Using Perspective Taking to De-Escalate Commitment to Software Product Launch Decisions

In software product development settings when things go awry and the original plan loses credibility, managers often choose to honor the originally announced product launch schedule anyway, in effect launching a product that may be seriously compromised in terms of both functionality and reliability. In this study, we draw on the perspective of escalation of commitment to investigate adherence to original product launch schedules despite negative feedback. Specifically, we use the notion of perspective taking to propose a de-escalation tactic. Through a laboratory experiment, we found strong support that taking the perspective of individuals that can be negatively influenced by a product launch can indeed effectively promote de-escalation of commitment. Furthermore, we found that the experiences of anticipated guilt mediate the relationship between perspective taking and de-escalation, and this indirect effect is significantly greater when a decision maker’s personal cost associated with de-escalation is high rather than low

Graph-theoretical optimization of fusion-based graph state generation

Graph states are versatile resources for various quantum information processing tasks, including measurement-based quantum computing and quantum repeaters. Although the type-II fusion gate enables all-optical generation of graph states by combining small graph states, its non-deterministic nature hinders the efficient generation of large graph states. In this work, we present a graph-theoretical strategy to effectively optimize fusion-based generation of any given graph state, along with a Python package OptGraphState. Our strategy comprises three stages: simplifying the target graph state, building a fusion network, and determining the order of fusions. Utilizing this proposed method, we evaluate the resource overheads of random graphs and various well-known graphs. Additionally, we investigate the success probability of graph state generation given a restricted number of available resource states. We expect that our strategy and software will assist researchers in developing and assessing experimentally viable schemes that use photonic graph states

Parity-encoding-based quantum computing with Bayesian error tracking

Measurement-based quantum computing (MBQC) in linear optical systems is promising for near-future quantum computing architecture. However, the nondeterministic nature of entangling operations and photon losses hinder the large-scale generation of graph states and introduce logical errors. In this work, we propose a linear optical topological MBQC protocol employing multiphoton qubits based on the parity encoding, which turns out to be highly photon-loss tolerant and resource-efficient even under the effects of nonideal entangling operations that unavoidably corrupt nearby qubits. For the realistic error analysis, we introduce a Bayesian methodology, in conjunction with the stabilizer formalism, to track errors caused by such detrimental effects. We additionally suggest a graph-theoretical optimization scheme for the process of constructing an arbitrary graph state, which greatly reduces its resource overhead. Notably, we show that our protocol is advantageous over several other existing approaches in terms of fault-tolerance, resource overhead, or feasibility of basic elements.Comment: Main text: 15 pages, 10 figures / Supplemental Material: 17 pages, 8 figure

MetaMix: Meta-state Precision Searcher for Mixed-precision Activation Quantization

Mixed-precision quantization of efficient networks often suffer from activation instability encountered in the exploration of bit selections. To address this problem, we propose a novel method called MetaMix which consists of bit selection and weight training phases. The bit selection phase iterates two steps, (1) the mixed-precision-aware weight update, and (2) the bit-search training with the fixed mixed-precision-aware weights, both of which combined reduce activation instability in mixed-precision quantization and contribute to fast and high-quality bit selection. The weight training phase exploits the weights and step sizes trained in the bit selection phase and fine-tunes them thereby offering fast training. Our experiments with efficient and hard-to-quantize networks, i.e., MobileNet v2 and v3, and ResNet-18 on ImageNet show that our proposed method pushes the boundary of mixed-precision quantization, in terms of accuracy vs. operations, by outperforming both mixed- and single-precision SOTA methods

CSGM Designer: a platform for designing cross-species intron-spanning genic markers linked with genome information of legumes.

BackgroundGenetic markers are tools that can facilitate molecular breeding, even in species lacking genomic resources. An important class of genetic markers is those based on orthologous genes, because they can guide hypotheses about conserved gene function, a situation that is well documented for a number of agronomic traits. For under-studied species a key bottleneck in gene-based marker development is the need to develop molecular tools (e.g., oligonucleotide primers) that reliably access genes with orthology to the genomes of well-characterized reference species.ResultsHere we report an efficient platform for the design of cross-species gene-derived markers in legumes. The automated platform, named CSGM Designer (URL: http://tgil.donga.ac.kr/CSGMdesigner), facilitates rapid and systematic design of cross-species genic markers. The underlying database is composed of genome data from five legume species whose genomes are substantially characterized. Use of CSGM is enhanced by graphical displays of query results, which we describe as "circular viewer" and "search-within-results" functions. CSGM provides a virtual PCR representation (eHT-PCR) that predicts the specificity of each primer pair simultaneously in multiple genomes. CSGM Designer output was experimentally validated for the amplification of orthologous genes using 16 genotypes representing 12 crop and model legume species, distributed among the galegoid and phaseoloid clades. Successful cross-species amplification was obtained for 85.3% of PCR primer combinations.ConclusionCSGM Designer spans the divide between well-characterized crop and model legume species and their less well-characterized relatives. The outcome is PCR primers that target highly conserved genes for polymorphism discovery, enabling functional inferences and ultimately facilitating trait-associated molecular breeding

Leukoaraiosis is associated with pneumonia after acute ischemic stroke

Diagnostic criteria for stroke associated pneumonia based on the CDC criteria. (DOCX 25 kb

A Case of Gastritis Associated with Gastric Capillariasis

This report is about the case of gastritis associated with capillariasis. The patient was a 52-yr-old Korean woman who occasionally ate raw fish and chicken. She complained of mild abdominal pain and nausea, but not diarrhea. An endoscopic examination revealed an exudative flat erosive change on the gastric mucosa of the antrum. She was microscopically diagnosed as chronic gastritis with numerous eosinophil infiltrations. The sectioned worms and eggs in mucosa were morphologically regarded as belonging to the genus Capillaria. This is the first case of gastric capillariasis reported in the Republic of Korea

The discovery and characterization of tungsten insertase in tungsten cofactor biosynthesis

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