Solid weak BCC-algebras

We characterize weak BCC-algebras in which the identity $(xy)z=(xz)y$ is satisfied only in the case when elements $x,y$ belong to the same branch

Representations of (2,n)(2,n)-semigroups by multiplace functions

We describe the representations of $(2,n)$-semigroups, i.e. groupoids with $n$ binary associative operations, by partial $n$-place functions and prove that any such representation is a union of some family of representations induced by Schein's determining pairs.Comment: 17 page

ADDMC: Weighted Model Counting with Algebraic Decision Diagrams

We present an algorithm to compute exact literal-weighted model counts of Boolean formulas in Conjunctive Normal Form. Our algorithm employs dynamic programming and uses Algebraic Decision Diagrams as the primary data structure. We implement this technique in ADDMC, a new model counter. We empirically evaluate various heuristics that can be used with ADDMC. We then compare ADDMC to state-of-the-art exact weighted model counters (Cachet, c2d, d4, and miniC2D) on 1914 standard model counting benchmarks and show that ADDMC significantly improves the virtual best solver.Comment: Presented at AAAI 202

Redox signals at the ER-mitochondria interface control melanoma progression.

Reactive oxygen species (ROS) are emerging as important regulators of cancer growth and metastatic spread. However, how cells integrate redox signals to affect cancer progression is not fully understood. Mitochondria are cellular redox hubs, which are highly regulated by interactions with neighboring organelles. Here, we investigated how ROS at the endoplasmic reticulum (ER)-mitochondria interface are generated and translated to affect melanoma outcome. We show that TMX1 and TMX3 oxidoreductases, which promote ER-mitochondria communication, are upregulated in melanoma cells and patient samples. TMX knockdown altered mitochondrial organization, enhanced bioenergetics, and elevated mitochondrial- and NOX4-derived ROS. The TMX-knockdown-induced oxidative stress suppressed melanoma proliferation, migration, and xenograft tumor growth by inhibiting NFAT1. Furthermore, we identified NFAT1-positive and NFAT1-negative melanoma subgroups, wherein NFAT1 expression correlates with melanoma stage and metastatic potential. Integrative bioinformatics revealed that genes coding for mitochondrial- and redox-related proteins are under NFAT1 control and indicated that TMX1, TMX3, and NFAT1 are associated with poor disease outcome. Our study unravels a novel redox-controlled ER-mitochondria-NFAT1 signaling loop that regulates melanoma pathobiology and provides biomarkers indicative of aggressive disease

Physics Opportunities with the 12 GeV Upgrade at Jefferson Lab

This white paper summarizes the scientific opportunities for utilization of the upgraded 12 GeV Continuous Electron Beam Accelerator Facility (CEBAF) and associated experimental equipment at Jefferson Lab. It is based on the 52 proposals recommended for approval by the Jefferson Lab Program Advisory Committee.The upgraded facility will enable a new experimental program with substantial discovery potential to address important topics in nuclear, hadronic, and electroweak physics.Comment: 64 page

Efficient exploration of unknown indoor environments using a team of mobile robots

Whenever multiple robots have to solve a common task, they need to coordinate their actions to carry out the task efficiently and to avoid interferences between individual robots. This is especially the case when considering the problem of exploring an unknown environment with a team of mobile robots. To achieve efficient terrain coverage with the sensors of the robots, one first needs to identify unknown areas in the environment. Second, one has to assign target locations to the individual robots so that they gather new and relevant information about the environment with their sensors. This assignment should lead to a distribution of the robots over the environment in a way that they avoid redundant work and do not interfere with each other by, for example, blocking their paths. In this paper, we address the problem of efficiently coordinating a large team of mobile robots. To better distribute the robots over the environment and to avoid redundant work, we take into account the type of place a potential target is located in (e.g., a corridor or a room). This knowledge allows us to improve the distribution of robots over the environment compared to approaches lacking this capability. To autonomously determine the type of a place, we apply a classifier learned using the AdaBoost algorithm. The resulting classifier takes laser range data as input and is able to classify the current location with high accuracy. We additionally use a hidden Markov model to consider the spatial dependencies between nearby locations. Our approach to incorporate the information about the type of places in the assignment process has been implemented and tested in different environments. The experiments illustrate that our system effectively distributes the robots over the environment and allows them to accomplish their mission faster compared to approaches that ignore the place labels