Cutting planes (cuts) play an important role in solving mixed-integer linear programs (MILPs), which formulate many important real-world applications. Cut selection heavily depends on (P1) which cuts to prefer and (P2) how many cuts to select. Although modern MILP solvers tackle (P1)-(P2) by human-designed heuristics, machine learning carries the potential to learn more effective heuristics. However, many existing learning-based methods learn which cuts to prefer, neglecting the importance of learning how many cuts to select. Moreover, we observe that (P3) what order of selected cuts to prefer significantly impacts the efficiency of MILP solvers as well. To address these challenges, we propose a novel hierarchical sequence/set model (HEM) to learn cut selection policies. Specifically, HEM is a bi-level model: (1) a higher-level module that learns how many cuts to select, (2) and a lower-level module -- that formulates the cut selection as a sequence/set to sequence learning problem -- to learn policies selecting an ordered subset with the cardinality determined by the higher-level module. To the best of our knowledge, HEM is the first data-driven methodology that well tackles (P1)-(P3) simultaneously. Experiments demonstrate that HEM significantly improves the efficiency of solving MILPs on eleven challenging MILP benchmarks, including two Huawei's real problems.

This paper details the development of an interactive sculpture built from deprecated hardware technology and intentionally decomposable, transient materials. We detail a case study of "Strain" - an emotive prototype that reclaims two orphaned digital artifacts to power a kinetic sculpture made of common disposable objects. We use the Myo, an abandoned myoelectric armband, in concert with the Programmable Air, a soft-robotics prototyping project, to manipulate a pneumatic bladder array constructed from condoms, bamboo skewers, and a small library of 3D printed PLA plastic connectors designed to work with these generic parts. The resulting sculpture achieves surprisingly organic actuation. The goal of this project is to produce several reusable components: software to resuscitate the Myo Armband, homeostasis software for the Programmable Air or equivalent pneumatic projects, and a library of easily-printed parts that will work with generic bamboo disposables for sculptural prototyping. This project works to develop usable, repeatable engineering by applying it to a slightly whimsical object that promotes a strong emotional response in its audience. Through this, we transform the disposable into the sustainable. In this paper, we reflect on project-based insights into rescuing and revitalizing abandoned consumer electronics for future works.

Large language models augmented with task-relevant documents have demonstrated impressive performance on knowledge-intensive tasks. However, regarding how to obtain effective documents, the existing methods are mainly divided into two categories. One is to retrieve from an external knowledge base, and the other is to utilize large language models to generate documents. We propose an iterative retrieval-generation collaborative framework. It is not only able to leverage both parametric and non-parametric knowledge, but also helps to find the correct reasoning path through retrieval-generation interactions, which is very important for tasks that require multi-step reasoning. We conduct experiments on four question answering datasets, including single-hop QA and multi-hop QA tasks. Empirical results show that our method significantly improves the reasoning ability of large language models and outperforms previous baselines.

Since MIP linear problems include both continuous and integer variables, they are proved to belong to the NP-hard class (see [38] for a more detailed analysis), meaning that they are not solvable in polynomial time. The complete exploration of the integer feasible set, whose cardinality grows exponentially with the number of variables, is yet possible to achieve the optimal solution, but for most of the practically significant instances, it would require unacceptable computational effort. In fact, the only way to solve to optimality any mixed-integer problem is to apply some of the well-known Branch and Bound techniques. However, despite combinatorial optimization community provided a great deal of these algorithms, for which the reader should refer to [31, 34, 16], MIP problems complexity is inherent with their belonging to NP-hard class. Therefore, when tackling MIP problems, one either seeks particular structures allowing to bring down the complexity, such as the availability, for a given class of problems, of the optimal formulation or exploits cutting plane generation to dramatically reduce the feasible region dimension. However, we often encounter MIP problems without having any prior knowledge of possible structures and, thus, pursuing the globally optimal solution could be in practice impossible or inefficient, since for our purpose a sub-optimal approximation is considered to be good enough. This makes heuristics one of the most widespread and feasible ways to achieve sub-optimal solutions of MIP problems within an affordable computational time. For the purpose of highlighting the perspective of our research, we can define two classes of MIP heuristics: improvement heuristics and start heuristics.

