We propose a new model for fair division and vehicle routing, where drivers have monotone profit preferences, and their vehicles have feasibility constraints, for customer requests. For this model, we design two new axiomatic notions for fairness for drivers: FEQ1 and FEF1. FEQ1 encodes driver pairwise bounded equitability. FEF1 encodes driver pairwise bounded envy freeness. We compare FEQ1 and FEF1 with popular fair division notions such as EQ1 and EF1. We also give algorithms for guaranteeing FEQ1 and FEF1, respectively.

Malware is a major threat to computer systems and imposes many challenges to cyber security. Targeted threats, such as ransomware, cause millions of dollars in losses every year. The constant increase of malware infections has been motivating popular antiviruses (AVs) to develop dedicated detection strategies, which include meticulously crafted machine learning (ML) pipelines. However, malware developers unceasingly change their samples' features to bypass detection. This constant evolution of malware samples causes changes to the data distribution (i.e., concept drifts) that directly affect ML model detection rates, something not considered in the majority of the literature work. In this work, we evaluate the impact of concept drift on malware classifiers for two Android datasets: DREBIN (about 130K apps) and a subset of AndroZoo (about 285K apps). We used these datasets to train an Adaptive Random Forest (ARF) classifier, as well as a Stochastic Gradient Descent (SGD) classifier. We also ordered all datasets samples using their VirusTotal submission timestamp and then extracted features from their textual attributes using two algorithms (Word2Vec and TF-IDF). Then, we conducted experiments comparing both feature extractors, classifiers, as well as four drift detectors (DDM, EDDM, ADWIN, and KSWIN) to determine the best approach for real environments. Finally, we compare some possible approaches to mitigate concept drift and propose a novel data stream pipeline that updates both the classifier and the feature extractor. To do so, we conducted a longitudinal evaluation by (i) classifying malware samples collected over nine years (2009-2018), (ii) reviewing concept drift detection algorithms to attest its pervasiveness, (iii) comparing distinct ML approaches to mitigate the issue, and (iv) proposing an ML data stream pipeline that outperformed literature approaches.

Imaging markers of cerebral small vessel disease provide valuable information on brain health, but their manual assessment is time-consuming and hampered by substantial intra- and interrater variability. Automated rating may benefit biomedical research, as well as clinical assessment, but diagnostic reliability of existing algorithms is unknown. Here, we present the results of the \textit{VAscular Lesions DetectiOn and Segmentation} (\textit{Where is VALDO?}) challenge that was run as a satellite event at the international conference on Medical Image Computing and Computer Aided Intervention (MICCAI) 2021. This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels. Overall, 12 teams participated in the challenge proposing solutions for one or more tasks (4 for Task 1 - EPVS, 9 for Task 2 - Microbleeds and 6 for Task 3 - Lacunes). Multi-cohort data was used in both training and evaluation. Results showed a large variability in performance both across teams and across tasks, with promising results notably for Task 1 - EPVS and Task 2 - Microbleeds and not practically useful results yet for Task 3 - Lacunes. It also highlighted the performance inconsistency across cases that may deter use at an individual level, while still proving useful at a population level.

Iterating on creating pixel art character sprite sheets is essential to the game development process. However, it can take a lot of effort until the final versions containing different poses and animation clips are achieved. This paper investigates using conditional generative adversarial networks to aid the designers in creating such sprite sheets. We propose an architecture based on Pix2Pix to generate images of characters facing a target side (e.g., right) given sprites of them in a source pose (e.g., front). Experiments with small pixel art datasets yielded promising results, resulting in models with varying degrees of generalization, sometimes capable of generating images very close to the ground truth. We analyze the results through visual inspection and quantitatively with FID.

