The selected features are fed into a Random Forest classifier, which is later trained to discriminate between benign and malignant tumors. Each BUS image is fed into the trained generative network to obtain the boundary of the tumor, and then 13 statistical features from that boundary are computed: fractal dimension, lacunarity, convex hull, convexity, circularity, area, perimeter, centroid, minor and major axis length, smoothness, Hu moments (6) and central moments (order 3 and below).

The Karush-Kuhn-Tucker Conditions are a set of first derivative tests or also can be set of conditions. Abstract: This expository paper contains a concise introduction to some significant works concerning the Karush-Kuhn-Tucker condition, a necessary condition for a solution in local optimality in problems with equality and inequality constraints. The study of this optimality condition has a long history and culminated in the appearance of subdifferentials. The 1970s and early 1980s were important periods for new developments and various generalizations of subdifferentials were introduced, including the Clarke subdifferential and Demyanov-Rubinov quasidifferential. In this paper, we mainly present four generalized Karush-Kuhn-Tucker conditions or Fritz John conditions in variational analysis and set-valued analysis via Lagrange multiplier methods besides Fr$\Acute{e}$chet differentiable situation, namely subdifferentials of convex functions, generalized gradients of locally Lipschitz functions, quasidifferentials of quasidifferentiable functions and contingent epiderivatives of set-valued maps and discuss the limits of Lagrangian methods slightly in the last chapter.

This paper presents a new robustness concept for uncertain multi-objective optimization problems. More precisely, in the paper the so-called recovery-to-efficiency robustness concept is proposed and investigated. Several approaches for generating recovery-to-efficiency robust sets in the context of multi-objective optimization are proposed as well. An extensive experimental analysis is performed to disclose differences among robust sets obtained using different concepts as well as to deduce some interesting observations. For testing purposes, instances from the bi-objective knapsack problem are considered.

This "Python OOP: Object Oriented Programming in Python" course provides good understanding of object oriented concepts and implementation in Python programming. Design and development of a product requires great understanding of implementation language. The complexity of real world application requires the use of strength of language to provide robust, flexible and efficient solutions. Python provides the Object Oriented capability and lot of rich features to stand with changing demand of current world application requirement. This "Python OOP: Object Oriented Programming in Python" tutorial explains the Object Oriented features of Python programming in step-wise manner.

From your Google voice assistant to your'Netflix and chill' recommendations to the very humble Grammarly -- they're all powered by deep learning. Deep learning has become one of the primary research areas in artificial intelligence. Most of the well-known applications of artificial intelligence, such as image processing, speech recognition and translations, and object identification are carried out by deep learning. Thus, deep learning has the potential to solve most business problems, streamlining your work procedures, or creating useful products for end customers. However, there are certain deep learning challenges that you should be aware of, before going ahead with business decisions involving deep learning.