CD20-targeting in B-cell malignancies: novel prospects for antibodies and combination therapies

Expression of CD20 antigen by the most of transformed B cells is believed to be the driving force for targeting this molecule by using anti-CD20 monoclonal antibodies. While it is true that most lymphoma/leukemia patients can be cured, these regimens are limited by the emergence of treatment resistance. Based on these observations, development of anti-CD20 monoclonal antibodies and combination therapies have been recently proposed, in particular with the aim to optimize the cytotoxic activity. Here we outline a range of new experimental agents concerning the CD20 positive B-cell tumors which provide high benefit from conventional therapy. © 2016, Springer Science+Business Media New York

Robot Learning from Demonstration Using Elastic Maps

Learning from Demonstration (LfD) is a popular method of reproducing and generalizing robot skills from human-provided demonstrations. In this paper, we propose a novel optimization-based LfD method that encodes demonstrations as elastic maps. An elastic map is a graph of nodes connected through a mesh of springs. We build a skill model by fitting an elastic map to the set of demonstrations. The formulated optimization problem in our approach includes three objectives with natural and physical interpretations. The main term rewards the mean squared error in the Cartesian coordinate. The second term penalizes the non-equidistant distribution of points resulting in the optimum total length of the trajectory. The third term rewards smoothness while penalizing nonlinearity. These quadratic objectives form a convex problem that can be solved efficiently with local optimizers. We examine nine methods for constructing and weighting the elastic maps and study their performance in robotic tasks. We also evaluate the proposed method in several simulated and real-world experiments using a UR5e manipulator arm, and compare it to other LfD approaches to demonstrate its benefits and flexibility across a variety of metrics.Comment: 7 pages, 9 figures, 3 tables. Accepted to IROS 2022. Code available at: https://github.com/brenhertel/ElMapTrajectories Accompanying video at: https://youtu.be/rZgN9Pkw0t

Know abnormal, find evil : frequent pattern mining for ransomware threat hunting and intelligence

Emergence of crypto-ransomware has significantly changed the cyber threat landscape. A crypto ransomware removes data custodian access by encrypting valuable data on victims’ computers and requests a ransom payment to reinstantiate custodian access by decrypting data. Timely detection of ransomware very much depends on how quickly and accurately system logs can be mined to hunt abnormalities and stop the evil. In this paper we first setup an environment to collect activity logs of 517 Locky ransomware samples, 535 Cerber ransomware samples and 572 samples of TeslaCrypt ransomware. We utilize Sequential Pattern Mining to find Maximal Frequent Patterns (MFP) of activities within different ransomware families as candidate features for classification using J48, Random Forest, Bagging and MLP algorithms. We could achieve 99% accuracy in detecting ransomware instances from goodware samples and 96.5% accuracy in detecting family of a given ransomware sample. Our results indicate usefulness and practicality of applying pattern mining techniques in detection of good features for ransomware hunting. Moreover, we showed existence of distinctive frequent patterns within different ransomware families which can be used for identification of a ransomware sample family for building intelligence about threat actors and threat profile of a given target

Design and Evaluation of a Bioinspired Tendon-Driven 3D-Printed Robotic Eye with Active Vision Capabilities

The field of robotics has seen significant advancements in recent years, particularly in the development of humanoid robots. One area of research that has yet to be fully explored is the design of robotic eyes. In this paper, we propose a computer-aided 3D design scheme for a robotic eye that incorporates realistic appearance, natural movements, and efficient actuation. The proposed design utilizes a tendon-driven actuation mechanism, which offers a broad range of motion capabilities. The use of the minimum number of servos for actuation, one for each agonist-antagonist pair of muscles, makes the proposed design highly efficient. Compared to existing ones in the same class, our designed robotic eye comprises aesthetic and realistic features. We evaluate the robot's performance using a vision-based controller, which demonstrates the effectiveness of the proposed design in achieving natural movement, and efficient actuation. The experiment code, toolbox, and printable 3D sketches of our design have been open-sourced

Impact of Relational Networks in Multi-Agent Learning: A Value-Based Factorization View

Effective coordination and cooperation among agents are crucial for accomplishing individual or shared objectives in multi-agent systems. In many real-world multi-agent systems, agents possess varying abilities and constraints, making it necessary to prioritize agents based on their specific properties to ensure successful coordination and cooperation within the team. However, most existing cooperative multi-agent algorithms do not take into account these individual differences, and lack an effective mechanism to guide coordination strategies. We propose a novel multi-agent learning approach that incorporates relationship awareness into value-based factorization methods. Given a relational network, our approach utilizes inter-agents relationships to discover new team behaviors by prioritizing certain agents over other, accounting for differences between them in cooperative tasks. We evaluated the effectiveness of our proposed approach by conducting fifteen experiments in two different environments. The results demonstrate that our proposed algorithm can influence and shape team behavior, guide cooperation strategies, and expedite agent learning. Therefore, our approach shows promise for use in multi-agent systems, especially when agents have diverse properties.Comment: Accepted to International Conference on Decision and Control (IEEE CDC 2023

A Multi-Robot Task Assignment Framework for Search and Rescue with Heterogeneous Teams

In post-disaster scenarios, efficient search and rescue operations involve collaborative efforts between robots and humans. Existing planning approaches focus on specific aspects but overlook crucial elements like information gathering, task assignment, and planning. Furthermore, previous methods considering robot capabilities and victim requirements suffer from time complexity due to repetitive planning steps. To overcome these challenges, we introduce a comprehensive framework__the Multi-Stage Multi-Robot Task Assignment. This framework integrates scouting, task assignment, and path-planning stages, optimizing task allocation based on robot capabilities, victim requirements, and past robot performance. Our iterative approach ensures objective fulfillment within problem constraints. Evaluation across four maps, comparing with a state-of-the-art baseline, demonstrates our algorithm's superiority with a remarkable 97 percent performance increase. Our code is open-sourced to enable result replication.Comment: The 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023 Advances in Multi-Agent Learning - Coordination, Perception, and Control Workshop

Collaborative Adaptation: Learning to Recover from Unforeseen Malfunctions in Multi-Robot Teams

Cooperative multi-agent reinforcement learning (MARL) approaches tackle the challenge of finding effective multi-agent cooperation strategies for accomplishing individual or shared objectives in multi-agent teams. In real-world scenarios, however, agents may encounter unforeseen failures due to constraints like battery depletion or mechanical issues. Existing state-of-the-art methods in MARL often recover slowly -- if at all -- from such malfunctions once agents have already converged on a cooperation strategy. To address this gap, we present the Collaborative Adaptation (CA) framework. CA introduces a mechanism that guides collaboration and accelerates adaptation from unforeseen failures by leveraging inter-agent relationships. Our findings demonstrate that CA enables agents to act on the knowledge of inter-agent relations, recovering from unforeseen agent failures and selecting appropriate cooperative strategies.Comment: Presented at Multi-Agent Dynamic Games (MADGames) workshop at IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023