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As the demand of wireless communication continues to rise, the radio spectrum (a finite resource) requires increasingly efficient utilization. This trend is driving the evolution from static, stand-alone spectrum allocation toward spectrum sharing and dynamic spectrum sharing. A critical element of this transition is spectrum sensing, which facilitates informed decision-making in shared environments. Previous studies on spectrum sensing and cognitive radio have been largely limited to individual sensors or small sensor groups. In this work, a large-scale spectrum sensing network (LarS-Net) is designed in a cost-effective manner. Spectrum sensors are either co-located with base stations (BSs) to share the tower, backhaul, and power infrastructure, or integrated directly into BSs as a new feature leveraging active BS antenna systems. As an example incumbent system, fixed service microwave link operating in the lower-7 GHz band is investigated. This band is a primary candidate for 6G, being considered by the WRC-23, ITU, and FCC. Based on Monte Carlo simulations, we determine the minimum subset of BSs equipped with sensing capability to guarantee a target incumbent detection probabili

In the first part of this paper I will describe my work together with Lars and in the second part I will give a look at some of Lars's oldest papers.

The Landau-Nordita conference that was held in Moscow in 1981 was the first place where I met Lars. This "marvellous meeting", as Lars quoted in one of his reminiscence article, was organised by Alan Luther. It was an event that generated a long-lasting friendship of scientists from East and West, provided a background for the future collaborations and, in particular, my own collaboration with Lars on a supersymmetric high-spin extension of the Poincaré group.

We present the Light Augmented Reality System LARS as an open-source and cost-effective tool. LARS leverages light-projected visual scenes for indirect robot-robot and human-robot interaction through the real environment. It operates in real-time and is compatible with a range of robotic platforms, from miniature to middle-sized robots. LARS can support researchers in conducting experiments with increased freedom, reliability, and reproducibility. This XR tool makes it possible to enrich the environment with full control by adding complex and dynamic objects while keeping the properties of robots as realistic as they are.

This is the story of the first Fields Medal awarded to Lars Ahlfors. It was smuggled out of Finland in 1944, pawned in Sweden during World War II, and returned to Helsinki in 2004. This article is based on an interview with Ahlfors' second daughter Vanessa Gruen, and established biographical sources.

We give some personal reflections on the person and scientist Lars Brink and on some of his scientific achievements. Our relations to Lars are briefly described in [1] and [2], while the sources relevant for this text are summarised in [3].

The progress in maritime obstacle detection is hindered by the lack of a diverse dataset that adequately captures the complexity of general maritime environments. We present the first maritime panoptic obstacle detection benchmark LaRS, featuring scenes from Lakes, Rivers and Seas. Our major contribution is the new dataset, which boasts the largest diversity in recording locations, scene types, obstacle classes, and acquisition conditions among the related datasets. LaRS is composed of over 4000 per-pixel labeled key frames with nine preceding frames to allow utilization of the temporal texture, amounting to over 40k frames. Each key frame is annotated with 8 thing, 3 stuff classes and 19 global scene attributes. We report the results of 27 semantic and panoptic segmentation methods, along with several performance insights and future research directions. To enable objective evaluation, we have implemented an online evaluation server. The LaRS dataset, evaluation toolkit and benchmark are publicly available at: https://lojzezust.github.io/lars-dataset

The notion of SIC-POVMs comes from quantum information theory, and they were not on the horizon when I was Lars Brink's student in the early 80s. In the summer of 2022 I told Lars that I know how to use number theoretical insights to construct SIC-POVMs in any Hilbert space of dimension $n^2+3$, and that the construction provides a geometric setting for some deep number theoretical conjectures. I will give a sketch of this development, of what it was like to be Lars' student, and of what his reaction to our construction was.

The adaptive LASSO has been used for consistent variable selection in place of LASSO in the linear regression model. In this article, we propose a modified LARS algorithm to combine adaptive LASSO with some biased estimators, namely the Almost Unbiased Ridge Estimator (AURE), Liu Estimator (LE), Almost Unbiased Liu Estimator (AULE), Principal Component Regression Estimator (PCRE), r-k class estimator, and r-d class estimator. Furthermore, we examine the performance of the proposed algorithm using a Monte Carlo simulation study and real-world examples.

