90 research outputs found
Distilling Cognitive Backdoor Patterns within an Image
This paper proposes a simple method to distill and detect backdoor patterns
within an image: \emph{Cognitive Distillation} (CD). The idea is to extract the
"minimal essence" from an input image responsible for the model's prediction.
CD optimizes an input mask to extract a small pattern from the input image that
can lead to the same model output (i.e., logits or deep features). The
extracted pattern can help understand the cognitive mechanism of a model on
clean vs. backdoor images and is thus called a \emph{Cognitive Pattern} (CP).
Using CD and the distilled CPs, we uncover an interesting phenomenon of
backdoor attacks: despite the various forms and sizes of trigger patterns used
by different attacks, the CPs of backdoor samples are all surprisingly and
suspiciously small. One thus can leverage the learned mask to detect and remove
backdoor examples from poisoned training datasets. We conduct extensive
experiments to show that CD can robustly detect a wide range of advanced
backdoor attacks. We also show that CD can potentially be applied to help
detect potential biases from face datasets. Code is available at
\url{https://github.com/HanxunH/CognitiveDistillation}.Comment: ICLR202
The influence of knowledge management on adoption intention of electric vehicles: perspective on technological knowledge
Purpose:
Technological innovation is one of the remarkable characteristics of electric vehicles (EVs). This study aims to analyze how consumers' technological knowledge affects their intention to adopt EVs.
Design/methodology/approach:
Original data were collected via a survey of 443 participants in China. An extended technology acceptance model was constructed to identify the factors influencing consumers' intention to adopt EVs and related technological knowledge pathways.
Findings:
The results show that consumer technological knowledge is positively and significantly related to EVs' perceived usefulness, perceived ease of use, perceived fun to use and consumers' intention to adopt EVs. In addition, no direct and significant relationship is found between perceived fun to use and willingness to adopt EVs, from the technical knowledge dimension.
Practical implications:
Imparting consumers with EV technological knowledge and usefulness may be an effective way to enhance their awareness and willingness to use EVs. Moreover, the role of females in the decision to adopt EVs should not be ignored, especially in decisions to purchase a family car.
Originality/value:
Prior studies lack a technological knowledge-based view, and few studies have discussed how to explore the effects of consumer technological knowledge about EVs on their adoption intention. This study fills the research gap
Imbalanced Gradients: A Subtle Cause of Overestimated Adversarial Robustness
Evaluating the robustness of a defense model is a challenging task in
adversarial robustness research. Obfuscated gradients, a type of gradient
masking, have previously been found to exist in many defense methods and cause
a false signal of robustness. In this paper, we identify a more subtle
situation called Imbalanced Gradients that can also cause overestimated
adversarial robustness. The phenomenon of imbalanced gradients occurs when the
gradient of one term of the margin loss dominates and pushes the attack towards
to a suboptimal direction. To exploit imbalanced gradients, we formulate a
Margin Decomposition (MD) attack that decomposes a margin loss into individual
terms and then explores the attackability of these terms separately via a
two-stage process. We also propose a MultiTargeted and an ensemble version of
our MD attack. By investigating 17 defense models proposed since 2018, we find
that 6 models are susceptible to imbalanced gradients and our MD attack can
decrease their robustness evaluated by the best baseline standalone attack by
another 2%. We also provide an in-depth analysis of the likely causes of
imbalanced gradients and effective countermeasures.Comment: 19 pages, 7 figue
Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs
Graph neural network (GNN) has gained increasing popularity in recent years
owing to its capability and flexibility in modeling complex graph structure
data. Among all graph learning methods, hypergraph learning is a technique for
exploring the implicit higher-order correlations when training the embedding
space of the graph. In this paper, we propose a hypergraph learning framework
named LFH that is capable of dynamic hyperedge construction and attentive
embedding update utilizing the heterogeneity attributes of the graph.
Specifically, in our framework, the high-quality features are first generated
by the pairwise fusion strategy that utilizes explicit graph structure
information when generating initial node embedding. Afterwards, a hypergraph is
constructed through the dynamic grouping of implicit hyperedges, followed by
the type-specific hypergraph learning process. To evaluate the effectiveness of
our proposed framework, we conduct comprehensive experiments on several popular
datasets with eleven state-of-the-art models on both node classification and
link prediction tasks, which fall into categories of homogeneous pairwise graph
learning, heterogeneous pairwise graph learning, and hypergraph learning. The
experiment results demonstrate a significant performance gain (average 12.5% in
node classification and 13.3% in link prediction) compared with recent
state-of-the-art methods
Fake Alignment: Are LLMs Really Aligned Well?
