A Note on "Assessing Generalization of SGD via Disagreement"

Several recent works find empirically that the average test error of deep neural networks can be estimated via the prediction disagreement of models, which does not require labels. In particular, Jiang et al. (2022) show for the disagreement between two separately trained networks that this `Generalization Disagreement Equality' follows from the well-calibrated nature of deep ensembles under the notion of a proposed `class-aggregated calibration.' In this reproduction, we show that the suggested theory might be impractical because a deep ensemble's calibration can deteriorate as prediction disagreement increases, which is precisely when the coupling of test error and disagreement is of interest, while labels are needed to estimate the calibration on new datasets. Further, we simplify the theoretical statements and proofs, showing them to be straightforward within a probabilistic context, unlike the original hypothesis space view employed by Jiang et al. (2022)

Integrated Java Bytecode Verification

AbstractExisting Java verifiers perform an iterative data-flow analysis to discover the unambiguous type of values stored on the stack or in registers. Our novel verification algorithm uses abstract interpretation to obtain definition/use information for each register and stack location in the program, which in turn is used to transform the program into Static Single Assignment form. In SSA, verification is reduced to simple type compatibility checking between the definition type of each SSA variable and the type of each of its uses. Inter-adjacent transitions of a value through stack and registers are no longer verified explicitly. This integrated approach is more efficient than traditional bytecode verification but still as safe as strict verification, as overall program correctness can be induced once the data flow from each definition to all associated uses is known to be type-safe

Untyped Memory in the Java Virtual Machine

We have implemented a virtual execution environment that executes legacy binary code on top of the type-safe Java Virtual Machine by recompiling native code instructions to type-safe bytecode. As it is essentially impossible to infer static typing into untyped machine code, our system emulates untyped memory on top of Java’s type system. While this approach allows to execute native code on any off-the-shelf JVM, the resulting runtime performance is poor. We propose a set of virtual machine extensions that add type-unsafe memory objects to JVM. We contend that these JVM extensions do not relax Java’s type system as the same functionality can be achieved in pure Java, albeit much less efficiently

Organization of the canine gene encoding the E isoform of retinal guanylate cyclase (cGC-E) and exclusion of its involvement in the inherited retinal dystrophy of the Swedish Briard and Briard–Beagle dogs

AbstractIntracellular cyclic GMP concentration is known to change in response to a wide variety of agents, including hormones, neurotransmitters or light. In vertebrate photoreceptors, different membrane-bound guanylate cyclase isoforms are responsible for cGMP synthesis and thus directly involved in termination of light signalling via the phototransduction cascade and recovery of the dark state. We have characterized a 4.7 kb long cDNA for the canine retinal guanylate cyclase isoform E (cGC-E) predicting a polypeptide of 1109 amino acids. The genomic structure and the complete sequence of the canine GC-E gene, which consists of 20 exons and spans about 14.5 kb, has also been determined. Northern blot analysis showed that GC-E was expressed in the canine retina as a 4.7 and 6.1 kb large transcript. RT-PCR analysis also detected low expression in cerebrum (occipital lobe). We performed a sequence analysis of the cGC-E gene in animals of a Swedish Briard and Briard–Beagle dog kinship in which an inherited retinal dystrophy is segregating. Several intragenic DNA polymorphisms were identified and used for segregation analysis which excluded cGC-E as a candidate gene for this type of canine retinal dystrophy

Properties of the Tangle for Uniform Random and Random Walk Tip Selection

The growing number of applications for distributed ledger technologies is driving both industry and academia to solve the limitations of blockchain, particularly its scalability issues. Recent distributed ledger technologies have replaced the blockchain linear structure with a more flexible directed acyclic graph in an attempt to accommodate a higher throughput. Despite the fast-growing diffusion of directed acyclic graph based distributed ledger technologies, researchers lack a basic understanding of their behavior. In this paper we analyze the Tangle, a directed acyclic graph that is used (with certain modifications) in various protocols such as IOTA, Byteball, Avalanche or SPECTRE. Our contribution is threefold. First, we run simulations in a continuous-time model to examine tip count stability and cumulative weight evolution while varying the rate of incoming transactions. In particular we confirm analytical predictions on the number of tips with uniform random tip selection strategy. Second, we show how different tip selection algorithms affect the growth of the Tangle. Moreover, we explain these differences by analyzing the spread of exit probabilities of random walks. Our findings confirm analytically derived predictions and provide novel insights on the different phases of growth of cumulative weight as well as on the average time difference for a transaction to receive its first approval when using distinct tip selection algorithms. Lastly, we analyze simulation overhead and performance as a function of Tangle size and compare results for different tip selection algorithms.Comment: Published in: 2019 IEEE International Conference on Blockchain (Blockchain