Role requiring'No experience data provided' months of experience in None Samsara (NYSE: IOT) is the pioneer of the Connected Operations Cloud, which allows businesses that depend on physical operations to harness IoT (Internet of Things) data to develop actionable business insights and improve their operations. Founded in San Francisco in 2015, we now employ more than 1,800 people globally and have over 1.5 million active devices. Samsara also went public in December 2021 and we're just getting started. Recent awards we've won include: • #2 in the Financial Times' Fastest Growing Companies in Americas list 2021 • Named as a Best Place to Work in Built In 2022 • #19 in the Forbes Cloud 100 2021 • IoT Analytics Company of the Year in 2022's IoT Breakthrough Winners • Forbes Advisor named us the Best Solution for Large Companies – Fleet management software for 2022! We're driving change in industries that are yet to fully embrace digital transformation. Physical operations make up a massive slice of the global economy but haven't benefited from innovation and actionable information in the way that other sectors have.

Levita Magnetics said it has raised $26 million to fund regulatory and commercial progress on its Magnetic-Assisted Robotic Surgery (MARS) platform. The Menlo Park, California-based robotic surgery system developer also appointed Maria Sainz as chair of its board of directors. The MARS platform is designed to help surgeons perform more high-volume abdominal procedures using fewer incisions and less personnel. Levita won FDA de novo classification for its Levita Magnetic Surgical System in 2015. That handheld device uses a magnet placed outside of a patient's abdomen to control a magnetic grasper inside the body during surgery, requiring only one incision instead of two.

Role requiring'No experience data provided' months of experience in Los Angeles Accentures SAP practice in the West, and we bring the New to life using design thinking, agile development methodologies, and the latest smart tech for SAP when it comes to automation and AI. We help out clients apply intelligence to set their business apart and make them more proactive, predictive and productive the power of the intelligent enterprise. We have also announced our partnership with SAP to develop SAPs new Responsible Production and Design solution, which will help companies consume fewer resources and build sustainability into their design processes. We believe sustainability is going to be the next digital, says Julie Sweet. Im hopeful that by 2025, well be able to say every business is a sustainable business.

The internet is collectively obsessed with art-generating AIs lately, and one of the newest tools is Midjourney. And while the full version requires a monthly subscription, Midjourney is free to use for up to 25 images, and you can browse and download images made by other users even if you never sign up for a paid account. Midjourney's images are impressive compared to other art-generating apps out there, with results that almost look like they were made by human artists. And I am really emphasizing the "almost" in that sentence. This is a machine mashing together works from real human painters, illustrators, and digital artists (none of whom are compensated for their work being used in this way, by the way).

Recent self-supervised approaches have used large-scale image-text datasets to learn powerful representations that transfer to many tasks without finetuning. These methods often assume that there is one-to-one correspondence between its images and their (short) captions. However, many tasks require reasoning about multiple images and long text narratives, such as describing news articles with visual summaries. Thus, we explore a novel setting where the goal is to learn a self-supervised visual-language representation that is robust to varying text length and the number of images. In addition, unlike prior work which assumed captions have a literal relation to the image, we assume images only contain loose illustrative correspondence with the text. To explore this problem, we introduce a large-scale multimodal dataset containing over 31M articles, 22M images and 1M videos. We show that state-of-the-art image-text alignment methods are not robust to longer narratives with multiple images. Finally, we introduce an intuitive baseline that outperforms these methods on zero-shot image-set retrieval by 10% on the GoodNews dataset.

Procedural Content Generation via Reinforcement Learning (PCGRL) foregoes the need for large human-authored data-sets and allows agents to train explicitly on functional constraints, using computable, user-defined measures of quality instead of target output. We explore the application of PCGRL to 3D domains, in which content-generation tasks naturally have greater complexity and potential pertinence to real-world applications. Here, we introduce several PCGRL tasks for the 3D domain, Minecraft (Mojang Studios, 2009). These tasks will challenge RL-based generators using affordances often found in 3D environments, such as jumping, multiple dimensional movement, and gravity. We train an agent to optimize each of these tasks to explore the capabilities of previous research in PCGRL. This agent is able to generate relatively complex and diverse levels, and generalize to random initial states and control targets. Controllability tests in the presented tasks demonstrate their utility to analyze success and failure for 3D generators.