Perceptions of randomness in binary sequences: Normative, heuristic, or both?

When people consider a series of random binary events, such as tossing an unbiased coin and recording the sequence of heads (H) and tails (T), they tend to erroneously rate sequences with less internal structure or order (such as HTTHT) as more probable than sequences containing more structure or order (such as HHHHH). This is traditionally explained as a local representativeness effect: Participants assume that the properties of long sequences of random outcomes—such as an equal proportion of heads and tails, and little internal structure—should also apply to short sequences. However, recent theoretical work has noted that the probability of a particular sequence of say, heads and tails of length n, occurring within a larger (>n) sequence of coin flips actually differs by sequence, so P(HHHHH) < P(HTTHT). In this alternative account, people apply rational norms based on limited experience. We test these accounts. Participants in Experiment 1 rated the likelihood of occurrence for all possible strings of 4, 5, and 6 observations in a sequence of coin flips. Judgments were better explained by representativeness in alternation rate, relative proportion of heads and tails, and sequence complexity, than by objective probabilities. Experiments 2 and 3 gave similar results using incentivized binary choice procedures. Overall the evidence suggests that participants are not sensitive to variation in objective probabilities of a sub-sequence occurring; they appear to use heuristics based on several distinct forms of representativeness

The Parallel Persistent Memory Model

We consider a parallel computational model that consists of $P$ processors, each with a fast local ephemeral memory of limited size, and sharing a large persistent memory. The model allows for each processor to fault with bounded probability, and possibly restart. On faulting all processor state and local ephemeral memory are lost, but the persistent memory remains. This model is motivated by upcoming non-volatile memories that are as fast as existing random access memory, are accessible at the granularity of cache lines, and have the capability of surviving power outages. It is further motivated by the observation that in large parallel systems, failure of processors and their caches is not unusual. Within the model we develop a framework for developing locality efficient parallel algorithms that are resilient to failures. There are several challenges, including the need to recover from failures, the desire to do this in an asynchronous setting (i.e., not blocking other processors when one fails), and the need for synchronization primitives that are robust to failures. We describe approaches to solve these challenges based on breaking computations into what we call capsules, which have certain properties, and developing a work-stealing scheduler that functions properly within the context of failures. The scheduler guarantees a time bound of $O(W/P_A + D(P/P_A) \lceil\log_{1/f} W\rceil)$ in expectation, where $W$ and $D$ are the work and depth of the computation (in the absence of failures), $P_A$ is the average number of processors available during the computation, and $f \le 1/2$ is the probability that a capsule fails. Within the model and using the proposed methods, we develop efficient algorithms for parallel sorting and other primitives.Comment: This paper is the full version of a paper at SPAA 2018 with the same nam

State of Academic Knowledge on Toxicity and Biological Fate of Quantum Dots

Quantum dots (QDs), an important class of emerging nanomaterial, are widely anticipated to find application in many consumer and clinical products in the near future. Premarket regulatory scrutiny is, thus, an issue gaining considerable attention. Previous review papers have focused primarily on the toxicity of QDs. From the point of view of product regulation, however, parameters that determine exposure (e.g., dosage, transformation, transportation, and persistence) are just as important as inherent toxicity. We have structured our review paper according to regulatory risk assessment practices, in order to improve the utility of existing knowledge in a regulatory context. Herein, we summarize the state of academic knowledge on QDs pertaining not only to toxicity, but also their physicochemical properties, and their biological and environmental fate. We conclude this review with recommendations on how to tailor future research efforts to address the specific needs of regulators

HetFS: A heterogeneous file system for everyone

Storage devices have been getting more and more diverse during the last decade. The advent of SSDs made it painfully clear that rotating devices, such as HDDs or magnetic tapes, were lacking in regards to response time. However, SSDs currently have a limited number of write cycles and a significantly larger price per capacity, which has prevented rotational technologies from begin abandoned. Additionally, Non-Volatile Memories (NVMs) have been lately gaining traction, offering devices that typically outperform NAND-based SSDs but exhibit a full new set of idiosyncrasies. Therefore, in order to appropriately support this diversity, intelligent mechanisms will be needed in the near-future to balance the benefits and drawbacks of each storage technology available to a system. In this paper, we present a first step towards such a mechanism called HetFS, an extension to the ZFS file system that is capable of choosing the storage device a file should be kept in according to preprogrammed filters. We introduce the prototype and show some preliminary results of the effects obtained when placing specific files into different devices.The research leading to these results has received funding from the European Community under the BIGStorage ETN (Project 642963 of the H2020-MSCA-ITN-2014), by the Spanish Ministry of Economy and Competitiveness under the TIN2015-65316 grant and by the Catalan Government under the 2014-SGR- 1051 grant. To learn more about the BigStorage project, please visit http: //bigstorage-project.eu/.Peer ReviewedPostprint (author's final draft

A unified model for holistic power usage in cloud datacenter servers

Cloud datacenters are compute facilities formed by hundreds and thousands of heterogeneous servers requiring significant power requirements to operate effectively. Servers are composed by multiple interacting sub-systems including applications, microelectronic processors, and cooling which reflect their respective power profiles via different parameters. What is presently unknown is how to accurately model the holistic power usage of the entire server when including all these sub-systems together. This becomes increasingly challenging when considering diverse utilization patterns, server hardware characteristics, air and liquid cooling techniques, and importantly quantifying the non-electrical energy cost imposed by cooling operation. Such a challenge arises due to the need for multi-disciplinary expertise required to study server operation holistically. This work provides a unified model for capturing holistic power usage within Cloud datacenter servers. Constructed through controlled laboratory experiments, the model captures the relationship of server power usage between software, hardware, and cooling agnostic of architecture and cooling type (air and liquid). An exciting prospect is the ability to quantify the amount of non-electrical power consumed through cooling, allowing for more realistic and accurate server power profiles. This work represents the first empirically supported analysis and modeling of holistic power usage for Cloud datacenter servers, and bridges a significant gap between computer science and mechanical engineering research. Model validation through experiments demonstrates an average standard error of 3% for server power usage within both air and liquid cooled environments