We are in the dawn of deep learning explosion for smartphones. To bridge the gap between research and practice, we present the first empirical study on 16,500 the most popular Android apps, demystifying how smartphone apps exploit deep learning in the wild. To this end, we build a new static tool that dissects apps and analyzes their deep learning functions. Our study answers threefold questions: what are the early adopter apps of deep learning, what do they use deep learning for, and how do their deep learning models look like. Our study has strong implications for app developers, smartphone vendors, and deep learning R&D. On one hand, our findings paint a promising picture of deep learning for smartphones, showing the prosperity of mobile deep learning frameworks as well as the prosperity of apps building their cores atop deep learning. On the other hand, our findings urge optimizations on deep learning models deployed on smartphones, protection of these models, and validation of research ideas on these models.

Deep learning (DL) becomes increasingly pervasive, being used in a wide range of software applications. These software applications, named as DL based software (in short as DL software), integrate DL models trained using a large data corpus with DL programs written based on DL frameworks such as TensorFlow and Keras. A DL program encodes the network structure of a desirable DL model and the process by which the model is trained using the training data. To help developers of DL software meet the new challenges posed by DL, enormous research e orts in software engineering have been devoted. Existing studies focus on the development of DL software and extensively analyze faults in DL programs. However, the deployment of DL software has not been comprehensively studied. To ll this knowledge gap, this paper presents a comprehensive study on understanding challenges in deploying DL software. We mine and analyze 3,023 relevant posts from Stack Over ow, a popular Q&A website for developers, and show the increasing popularity and high di culty of DL software deployment among developers. We build a taxonomy of speci c challenges encountered by developers in the process of DL software deployment through manual inspection of 769 sampled posts and report a series of actionable implications for researchers, developers, and DL framework vendors. CCS CONCEPTS • Software and its engineering → Software creation and management; • Computing methodologies → Arti cial intelligence; • General and reference → Empirical studies.

Deep learning (DL) is moving its step into a growing number of mobile software applications. These software applications, named as DL based mobile applications (abbreviated as mobile DL apps) integrate DL models trained using large-scale data with DL programs. A DL program encodes the structure of a desirable DL model and the process by which the model is trained using training data. Due to the increasing dependency of current mobile apps on DL, software engineering (SE) for mobile DL apps has become important. However, existing efforts in SE research community mainly focus on the development of DL models and extensively analyze faults in DL programs. In contrast, faults related to the deployment of DL models on mobile devices (named as deployment faults of mobile DL apps) have not been well studied. Since mobile DL apps have been used by billions of end users daily for various purposes including for safety-critical scenarios, characterizing their deployment faults is of enormous importance. To fill in the knowledge gap, this paper presents the first comprehensive study to date on the deployment faults of mobile DL apps. We identify 304 real deployment faults from Stack Overflow and GitHub, two commonly used data sources for studying software faults. Based on the identified faults, we construct a fine-granularity taxonomy consisting of 23 categories regarding to fault symptoms and distill common fix strategies for different fault symptoms. Furthermore, we suggest actionable implications and research avenues that can potentially facilitate the deployment of DL models on mobile devices.

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In the efforts to explore an aptamer-based approach for target sensing and detection with higher sensitivity and specificity, instead of directly labeling aptamer with fluorophores, we proposed a new strategy by attaching a polymerase chain reaction (PCR) template to an oligonucleotide aptamer selected by systematic evolution of ligands by exponential enrichment (SELEX), so that after aptamer target binding, the template moiety serves as the PCR template in real-time quantitative PCR (RT-PCR), and therefore, the binding event can be reported by the following RT-PCR signals. Using the subtractive SELEX method, the oligonucleotide aptamers specific for the Fc fragment of mouse IgG were selected and subjected to coupling with the PCR dsDNA template by using overlap and the asymmetric extension PCR method. The target binding affinity of the PCR template tethered aptamer has been proven by electrophoretic mobility shift assay (EMSA), and further template tethered aptamer mediated real-time quantitative PCR (A-PCR) was conducted to validate the application for such a template tethered aptamer to be a sensitive probe for IgG detection. The results show that the protocols of A-PCR can detect 10-fold serial dilutions of the target, demonstrating a new mechanism to convert aptamer target binding events to amplified RT-PCR signal, and the feasibility of the PCR template tethered aptamer as a facile, specific, and sensitive target probing and detection is established. This new approach also has potential applications in multiple parallel target detection and analysis in a wide range of research fields.

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