Chain-of-thought (CoT) prompting is a popular in-context learning (ICL) approach for large language models (LLMs), especially when tackling complex reasoning tasks. Traditional ICL approaches construct prompts using examples that contain questions similar to the input question. However, CoT prompting, which includes crucial intermediate reasoning steps (rationales) within its examples, necessitates selecting examples based on these rationales rather than the questions themselves. Existing methods require human experts or pre-trained LLMs to describe the skill, a high-level abstraction of rationales, to guide the selection. These methods, however, are often costly and difficult to scale. Instead, this paper introduces a new approach named Latent Reasoning Skills (LaRS) that employs unsupervised learning to create a latent space representation of rationales, with a latent variable called a reasoning skill. Concurrently, LaRS learns a reasoning policy to determine the required reasoning skill for a given question. Then the ICL examples are selected by aligning the reasoning skills between past examples and the question. This approach is theoretically grounded and compute-efficient, el

Increasing the batch size of a deep learning model is a challenging task. Although it might help in utilizing full available system memory during training phase of a model, it results in significant loss of test accuracy most often. LARS solved this issue by introducing an adaptive learning rate for each layer of a deep learning model. However, there are doubts on how popular distributed machine learning systems such as SystemML or MLlib will perform with this optimizer. In this work, we apply LARS optimizer to a deep learning model implemented using SystemML.We perform experiments with various batch sizes and compare the performance of LARS optimizer with \textit{Stochastic Gradient Descent}. Our experimental results show that LARS optimizer performs significantly better than Stochastic Gradient Descent for large batch sizes even with the distributed machine learning framework, SystemML.

Extreme learning machine (ELM) as a neural network algorithm has shown its good performance, such as fast speed, simple structure etc, but also, weak robustness is an unavoidable defect in original ELM for blended data. We present a new machine learning framework called LARSEN-ELM for overcoming this problem. In our paper, we would like to show two key steps in LARSEN-ELM. In the first step, preprocessing, we select the input variables highly related to the output using least angle regression (LARS). In the second step, training, we employ Genetic Algorithm (GA) based selective ensemble and original ELM. In the experiments, we apply a sum of two sines and four datasets from UCI repository to verify the robustness of our approach. The experimental results show that compared with original ELM and other methods such as OP-ELM, GASEN-ELM and LSBoost, LARSEN-ELM significantly improve robustness performance while keeping a relatively high speed.

We propose a feature selection method that finds non-redundant features from a large and high-dimensional data in nonlinear way. Specifically, we propose a nonlinear extension of the non-negative least-angle regression (LARS) called N${}^3$LARS, where the similarity between input and output is measured through the normalized version of the Hilbert-Schmidt Independence Criterion (HSIC). An advantage of N${}^3$LARS is that it can easily incorporate with map-reduce frameworks such as Hadoop and Spark. Thus, with the help of distributed computing, a set of features can be efficiently selected from a large and high-dimensional data. Moreover, N${}^3$LARS is a convex method and can find a global optimum solution. The effectiveness of the proposed method is first demonstrated through feature selection experiments for classification and regression with small and high-dimensional datasets. Finally, we evaluate our proposed method over a large and high-dimensional biology dataset.

Stream reasoning systems are designed for complex decision-making from possibly infinite, dynamic streams of data. Modern approaches to stream reasoning are usually performing their computations using stand-alone solvers, which incrementally update their internal state and return results as the new portions of data streams are pushed. However, the performance of such approaches degrades quickly as the rates of the input data and the complexity of decision problems are growing. This problem was already recognized in the area of stream processing, where systems became distributed in order to allocate vast computing resources provided by clouds. In this paper we propose a distributed approach to stream reasoning that can efficiently split computations among different solvers communicating their results over data streams. Moreover, in order to increase the throughput of the distributed system, we suggest an interval-based semantics for the LARS language, which enables significant reductions of network traffic. Performed evaluations indicate that the distributed stream reasoning significantly outperforms existing stand-alone LARS solvers when the complexity of decision problems and the