Deductive Optimization of Relational Data Storage

Optimizing the physical data storage and retrieval of data are two key database management problems. In this paper, we propose a language that can express a wide range of physical database layouts, going well beyond the row- and column-based methods that are widely used in database management systems. We use deductive synthesis to turn a high-level relational representation of a database query into a highly optimized low-level implementation which operates on a specialized layout of the dataset. We build a compiler for this language and conduct experiments using a popular database benchmark, which shows that the performance of these specialized queries is competitive with a state-of-the-art in memory compiled database system

Partial replay of long-running applications

Bugs in deployed software can be extremely difficult to track down. Invasive logging techniques, such as logging all non-deterministic inputs, can incur substantial runtime overheads. This paper shows how symbolic analysis can be used to re-create path equivalent executions for very long running programs such as databases and web servers. The goal is to help developers debug such long-running programs by allowing them to walk through an execution of the last few requests or transactions leading up to an error. The challenge is to provide this functionality without the high runtime overheads associated with traditional replay techniques based on input logging or memory snapshots. Our approach achieves this by recording a small amount of information about program execution, such as the direction of branches taken, and then using symbolic analysis to reconstruct the execution of the last few inputs processed by the application, as well as the state of memory before these inputs were executed. We implemented our technique in a new tool called bbr. In this paper, we show that it can be used to replay bugs in long-running single-threaded programs starting from the middle of an execution. We show that bbr incurs low recording overhead (avg. of 10%) during program execution, which is much less than existing replay schemes. We also show that it can reproduce real bugs from web servers, database systems, and other common utilities

Geographical interdependence, international trade and economic dynamics: the Chinese and German solar energy industries

The trajectories of the German and Chinese photovoltaic industries differ significantly yet are strongly interdependent. Germany has seen a rapid growth in market demand and a strong increase in production, especially in the less developed eastern half of the country. Chinese growth has been export driven. These contrasting trajectories reflect the roles of market creation, investment and credit and the drivers of innovation and competitiveness. Consequent differences in competiveness have generated major trade disputes

Program synthesis from polymorphic refinement types

We present a method for synthesizing recursive functions that provably satisfy a given specification in the form of a polymorphic refinement type. We observe that such specifications are particularly suitable for program synthesis for two reasons. First, they offer a unique combination of expressive power and decidability, which enables automatic verification—and hence synthesis—of nontrivial programs. Second, a type-based specification for a program can often be effectively decomposed into independent specifications for its components, causing the synthesizer to consider fewer component combinations and leading to a combinatorial reduction in the size of the search space. At the core of our synthesis procedure is a newalgorithm for refinement type checking, which supports specification decomposition. We have evaluated our prototype implementation on a large set of synthesis problems and found that it exceeds the state of the art in terms of both scalability and usability. The tool was able to synthesize more complex programs than those reported in prior work (several sorting algorithms and operations on balanced search trees), as well as most of the benchmarks tackled by existing synthesizers, often starting from a more concise and intuitive user input.National Science Foundation (U.S.) (Grant CCF-1438969)National Science Foundation (U.S.) (Grant CCF-1139056)United States. Defense Advanced Research Projects Agency (Grant FA8750-14-2-0242

SAT-Based Synthesis Methods for Safety Specs

Automatic synthesis of hardware components from declarative specifications is an ambitious endeavor in computer aided design. Existing synthesis algorithms are often implemented with Binary Decision Diagrams (BDDs), inheriting their scalability limitations. Instead of BDDs, we propose several new methods to synthesize finite-state systems from safety specifications using decision procedures for the satisfiability of quantified and unquantified Boolean formulas (SAT-, QBF- and EPR-solvers). The presented approaches are based on computational learning, templates, or reduction to first-order logic. We also present an efficient parallelization, and optimizations to utilize reachability information and incremental solving. Finally, we compare all methods in an extensive case study. Our new methods outperform BDDs and other existing work on some classes of benchmarks, and our parallelization achieves a super-linear speedup. This is an extended version of [5], featuring an additional appendix.Comment: Extended version of a paper at VMCAI'1

Telescoping Solar Array Concept for Achieving High Packaging Efficiency

Lightweight, high-efficiency solar arrays are required for future deep space missions using high-power Solar Electric Propulsion (SEP). Structural performance metrics for state-of-the art 30-50 kW flexible blanket arrays recently demonstrated in ground tests are approximately 40 kW/cu m packaging efficiency, 150 W/kg specific power, 0.1 Hz deployed stiffness, and 0.2 g deployed strength. Much larger arrays with up to a megawatt or more of power and improved packaging and specific power are of interest to mission planners for minimizing launch and life cycle costs of Mars exploration. A new concept referred to as the Compact Telescoping Array (CTA) with 60 kW/cu m packaging efficiency at 1 MW of power is described herein. Performance metrics as a function of array size and corresponding power level are derived analytically and validated by finite element analysis. Feasible CTA packaging and deployment approaches are also described. The CTA was developed, in part, to serve as a NASA reference solar array concept against which other proposed designs of 50-1000 kW arrays for future high-power SEP missions could be compared

