Pattern database (PDB) is one of the most popular automated heuristic generation techniques. A PDB maps states in a planning task to abstract states by considering a subset of variables and stores their optimal costs to the abstract goal in a look up table. As the result of the progress made on symbolic search over recent years, symbolic-PDB-based planners achieved impressive results in the International Planning Competition (IPC) 2018. Among them, Complementary 1 (CPC1) tied as the second best planners and the best non-portfolio planners in the cost optimal track, only 2 tasks behind the winner. It uses a combination of different pattern generation algorithms to construct PDBs that are complementary to existing ones. As shown in the post contest experiments, there is room for improvement. In this paper, we would like to present our work on refining the PDB construction mechanism of CPC1. By testing on IPC 2018 benchmarks, the results show that a significant improvement is made on our modified planner over the original version.

The rapid advancement of large language models (LLMs) has revolutionized role-playing, enabling the development of general role-playing models. However, current role-playing training has two significant issues: (I) Using a predefined role profile to prompt dialogue training for specific scenarios usually leads to inconsistencies and even conflicts between the dialogue and the profile, resulting in training biases. (II) The model learns to imitate the role based solely on the profile, neglecting profile-dialogue alignment at the sentence level. In this work, we propose a simple yet effective framework called BEYOND DIALOGUE, designed to overcome these hurdles. This framework innovatively introduces "beyond dialogue" tasks to align dialogue with profile traits based on each specific scenario, thereby eliminating biases during training. Furthermore, by adopting an innovative prompting mechanism that generates reasoning outcomes for training, the framework allows the model to achieve fine-grained alignment between profile and dialogue at the sentence level. The aforementioned methods are fully automated and low-cost. Additionally, the integration of automated dialogue and objective evaluation methods forms a comprehensive framework, paving the way for general role-playing. Experimental results demonstrate that our model excels in adhering to and reflecting various dimensions of role profiles, outperforming most proprietary general and specialized role-playing baselines. All code and datasets are available at https://github.com/yuyouyu32/BeyondDialogue.

Agent-based simulations have been used in modeling transportation systems for traffic management and passenger flows. In this work, we hope to shed light on the complex factors that influence transportation mode decisions within developing countries, using Colombia as a case study. We model an ecosystem of human agents that decide at each time step on the mode of transportation they would take to work. Their decision is based on a combination of their personal satisfaction with the journey they had just taken, which is evaluated across a personal vector of needs, the information they crowdsource from their prevailing social network, and their personal uncertainty about the experience of trying a new transport solution. We simulate different network structures to analyze the social influence for different decision-makers. We find that in low/medium connected groups inquisitive people actively change modes cyclically over the years while imitators cluster rapidly and change less frequently.

Within the framework of parameter dependent PDEs, we develop a constructive approach based on Deep Neural Networks for the efficient approximation of the parameter-to-solution map. The research is motivated by the limitations and drawbacks of state-of-the-art algorithms, such as the Reduced Basis method, when addressing problems that show a slow decay in the Kolmogorov n-width. Our work is based on the use of deep autoencoders, which we employ for encoding and decoding a high fidelity approximation of the solution manifold. To provide guidelines for the design of deep autoencoders, we consider a nonlinear version of the Kolmogorov n-width over which we base the concept of a minimal latent dimension. We show that the latter is intimately related to the topological properties of the solution manifold, and we provide theoretical results with particular emphasis on second order elliptic PDEs, characterizing the minimal dimension and the approximation errors of the proposed approach. The theory presented is further supported by numerical experiments, where we compare the proposed approach with classical POD-Galerkin reduced order models. In particular, we consider parametrized advection-diffusion PDEs, and we test the methodology in the presence of strong transport fields, singular terms and stochastic coefficients. Introduction In many areas of science, such as physics, biology and engineering, phenomena are modeled in terms of Partial Differential Equations (PDEs) that exhibit dependence on one or multiple parameters.

Talend has released the latest update to its Talend Data Fabric platform is adding several new features, including AI/ML, to more quickly reveal latent intelligence held inside dispersed enterprise data. The Talend Winter '20 release delivers trusted data quickly, reliably and at first sight for faster business outcomes, according to Talend execs. "The innovations introduced in Talend Data Fabric will provide our customers with dramatically improved efficiency, optimized productivity and scale, and accelerated path to revealing value from data," said Talend's Ciaran Dynes senior vice president products in a statement. Here's a list of notable features in Talend's Winter '20 release, and how they deliver value. Data Inventory: This new cloud-based app automatically inventories and quality checks data to reveal trusted data quickly and easily. This lets users more easily unlock data silos with efficient reuse and deeper trust.

As organizations became engulfed in big data – high-volume, high-velocity, and/or high-variety information assets – the question quickly became how to effectively derive insight and business value from it. "Big data naturally leads to advanced analytics. When we can capture a lot of information about a business topic that you can improve, you don't want just to scratch the surface. You want to discover the unknown, find out the root cause, predict what will happen, address issues with extreme precision," says Jean-Michel Franco, senior director of product at Talend. "This is more than what humans can do alone, without the help of the machine."

This article originally appeared in Vulture. Nothing is ever as it seems when it comes to James Franco. The man makes a lot of baffling "artistic" choices, any of which could conceivably be explained away as one of the performance-art pranks he so enjoys pulling on the public, and in a greater sense, on himself. Is he penning a column of film criticism, or engaging in an Adaptation-style interrogation of a self divorced from the self? Is he challenging the pillars of historical thought, or just putting goo on stuff?

Right before Laura Franco went to look at a three-bedroom apartment for rent in Santa Clara, Calif., in mid-January, she got a surprising text message from the property manager, Zenplace. "They said a robot would meet me at the property. I thought, 'a robot?' " said Ms. Franco, 31, an event planner and bartender. When she arrived at the listing, a text message provided her with a code she used to let herself in. Then a long-necked white robot on wheels, with a screen that looks like a small tablet, rolled up to her.

As Katy Franco waited for her morning coffee, passersby pulled out their phones and snapped photos and video of her barista. A man in his 20s did a double take, recorded the scene on his iPhone and posted it to Instagram. Another woman drifted toward the barista and asked no one in particular: "What's going on here?" Franco's barista was a robot. It's part of an automated coffee shop called Cafe X - the latest example of the San Francisco's dual infatuations: artisanal coffee and automated technology.

As Katy Franco waited for her morning coffee, passersby pulled out their phones and snapped photos and video of her barista. A man in his 20s did a double take, recorded the scene on his iPhone and posted it to Instagram. Another woman drifted toward the barista and asked no one in particular: "What's going on here?" Franco's barista was a robot. It's part of an automated coffee shop called Cafe X -- the latest example of the San Francisco's dual infatuations: artisanal coffee and automated technology.