There are a number of autonomous boats under development, but we've seen few commercial self-driving ships plying waterways. Now, a company called Sea Machines has announced that it will send an autonomous, remotely commanded tugboat on a 1,000 nautical mile (1,150 mile) "Machine Odyssey" voyage around Denmark. The tug ("Nellie Bly") will have "full onboard vessel control managed by autonomous technology," but be operated under the authority of officers located in the US. The aim is to show "global companies that operate the fleets of cargo ships, tugs, ferries, and the many other types of commercial workboats that they can integrate autonomous technology into their vessel operations for a host of technology-driven benefits." The tug will be steered by Sea Machines' SM300 autonomous system equipped with long-range computer vision. It's a "sensor-to-propeller" system that employs "path-planning, obstacle avoidance replanning, vectored nautical chart data and dynamic domain perception" to control a voyage from start to finish.

Norwegian waste management frontrunner Bjorstaddalen has opened the country's first robotic sorting facility for C&D and C&I waste in the municipality of Skien in Norway. The fully automated robotic sorting station supplied by ZenRobotics features robotic arms that will perform up to 6000 picks per hour. The robotic sorting station is set up as a standalone waste sorting process connected to Bjorstaddalen's existing material recycling facility that has a total capacity of 150 000 tons per hour. By investing in AI and robot technologies, Bjorstaddalen aims to become a leader in material recycling in Norway. The robotic sorting station will substantially increase material recovery, reducing waste incineration and making a major leap toward the circular economy.

Transportation occupies one-third of the amount in the logistics costs, and accordingly transportation systems largely influence the performance of the logistics system. This work presents an adaptive data-driven innovative modular approach for solving the real-world Vehicle Routing Problems (VRP) in the field of logistics. The work consists of two basic units: (i) an innovative multi-step algorithm for successful and entirely feasible solving of the VRP problems in logistics, (ii) an adaptive approach for adjusting and setting up parameters and constants of the proposed algorithm. The proposed algorithm combines several data transformation approaches, heuristics and Tabu search. Moreover, as the performance of the algorithm depends on the set of control parameters and constants, a predictive model that adaptively adjusts these parameters and constants according to historical data is proposed. A comparison of the acquired results has been made using the Decision Support System with predictive models: Generalized Linear Models (GLM) and Support Vector Machine (SVM). The algorithm, along with the control parameters, which using the prediction method were acquired, was incorporated into a web-based enterprise system, which is in use in several big distribution companies in Bosnia and Herzegovina. The results of the proposed algorithm were compared with a set of benchmark instances and validated over real benchmark instances as well. The successful feasibility of the given routes, in a real environment, is also presented.

The spinal cord is the vital link between the brain and body, a superhighway of life-critical information protected by the bony vertebra of the spinal column. The spinal column is the most common site for bone metastasis, when tumors spread from internal organs to the bones. Estimates indicate that at least 30% and as many as 70% of patients with cancer will experience spread of cancer to their spine. Each year, about 10,000 Americans develop primary or metastatic spinal cord tumors. "The spine is a place where people can miss lesions, especially very small metastases, which could be the difference between finding an early, treatable tumor and finding a tumor too late to be treatable," says Dr. Michael Fanariotis, chief radiologist for CT at the Telemark Hospital in Skien, Norway. About 6% of CT exams is dedicated to spine imaging, but many more exams benefit from information observed within the vertebral bodies and the discs between them.

MEPs have warned European countries must "seriously" consider introducing a general basic income to prepare for wide scale unemployment that could come as a result of robots taking over manual jobs. A draft report, tabled by a socialist MEP Mady Delvaux-Stehres, warns preparations must be made for what it describes as the "technological revolution" currently taking place, including provisions for the "possible effects on the labour market of robotics". The report, which passed by 17 votes to two and will be put in front of the entire European Parliament in February, urges member states to consider a general basic income in preparation for robots taking over people's jobs. It states: "In the light of the possible effects on the labour market of robotics and AI a general basic income should be seriously considered, and invites all Member States to do so." The resolution also suggests that a system of reporting on how robotics are affecting the economic results of companies should be established "for the purpose of taxation and social security contributions".

Norway has violated the human rights of mass killer Anders Breivik by keeping him in solidarity confinement in a three-cell complex where he can play video games, watch TV and exercise, a court has ruled. The ruling found in Oslo on Wednesday that Breivik - who killed 77 people in twin attacks in 2011 - had been subjected to strip searches, had been woken up hourly by guards for long periods and that the authorities had done little to alleviate the effect of his isolation. "The prohibition of inhuman and degrading treatment represents a fundamental value in a democratic society. This applies no matter what - also in the treatment of terrorists and killers," judge Helen Andenaes Sekulic said in her ruling. The state must pay Breivik's legal fees of more than 40,000, the judge ruled.