Optimization of atomic structures presents a challenging problem, due to their highly rough and non-convex energy landscape, with wide applications in the fields of drug design, materials discovery, and mechanics. Here, we present a graph reinforcement learning approach, StriderNet, that learns a policy to displace the atoms towards low energy configurations. We evaluate the performance of StriderNet on three complex atomic systems, namely, binary Lennard-Jones particles, calcium silicate hydrates gel, and disordered silicon. We show that StriderNet outperforms all classical optimization algorithms and enables the discovery of a lower energy minimum. In addition, StriderNet exhibits a higher rate of reaching minima with energies, as confirmed by the average over multiple realizations. Finally, we show that StriderNet exhibits inductivity to unseen system sizes that are an order of magnitude different from the training system. All the codes and datasets are available at https://github.com/M3RG-IITD/StriderNET.

Using mRNA-Seq and clinical data for 469 clear cell Renal Cell Carcinoma (ccRCC) samples from The Cancer Genome Atlas (TCGA), we develop a protocol to identify patients likely to have early recurrence of their disease. We first split the data into two sets, with 380 samples in the training set and 89 samples in the test set. Using the training set, we identify genes whose outlier status (high or low mRNA expression) is predictive of recurrence, based on Kaplan-Meier recurrence free survival log-rank p-value. We find a significant overlap among genes identified as predictive biomarkers in Reads per Kilobase Million (RPKM) normalized data and Raw Reads mRNA-Seq data. Using 80 consensus genes predictive in both RPKM and Raw Reads data, we define an outlier-based risk score R to stratify patients into two groups, a high-risk (early recurrence) group (R2). The KM recurrence curve using this stratification shows excellent separation in training and test sets. Restricting the analysis to patients who had recurrence within two years (109 cases) and those who had no recurrence in five years (107 cases) we find that the risk predictor achieves ca. 80 percent sensitivity and specificity. The 80 genes identified by the outlier analysis were used to develop a more intuitive classifier based on Generalized Matrix Learning Vector Quantization (GMLVQ). This method stratifies samples into risk classes based on defining prototypes in feature space and an appropriate distance metric. GMLVQ identified a subset of 12 genes that have high accuracy in predicting recurrence, which suggests that an assay with a small number of genes might be able to predict recurrence in ccRCC.

Failure detectors are oracles that have been introduced to provide processes in asynchronous systems with information about faults. This information can then be used to solve problems otherwise unsolvable in asynchronous systems. A natural question is on the "minimum amount of information" a failure detector has to provide for a given problem. This question is classically addressed using a relation that states that a failure detector D is stronger (that is, provides "more, or better, information") than a failure detector D' if D can be used to implement D'. It has recently been shown that this classic implementability relation has some drawbacks. To overcome this, different relations have been defined, one of which states that a failure detector D is stronger than D' if D can solve all the time-free problems solvable by D'. In this paper we compare the implementability-based hierarchy of failure detectors to the hierarchy based on solvability. This is done by introducing a new proof technique for establishing the solvability relation. We apply this technique to known failure detectors from the literature and demonstrate significant differences between the hierarchies.

Failure detectors - oracles that provide information about process crashes - are an important abstraction for crash tolerance in distributed systems. Although current failure-detector theory provides great generality and expressiveness, it also poses significant challenges in developing a robust hierarchy of failure detectors. We address some of these challenges by proposing a variant of failure detectors called asynchronous failure detectors and an associated modeling framework. Unlike the traditional failure-detector framework, our framework eschews real time completely. We show that asynchronous failure detectors are sufficiently expressive to include several popular failure detectors. Additionally, we show that asynchronous failure detectors satisfy many desirable properties: they are self-implementable, guarantee that stronger asynchronous failure detectors solve more problems, and ensure that their outputs encode no information other than process crashes. We introduce the notion of a failure detector being representative of a problem to capture the idea that some problems encode the same information about process crashes as their weakest failure detectors do. We show that a large class of problems, called finite problems, do not have representative failure detectors.

This corrigendum corrects and clarifies our remarks in [2] from SPAA 2009 about the related work in [1] regarding the status of ◊P as the weakest failure detector for boosting obstruction-freedom to wait-freedom.

Failure detectors are a service that provides (approximate) information about process crashes in a distributed system. The well-known ldquoeventually perfectrdquo failure detector, diamP, has been implemented in partially synchronous systems with unknown upper bounds on message delay and relative process speeds. However, previous implementations have overlooked an important subtlety with respect to measuring the passage of time in ldquoceleratingrdquo environments, in which absolute process speeds can continually increase or decrease while maintaining bounds on relative process speeds. Existing implementations either use action clocks, which fail in accelerating environments, or use real-time clocks, which fail in decelerating environments. We propose the use of bichronal clocks, which are a composition of action clocks and real-time clocks. Our solution can be readily adopted to make existing implementations of diamP robust to process celeration, which can result from hardware upgrades, server overloads, denial-of-service attacks, and other system volatilities.

Dining philosophers is a classic scheduling problem for local mutual exclusion on arbitrary conflict graphs. We establish necessary conditions to solve wait-free dining under eventual weak exclusion in message-passing systems with crash faults. Wait-free dining ensures that every correct hungry process eventually eats. Eventual weak exclusion permits finitely many scheduling mistakes, but eventually no live neighbors eat simultaneously; this exclusion criterion models scenarios where scheduling mistakes are recoverable or only affect performance. Previous work showed that the eventually perfect failure detector (◊P) is sufficient to solve wait-free dining under eventual weak exclusion; we prove that ◊P is also necessary, and thus ◊P is the weakest oracle to solve this problem. Our reduction also establishes that any such dining solution can be made eventually fair. Finally, the reduction itself may be of more general interest; when applied to wait-free perpetual weak exclusion, our reduction produces an alternative proof that the more powerful trusting oracle (T) is necessary (but not sufficient) to solve the problem of Fault-Tolerant Mutual Exclusion (FTME).

The use of anatomical information to improve the quality of reconstructed images in positron emission tomography (PET) has been extensively studied. A common strategy has been to include spatial smoothing within boundaries defined from the anatomical data. We present an alternative method for the incorporation of anatomical information into PET image reconstruction, in which we use segmented magnetic resonance (MR) images to assign tissue composition to PET image pixels. We model the image as a sum of activities for each tissue type, weighted by the assigned tissue composition. The reconstruction is performed as a maximum a posteriori (MAP) estimation of the activities of each tissue type. Two prior functions, defined for tissue-type activities, are considered. The algorithm is tested in realistic simulations employing a full physical model of the PET scanner.