207 research outputs found
AutoML from Software Engineering Perspective: Landscapes and Challenges
Machine learning (ML) has been widely adopted in modern software, but the manual configuration of ML (e.g., hyper-parameter configuration) poses a significant challenge to software developers. Therefore, automated ML (AutoML), which seeks the optimal configuration of ML automatically, has received increasing attention from the software engineering community. However, to date, there is no comprehensive understanding of how AutoML is used by developers and what challenges developers encounter in using AutoML for software development. To fill this knowledge gap, we conduct the first study on understanding the use and challenges of AutoML from software developers’ perspective. We collect and analyze 1,554 AutoML downstream repositories, 769 AutoML-related Stack Overflow questions, and 1,437 relevant GitHub issues. The results suggest the increasing popularity of AutoML in a wide range of topics, but also the lack of relevant expertise. We manually identify specific challenges faced by developers for AutoML-enabled software. Based on the results, we derive a series of implications for AutoML framework selection, framework development, and research
Regulating nanoscale heat transfer with Janus nanoparticles
Janus nanoparticles (JNPs) with heterogeneous compositions or interfacial
properties can exhibit directional heating upon external excitation, such as
laser radiation and magnetic field. This directional heating may be harnessed
for new nanotechnology and biomedical applications. Understanding thermal
transport and temperature control with JNP heating is critical for these
advances. Here, we developed a numerical framework to analyze the asymmetric
thermal transport in JNP heating under photothermal stimulation. We found that
JNP-induced temperature contrast, defined as the ratio of temperature increase
in the surrounding water, shows a substantial size and polar angle dependence.
Notably, we discovered a significant enhancement of the temperature contrast
under pulsed heating due to thermal confinement, compared with the continuous
heating. This work brings new insights into the thermal responses of JNP
heating and advances the field.Comment: 5 figures in the main text, and 9 figures in the supporting
informatio
Bilayer Kagome Borophene with Multiple van Hove Singularities
The appearance of van Hove singularities near the Fermi level leads to
prominent phenomena, including superconductivity, charge density wave, and
ferromagnetism. Here a bilayer Kagome lattice with multiple van Hove
singularities is designed and a novel borophene with such lattice
(BK-borophene) is proposed by the first-principles calculations. BK-borophene,
which is formed via three-center two-electron (3c-2e) sigma-type bonds, is
predicted to be energetically, dynamically, thermodynamically, and mechanically
stable. The electronic structure hosts both conventional and high-order van
Hove singularities in one band. The conventional van Hove singularity resulting
from the horse saddle is 0.065 eV lower than the Fermi level, while the
high-order one resulting from the monkey saddle is 0.385 eV below the Fermi
level. Both the singularities lead to the divergence of electronic density of
states. Besides, the high-order singularity is just intersected to a Dirac-like
cone, where the Fermi velocity can reach 1340000 m/s. The interaction between
the two Kagome lattices is critical for the appearance of high-order van Hove
singularities. The novel bilayer Kagome borophene with rich and intriguing
electronic structure offers an unprecedented platform for studying correlation
phenomena in quantum material systems and beyond
Rise of the Planet of Serverless Computing: A Systematic Review
Serverless computing is an emerging cloud computing paradigm, being adopted to develop a wide range of software applications.
It allows developers to focus on the application logic in the granularity of function, thereby freeing developers from tedious and
error-prone infrastructure management. Meanwhile, its unique characteristic poses new challenges to the development and deployment
of serverless-based applications. To tackle these challenges, enormous research efforts have been devoted. This paper provides a
comprehensive literature review to characterize the current research state of serverless computing. Specifically, this paper covers 164
papers on 17 research directions of serverless computing, including performance optimization, programming framework, application
migration, multi-cloud development, testing and debugging, etc. It also derives research trends, focus, and commonly-used platforms
for serverless computing, as well as promising research opportunities
MAAT: A Novel Ensemble Approach to Addressing Fairness and Performance Bugs for Machine Learning Software
Machine Learning (ML) software can lead to unfair and unethical decisions, making software fairness bugs an increasingly significant concern for software engineers. However, addressing fairness bugs often comes at the cost of introducing more ML performance (e.g., accuracy) bugs. In this paper, we propose MAAT, a novel ensemble approach to improving fairness-performance trade-off for ML software. Conventional ensemble methods combine different models with identical learning objectives. MAAT, instead, combines models optimized for different objectives: fairness and ML performance. We conduct an extensive evaluation of MAAT with 5 state-of-the-art methods, 9 software decision tasks, and 15 fairness-performance measurements. The results show that MAAT significantly outperforms the state-of-the-art. In particular, MAAT beats the trade-off baseline constructed by a recent benchmarking tool in 92.2% of the overall cases evaluated, 12.2 percentage points more than the best technique currently available. Moreover, the superiority of MAAT over the state-of-the-art holds on all the tasks and measurements that we study. We have made publicly available the code and data of this work to allow for future replication and extension
Adonis: Practical and Efficient Control Flow Recovery through OS-Level Traces
Control flow recovery is critical to promise the software quality, especially for large-scale software in production environment.
However, the efficiency of most current control flow recovery techniques is compromised due to their runtime overheads along with
deployment and development costs. To tackle this problem, we propose a novel solution, Adonis, which harnesses OS-level traces,
such as dynamic library calls and system call traces, to efficiently and safely recover control flows in practice. Adonis operates in
two steps: it first identifies the call-sites of trace entries, then it executes a pair-wise symbolic execution to recover valid execution
paths. This technique has several advantages. First, Adonis does not require the insertion of any probes into existing applications,
thereby minimizing runtime cost. Second, given that OS-level traces are hardware-independent, Adonis can be implemented across
various hardware configurations without the need for hardware-specific engineering efforts, thus reducing deployment cost. Third, as
Adonis is fully automated and does not depend on manually created logs, it circumvents additional development cost. We conducted an
evaluation of Adonis on representative desktop applications and real-world IoT applications. Adonis can faithfully recover the control
flow with 86.8% recall and 81.7% precision. Compared to the state-of-the-art log-based approach, Adonis can not only cover all the
execution paths recovered, but also recover 74.9% of statements that cannot be covered. In addition, the runtime cost of Adonis is
18.3× lower than the instrument-based approach; the analysis time and storage cost (indicative of the deployment cost) of Adonis is
50× smaller and 443× smaller than the hardware-based approach, respectively. To facilitate future replication and extension of this
work, we have made the code and data publicly available
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