Socially Optimal Mining Pools

Mining for Bitcoins is a high-risk high-reward activity. Miners, seeking to reduce their variance and earn steadier rewards, collaborate in pooling strategies where they jointly mine for Bitcoins. Whenever some pool participant is successful, the earned rewards are appropriately split among all pool participants. Currently a dozen of different pooling strategies (i.e., methods for distributing the rewards) are in use for Bitcoin mining. We here propose a formal model of utility and social welfare for Bitcoin mining (and analogous mining systems) based on the theory of discounted expected utility, and next study pooling strategies that maximize the social welfare of miners. Our main result shows that one of the pooling strategies actually employed in practice--the so-called geometric pay pool--achieves the optimal steady-state utility for miners when its parameters are set appropriately. Our results apply not only to Bitcoin mining pools, but any other form of pooled mining or crowdsourcing computations where the participants engage in repeated random trials towards a common goal, and where "partial" solutions can be efficiently verified

Envy, Regret, and Social Welfare Loss

Incentive compatibility (IC) is a desirable property for any auction mechanism, including those used in online advertising. However, in real world applications practical constraints and complex environments often result in mechanisms that lack incentive compatibility. Recently, several papers investigated the problem of deploying black-box statistical tests to determine if an auction mechanism is incentive compatible by using the notion of IC-Regret that measures the regret of a truthful bidder. Unfortunately, most of those methods are computationally intensive, since they require the execution of many counterfactual experiments. In this work, we show that similar results can be obtained using the notion of IC-Envy. The advantage of IC-Envy is its efficiency: it can be computed using only the auction's outcome. In particular, we focus on position auctions. For position auctions, we show that for a large class of pricing schemes (which includes e.g. VCG and GSP), IC-Envy ≥ IC-Regret (and IC-Envy = IC-Regret under mild supplementary conditions). Our theoretical results are completed showing that, in the position auction environment, IC-Envy can be used to bound the loss in social welfare due to the advertiser untruthful behavior. Finally, we show experimentally that IC-Envy can be used as a feature to predict IC-Regret in settings not covered by the theoretical results. In particular, using IC-Envy yields better results than training models using only price and value features

LEDGF/p75 is critical but not essential for multiple-round HIV 1 replication

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Kidney growth in normal and diabetic mice is not affected by human insulin-like growth factor binding protein-1 administration

Insulin-like growth factor I (IGF-I) accumulates in the kidney following the onset of diabetes, initiating diabetic renal hypertrophy. Increased renal IGF-I protein content, which is not reflected in messenger RNA (mRNA) levels, suggests that renal IGF-I accumulation is due to sequestration of circulating IGF-I rather than to local synthesis. It has been suggested that IGF-I is trapped in the kidney by IGF binding protein 1 (IGFBP-1). We administered purified human IGFBP-1 (hIGFBP-1) to nondiabetic and diabetic mice as three daily sc injections for 14 days, starting 6 days after induction of streptozotocin diabetes when the animals were overtly diabetic. Markers of early diabetic renal changes (i.e., increased kidney weight, glomerular volume, and albuminuria) coincided with accumulation of renal cortical IGF-I despite decreased mRNA levels in 20-day diabetic mice. Human IGFBP-1 administration had no effect on increased kidney weight or albuminuria in early diabetes, although it abolished renal cortical IGF-I accumulation and glomerular hypertrophy in diabetic mice. Increased IGF-I levels in kidneys of normal mice receiving hIGFBP-1 were not reflected on kidney parameters. IGFBP-1 administration in diabetic mice had only minor effects on diabetic renal changes. Accordingly, these results did not support the hypothesis that IGFBP-1 plays a major role in early renal changes in diabetes

Exploiting Term Hiding to Reduce Run-time Checking Overhead

One of the most attractive features of untyped languages is the flexibility in term creation and manipulation. However, with such power comes the responsibility of ensuring the correctness of these operations. A solution is adding run-time checks to the program via assertions, but this can introduce overheads that are in many cases impractical. While static analysis can greatly reduce such overheads, the gains depend strongly on the quality of the information inferred. Reusable libraries, i.e., library modules that are pre-compiled independently of the client, pose special challenges in this context. We propose a technique which takes advantage of module systems which can hide a selected set of functor symbols to significantly enrich the shape information that can be inferred for reusable libraries, as well as an improved run-time checking approach that leverages the proposed mechanisms to achieve large reductions in overhead, closer to those of static languages, even in the reusable-library context. While the approach is general and system-independent, we present it for concreteness in the context of the Ciao assertion language and combined static/dynamic checking framework. Our method maintains the full expressiveness of the assertion language in this context. In contrast to other approaches it does not introduce the need to switch the language to a (static) type system, which is known to change the semantics in languages like Prolog. We also study the approach experimentally and evaluate the overhead reduction achieved in the run-time checks.Comment: 26 pages, 10 figures, 2 tables; an extension of the paper version accepted to PADL'18 (includes proofs, extra figures and examples omitted due to space reasons