2,773 research outputs found
Tiresias: Online Anomaly Detection for Hierarchical Operational Network Data
Operational network data, management data such as customer care call logs and
equipment system logs, is a very important source of information for network
operators to detect problems in their networks. Unfortunately, there is lack of
efficient tools to automatically track and detect anomalous events on
operational data, causing ISP operators to rely on manual inspection of this
data. While anomaly detection has been widely studied in the context of network
data, operational data presents several new challenges, including the
volatility and sparseness of data, and the need to perform fast detection
(complicating application of schemes that require offline processing or
large/stable data sets to converge).
To address these challenges, we propose Tiresias, an automated approach to
locating anomalous events on hierarchical operational data. Tiresias leverages
the hierarchical structure of operational data to identify high-impact
aggregates (e.g., locations in the network, failure modes) likely to be
associated with anomalous events. To accommodate different kinds of operational
network data, Tiresias consists of an online detection algorithm with low time
and space complexity, while preserving high detection accuracy. We present
results from two case studies using operational data collected at a large
commercial IP network operated by a Tier-1 ISP: customer care call logs and
set-top box crash logs. By comparing with a reference set verified by the ISP's
operational group, we validate that Tiresias can achieve >94% accuracy in
locating anomalies. Tiresias also discovered several previously unknown
anomalies in the ISP's customer care cases, demonstrating its effectiveness
Facile hydrothermal synthesis and optical limiting properties of TiO 2 -reduced graphene oxide nanocomposites
TiO2/reduced graphene oxide (RGO) nanocomposites Gx (RGO titania nanocomposite, x grams tetrabutyl titanate per 0.03 g RGO, x = 0.25, 0.50, 1.00) were prepared by a hydrothermal method: graphene oxide was reduced to RGO in a 2:1 water:ethanol mixture in the presence of varying quantities of tetrabutyl titanate, which deposited as TiO2 on the RGO sheets. The nanocomposites were characterized by a combination of Fourier transform infrared spectroscopy, diffuse reflectance ultraviolet-visible spectroscopy, photoluminescence spectroscopy, Raman spectroscopy, X-ray powder diffraction, X-ray photoelectron spectroscopy and transmission electron microscopy studies. The nanocomposite G0.25 exhibits enhanced nonlinear optical properties compared to its individual components, which is ascribed to a combination of mechanisms. The role of defects and electron/energy transfer in the optical limiting performance of G0.25 was clarified with the help of Raman and photoluminescence spectroscopies. Intensity-dependent switching between reverse saturable absorption and saturable absorption behavior was observed with the G0.50 nanocomposite
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Quadruple bonding between iron and boron in the BFe(CO)3- complex.
While main group elements have four valence orbitals accessible for bonding, quadruple bonding to main group elements is extremely rare. Here we report that main group element boron is able to form quadruple bonding interactions with iron in the BFe(CO)3- anion complex, which has been revealed by quantum chemical investigation and identified by mass-selected infrared photodissociation spectroscopy in the gas phase. The complex is characterized to have a B-Fe(CO)3- structure of C3v symmetry and features a B-Fe bond distance that is much shorter than that expected for a triple bond. Various chemical bonding analyses indicate that the complex involves unprecedented Bā£Fe quadruple bonding interactions. Besides the common one electron-sharing Ļ bond and two FeāB dative Ļ bonds, there is an additional weak BāFe dative Ļ bonding interaction. This finding of the new quadruple bonding indicates that there might exist a wide range of boron-metal complexes that contain such high multiplicity of chemical bonds
Convection enhanced delivery of light responsive antigen capturing oxygen generators for chemo-phototherapy triggered adaptive immunity
Acknowledgments Chi-Hwa Wang is supported by the National Additive Manufacturing Innovation Cluster @ the National University of Singapore. Vishnu Sunil and Teoh Jia Heng greatly appreciate the National University of Singapore Research Scholarship for the funding of their Ph.D. studies at the National University of Singapore.Peer reviewedPostprin
Transfer Attacks and Defenses for Large Language Models on Coding Tasks
Modern large language models (LLMs), such as ChatGPT, have demonstrated
impressive capabilities for coding tasks including writing and reasoning about
code. They improve upon previous neural network models of code, such as
code2seq or seq2seq, that already demonstrated competitive results when
performing tasks such as code summarization and identifying code
vulnerabilities. However, these previous code models were shown vulnerable to
adversarial examples, i.e. small syntactic perturbations that do not change the
program's semantics, such as the inclusion of "dead code" through false
conditions or the addition of inconsequential print statements, designed to
"fool" the models. LLMs can also be vulnerable to the same adversarial
perturbations but a detailed study on this concern has been lacking so far. In
this paper we aim to investigate the effect of adversarial perturbations on
coding tasks with LLMs. In particular, we study the transferability of
adversarial examples, generated through white-box attacks on smaller code
models, to LLMs. Furthermore, to make the LLMs more robust against such
adversaries without incurring the cost of retraining, we propose prompt-based
defenses that involve modifying the prompt to include additional information
such as examples of adversarially perturbed code and explicit instructions for
reversing adversarial perturbations. Our experiments show that adversarial
examples obtained with a smaller code model are indeed transferable, weakening
the LLMs' performance. The proposed defenses show promise in improving the
model's resilience, paving the way to more robust defensive solutions for LLMs
in code-related applications
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