Evolution of central pattern generators for the control of a five-link bipedal walking mechanism

Central pattern generators (CPGs), with a basis is neurophysiological studies, are a type of neural network for the generation of rhythmic motion. While CPGs are being increasingly used in robot control, most applications are hand-tuned for a specific task and it is acknowledged in the field that generic methods and design principles for creating individual networks for a given task are lacking. This study presents an approach where the connectivity and oscillatory parameters of a CPG network are determined by an evolutionary algorithm with fitness evaluations in a realistic simulation with accurate physics. We apply this technique to a five-link planar walking mechanism to demonstrate its feasibility and performance. In addition, to see whether results from simulation can be acceptably transferred to real robot hardware, the best evolved CPG network is also tested on a real mechanism. Our results also confirm that the biologically inspired CPG model is well suited for legged locomotion, since a diverse manifestation of networks have been observed to succeed in fitness simulations during evolution.Comment: 11 pages, 9 figures; substantial revision of content, organization, and quantitative result

Syntax-guided synthesis

The classical formulation of the program-synthesis problem is to find a program that meets a correctness specification given as a logical formula. Recent work on program synthesis and program optimization illustrates many potential benefits of allowing the user to supplement the logical specification with a syntactic template that constrains the space of allowed implementations. Our goal is to identify the core computational problem common to these proposals in a logical framework. The input to the syntax-guided synthesis problem (SyGuS) consists of a background theory, a semantic correctness specification for the desired program given by a logical formula, and a syntactic set of candidate implementations given by a grammar. The computational problem then is to find an implementation from the set of candidate expressions so that it satisfies the specification in the given theory. We describe three different instantiations of the counter-example-guided-inductive-synthesis (CEGIS) strategy for solving the synthesis problem, report on prototype implementations, and present experimental results on an initial set of benchmarks.National Science Foundation (U.S.) (Expeditions in Computing Project ExCAPE Award CCF 1138996

Learning Moore Machines from Input-Output Traces

The problem of learning automata from example traces (but no equivalence or membership queries) is fundamental in automata learning theory and practice. In this paper we study this problem for finite state machines with inputs and outputs, and in particular for Moore machines. We develop three algorithms for solving this problem: (1) the PTAP algorithm, which transforms a set of input-output traces into an incomplete Moore machine and then completes the machine with self-loops; (2) the PRPNI algorithm, which uses the well-known RPNI algorithm for automata learning to learn a product of automata encoding a Moore machine; and (3) the MooreMI algorithm, which directly learns a Moore machine using PTAP extended with state merging. We prove that MooreMI has the fundamental identification in the limit property. We also compare the algorithms experimentally in terms of the size of the learned machine and several notions of accuracy, introduced in this paper. Finally, we compare with OSTIA, an algorithm that learns a more general class of transducers, and find that OSTIA generally does not learn a Moore machine, even when fed with a characteristic sample

Randomized trial of daily high-dose vitamin D3 in patients with RRMS receiving subcutaneous interferon β-1a

OBJECTIVE: In the phase II, randomized, double-blind, placebo-controlled Supplementation of Vigantol Oil versus Placebo Add-on in Patients with Relapsing-Remitting Multiple Sclerosis (RRMS) Receiving Rebif Treatment (SOLAR) study (NCT01285401), we assessed the efficacy and safety of add-on vitamin D3 in patients with RRMS. METHODS: Eligible patients with RRMS treated with SC interferon-β-1a (IFN-β-1a) 44 μg 3 times weekly and serum 25(OH)D levels <150 nmol/L were included. From February 15, 2011, to May 11, 2015, 229 patients were included and randomized 1:1 to receive SC IFN-β-1a plus placebo (n = 116) or SC IFN-β-1a plus oral high-dose vitamin D3 14,007 IU/d (n = 113). The revised primary outcome was the proportion of patients with no evidence of disease activity (NEDA-3) at week 48. RESULTS: At 48 weeks, 36.3% of patients who received high-dose vitamin D3 had NEDA-3, without a statistically significant difference in NEDA-3 status between groups (placebo 35.3%; odds ratio 0.93; 95% confidence interval [CI] 0.53-1.63; p = 0.80). Compared with placebo, the high-dose vitamin D3 group had better MRI outcomes for combined unique active lesions (incidence rate ratio 0.68; 95% CI 0.52-0.89; p = 0.0045) and change from baseline in total volume of T2 lesions (difference in mean ranks: -0.074; p = 0.035). CONCLUSIONS: SOLAR did not establish a benefit for high-dose vitamin D3 as add-on to IFN-β-1a, based on the primary outcome of NEDA-3, but findings from exploratory outcomes suggest protective effects on development of new MRI lesions in patients with RRMS. CLINICALTRIALSGOV IDENTIFIER: NCT01285401. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that for patients with RRMS treated with SC IFN-β-1a, 48 weeks of cholecalciferol supplementation did not promote NEDA-3 status