The growing awareness of safety concerns in large language models (LLMs) has
sparked considerable interest in the evaluation of safety within current
research endeavors. This study investigates an interesting issue pertaining to
the evaluation of LLMs, namely the substantial discrepancy in performance
between multiple-choice questions and open-ended questions. Inspired by
research on jailbreak attack patterns, we argue this is caused by mismatched
generalization. That is, the LLM does not have a comprehensive understanding of
the complex concept of safety. Instead, it only remembers what to answer for
open-ended safety questions, which makes it unable to solve other forms of
safety tests. We refer to this phenomenon as fake alignment and construct a
comparative benchmark to empirically verify its existence in LLMs. Such fake
alignment renders previous evaluation protocols unreliable. To address this, we
introduce the Fake alIgNment Evaluation (FINE) framework and two novel
metrics--Consistency Score (CS) and Consistent Safety Score (CSS), which
jointly assess two complementary forms of evaluation to quantify fake alignment
and obtain corrected performance estimates. Applying FINE to 14 widely-used
LLMs reveals several models with purported safety are poorly aligned in
practice. Our work highlights potential limitations in prevailing alignment
methodologies
Thermal cycle stability of Co 64 V 15 Si 17 Al 4 high-temperature shape memory alloy
Abstract(#br)The microstructure, martensitic transformation and thermal cycle stability of Co 64 V 15 Si 17 Al 4 high-temperature shape memory alloy were studied. The results show that the martensite transformation of L2 1 /D0 22 occurred in the Co 64 V 15 Si 17 Al 4 alloy. In the Co 64 V 15 Si 17 Al 4 alloy, the transformation temperatures of forward transformation and reverse transformation are pretty high, reaching 589.6 °C and 649.1 °C, respectively. The temperatures of the martensitic transformation and the transformation heat show a neglectable difference after 200 thermal cycles in the alloy. This alloy exhibits good thermal stability during 200 thermal cycles between room temperature and 850 °C, in which the microstructure and martensitic transformation behavior have no obvious change
A prognostic signature based on snoRNA predicts the overall survival of lower-grade glioma patients
IntroductionSmall nucleolar RNAs (snoRNAs) are a group of non-coding RNAs enriched in the nucleus which direct post-transcriptional modifications of rRNAs, snRNAs and other molecules. Recent studies have suggested that snoRNAs have a significant role in tumor oncogenesis and can be served as prognostic markers for predicting the overall survival of tumor patients. MethodsWe screened 122 survival-related snoRNAs from public databases and eventually selected 7 snoRNAs that were most relevant to the prognosis of lower-grade glioma (LGG) patients for the establishment of the 7-snoRNA prognostic signature. Further, we combined clinical characteristics related to the prognosis of glioma patients and the 7-snoRNA prognostic signature to construct a nomogram.ResultsThe prognostic model displayed greater predictive power in both validation set and stratification analysis. Results of enrichment analysis revealed that these snoRNAs mainly participated in the post-transcriptional process such as RNA splicing, metabolism and modifications. In addition, 7-snoRNA prognostic signature were positively correlated with immune scores and expression levels of multiple immune checkpoint molecules, which can be used as potential biomarkers for immunotherapy prediction. From the results of bioinformatics analysis, we inferred that SNORD88C has a major role in the development of glioma, and then performed in vitro experiments to validate it. The results revealed that SNORD88C could promote the proliferation, invasion and migration of glioma cells. DiscussionWe established a 7-snoRNA prognostic signature and nomogram that can be applied to evaluate the survival of LGG patients with good sensitivity and specificity. In addition, SNORD88C could promote the proliferation, migration and invasion of glioma cells and is involved in a variety of biological processes related to DNA and RNA
Biomass based porous carbon for supercapacitor by hydrothermal assisted activating method
A facile route has been employed to synthesize a series of high performance activated carbons as the electrode material for supercapacitors. The structure of the carbons are characterized by N2 adsorption/desorption and FTIR spectroscopy. The electrochemical performances of the carbons as an electrode material were evaluated by cyclic voltammetry test and galvanostatic charge/discharge measurements. As a biomass derived carbon, KOH-1 exhibits high capacity, good rate capability and high energy density, indicating the promising application of hydrothermal combining with KOH activation method for biomass materials that used in supercapacitor
Acoustic emission during the solvent mediated cooling crystallization of citric acid
International audienceThe potential of Acoustic Emission (AE) for controlling crystallization processes is investigated. The sensing technology is successfully applied to monitor the batch cooling crystallization of citric acid (CA) in water. The solvent-mediated phase transition between the anhydrous and the monohydrated forms of CA is clearly detected from the recorded acoustic measurements. A tremendous amount of acoustic data is recorded by the equipment, and the analysis of the data is focused on the evaluation of AE as a new sensor for monitoring the basic steps of the crystallization processes (i.e., nucleation, growth, phase transition, etc.) A time- and frequency-domain analysis is presented which shows the wealth of the technique. It is finally concluded that AE allows very early detection of nucleation events, provides a means of monitoring the development of the crystallization process and allows monitoring phase transition phenomena obtained through cooling. It is thus suggested that acoustic emission could be valuable in the development of new crystallization monitoring and control strategies: this is all the more interesting that the acoustic piezo-sensor is non-intrusive and does not require any sampling of the slurry, two features which are of tremendous importance in the field of cooling crystallization processes
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