Information System Development Team Collaboration Antecedents

Despite information system development companies have invested substantial resources to support the success of information system development (ISD) projects, the failure rate is still high. Extant studies indicated that the constant changes from socio-technical environments are the main causes of the low success rate. This study argues that team collaboration is a key factor to effectively cope with unexpected disruptions that would have negative effect on overall software product success. This study proposes a research model exploring factors that influence the development of team collaboration. These factors include the team commitment, transactive memory systems (TMS), and collective mind. In addition, the study suggests that the collective mind has an intermediate effect on the team commitment, TMS, and team collaboration. This study takes the information development teams of various companies in Taiwan as its subjects

C-Cosine Functions and the Abstract Cauchy Problem, I

AbstractIfAis the generator of an exponentially boundedC-cosine function on a Banach spaceX, then the abstract Cauchy problem (ACP) forAhas a unique solution for every pair (x,y) of initial values from (λ−A)−1C(X). The main result is a characterization of the generator of aC-cosine function, which may not be exponentially bounded and may have a nondensely defined generator, in terms of the associated ACP

How Do People Process Ambiguous Strings

This article combines ambiguity phenomenon with Chinese word segmentation to observe how human being conduct language processing to clarify ambiguity between overlapping ambiguity and combination ambiguity. Artificial intelligence will easily missegment these two strings, while the study tries to introduce optimality theory to discover possible base of these two types of ambiguity comprehended by general people. According to the result, the key to clarify ambiguity is context, idiomaticity and word frequency

Network partitioning into tree hierarchies

This paper addresses the problem of partitioning a circuit into a tree hierarchy with an objective of minimizing a glo-bal interconnection cost. An efficient and effective algo-rithm is necessary when the circuit is huge and the tree has many levels of hierarchy. We propose a heuristic algorithm for improving a partition with respect to a given tree struc-ture. The algorithm utilizes the tree hierarchy as an efficient mechanism for iterative improvement. We also extend the tree hierarchy to apply a multi-phase partitioning approach. Experimental results show that the algorithm significantly improves the initial partitions produced by multiway parti-tioning and by recursive partitioning. 1

Qubit Mapping Toward Quantum Advantage

Qubit Mapping is a pivotal stage in quantum compilation flow. Its goal is to convert logical circuits into physical circuits so that a quantum algorithm can be executed on real-world non-fully connected quantum devices. Qubit Mapping techniques nowadays still lack the key to quantum advantage, scalability. Several studies have proved that at least thousands of logical qubits are required to achieve quantum computational advantage. However, to our best knowledge, there is no previous research with the ability to solve the qubit mapping problem with the necessary number of qubits for quantum advantage in a reasonable time. In this work, we provide the first qubit mapping framework with the scalability to achieve quantum advantage while accomplishing a fairly good performance. The framework also boasts its flexibility for quantum circuits of different characteristics. Experimental results show that the proposed mapping method outperforms the state-of-the-art methods on quantum circuit benchmarks by improving over 5% of the cost complexity in one-tenth of the program running time. Moreover, we demonstrate the scalability of our method by accomplishing mapping of an 11,969-qubit Quantum Fourier Transform within five hours

Decoding Information from noisy, redundant, and intentionally-distorted sources

Advances in information technology reduce barriers to information propagation, but at the same time they also induce the information overload problem. For the making of various decisions, mere digestion of the relevant information has become a daunting task due to the massive amount of information available. This information, such as that generated by evaluation systems developed by various web sites, is in general useful but may be noisy and may also contain biased entries. In this study, we establish a framework to systematically tackle the challenging problem of information decoding in the presence of massive and redundant data. When applied to a voting system, our method simultaneously ranks the raters and the ratees using only the evaluation data, consisting of an array of scores each of which represents the rating of a ratee by a rater. Not only is our appraoch effective in decoding information, it is also shown to be robust against various hypothetical types of noise as well as intentional abuses.Comment: 19 pages, 5 figures, accepted for publication in Physica