141 research outputs found
Effect of Crystallization Time on Behaviors of Glass-ceramic Produced from Sludge Incineration Ash
AbstractIncineration has become a significant treatment method for municipal sewage sludge because of the rising difficulty to find suitable sites for traditional landfill. However, a large amount of sludge incineration ash containing high levels of heavy metals is remained. In order to achieve resource utilization, glass–ceramics have been produced using sludge incineration ash. The optimum heat treatment was identified as Tn = 837°C for 1.0 h and Tc = 977°C for 2.0 h, respectively. The major crystalline phase identified from X-ray diffraction (XRD) and scanning electron microscopy (SEM) was wollastonite (CaSiO3) and the products displayed good performances. The results indicated that it was a feasible attempt to produce glass-ceramics from sludge incineration ash as decorative materials
MyEvents: a personal visual analytics approach for mining key events and knowledge discovery in support of personal reminiscence
Reminiscence is an important aspect in our life. It preserves precious memories, allows us to form our own identities and encourages us to accept the past. Our work takes advantage of modern sensor technologies to support reminiscence, enabling self-monitoring of personal activities and individual movement in space and time on a daily basis. This paper presents MyEvents, a web-based personal visual analytics platform designed for non-computing experts, that allows for the collection of long-term location and movement data and the generation of event mementos. Our research is focused on two prominent goals in event reminiscence: 1) selection subjectivity and human involvement in the process of self knowledge discovery and memento creation; and 2) the enhancement of event familiarity by presenting target events and their related information for optimal memory recall and reminiscence. A novel multi-significance event ranking model is proposed to determine significant events in the personal history according to user preferences for event category, frequency and regularity. The evaluation results show that MyEvents effectively fulfils the reminiscence goals and tasks.
Semantic lifting and reasoning on the personalised activity big data repository for healthcare research
The fast growing markets of smart health monitoring devices and mobile applications provide opportunities for common citizens to have capability for understanding and managing their own health situations. However, there are many challenges for data engineering and knowledge discovery research to enable efficient extraction of knowledge from data that is collected from heterogonous devices and applications with big volumes and velocity. This paper presents research that initially started with the EC MyHealthAvatar project and is under continual improvement following the project’s completion. The major contribution of the work is a comprehensive big data and semantic knowledge discovery framework which integrates data from varied data resources. The framework applies hybrid database architecture of NoSQL and RDF repositories with introductions for semantic oriented data mining and knowledge lifting algorithms. The activity stream data is collected through Kafka’s big data processing component. The motivation of the research is to enhance the knowledge management, discovery capabilities and efficiency to support further accurate health risk analysis and lifestyle summarisation
Generation of Ultra-intense Gamma-ray Train by QED Harmonics
When laser intensity exceeds 10^22W/cm^2, photons with energy above MeV can
be generated from high-order harmonics process in the laser-plasma interaction.
We find that under such laser intensity, QED effect plays a dominating role in
the radiation pattern. Contrast to the gas and relativistic HHG processes, both
the occurrence and energy of gamma-ray emission produced by QED harmonics are
random and QED harmonics are usually not coherent, while the property of high
intensity and ultra-short duration is conserved. Our simulation shows that the
period of gamma-ray train is half of the laser period and the peak intensity is
1.4e22W/cm^2. This new harmonic production with QED effects are crucial to
light-matter interaction in strong field and can be verified in experiments by
10PW laser facilities in the near future.Comment: 12 pages, 4 figure
Enhanced baseline activity in the left ventromedial putamen predicts individual treatment response in drug-naive, first-episode schizophrenia: Results from two independent study samples
BACKGROUND: Antipsychotic medications are the common treatment for schizophrenia. However, reliable biomarkers that can predict individual treatment response are still lacking. The present study aimed to examine whether baseline putamen activity can predict individual treatment response in schizophrenia.
METHODS: Two independent samples of patients with drug-naive, first-episode schizophrenia (32 patients in sample 1 and 44 in sample 2) and matched healthy controls underwent resting-state functional magnetic resonance imaging (fMRI) at baseline. Patients were treated with olanzapine for 8 weeks; symptom severity was assessed using the Positive and Negative Syndrome Scale (PANSS) at baseline and week 8. Fractional amplitude of low frequency fluctuation (fALFF) and pattern classification techniques were used to analyze the data.
FINDINGS: Univariate analysis shows an elevated pre-treatment fALFF in the left ventromedial putamen in both patient samples compared to healthy controls (p\u27s \u3c 0.001). The support vector regression (SVR) analysis suggests a positive relationship between baseline pre-treatment fALFF in the left ventromedial putamen and improvement in positive symptom at week 8 in each patient group using a cross-validated method (r=0.452, p=.002; r=0.511, p=.003, respectively).
INTERPRETATION: Our study suggests that elevated pre-treatment mean fALFF in the left ventromedial putamen may predict individual therapeutic response to olanzapine treatment in drug-naive, first-episode patients with schizophrenia. Future studies are needed to confirm whether this finding is generalizable to patients with schizophrenia treated with other antipsychotic medications.
FUND: The National Key RandD Program of China and the National Natural Science Foundation of China
LMSanitator: Defending Prompt-Tuning Against Task-Agnostic Backdoors
Prompt-tuning has emerged as an attractive paradigm for deploying large-scale
language models due to its strong downstream task performance and efficient
multitask serving ability. Despite its wide adoption, we empirically show that
prompt-tuning is vulnerable to downstream task-agnostic backdoors, which reside
in the pretrained models and can affect arbitrary downstream tasks. The
state-of-the-art backdoor detection approaches cannot defend against
task-agnostic backdoors since they hardly converge in reversing the backdoor
triggers. To address this issue, we propose LMSanitator, a novel approach for
detecting and removing task-agnostic backdoors on Transformer models. Instead
of directly inverting the triggers, LMSanitator aims to invert the predefined
attack vectors (pretrained models' output when the input is embedded with
triggers) of the task-agnostic backdoors, which achieves much better
convergence performance and backdoor detection accuracy. LMSanitator further
leverages prompt-tuning's property of freezing the pretrained model to perform
accurate and fast output monitoring and input purging during the inference
phase. Extensive experiments on multiple language models and NLP tasks
illustrate the effectiveness of LMSanitator. For instance, LMSanitator achieves
92.8% backdoor detection accuracy on 960 models and decreases the attack
success rate to less than 1% in most scenarios.Comment: To Appear in the Network and Distributed System Security (NDSS)
Symposium 2024, 26 February - 1 March 2024, San Diego, CA, USA; typos
correcte
MyHealthAvatar and CARRE: case studies of interactive visualisation for Internet-enabled sensor-assisted health monitoring and risk analysis
With the progress of wearable sensor technologies, more wearable health sensors have been made available on the market, which enables not only people to monitor their health and lifestyle in a continuous way but also doctors to utilise them to make better diagnoses. Continuous measurement from a variety of wearable sensors implies that a huge amount of data needs to be collected, stored, processed and presented, which cannot be achieved by traditional data processing methods. Visualisation is designed to promote knowledge discovery and utilisation via mature visual paradigms with well-designed user interactions and has become indispensable in data analysis. In this paper we introduce the role of visualisation in wearable sensor-assisted health analysis platforms by case studies of two projects funded by the European Commission: MyHealthAvatar and CARRE. The former focuses on health sensor data collection and lifestyle tracking while the latter aims to provide innovative means for the management of cardiorenal diseases with the assistance of wearable sensors. The roles of visualisation components including timeline, parallel coordinates, map, node-link diagrams, Sankey diagrams, etc. are introduced and discussed
L-band GHz femtosecond passively harmonic mode-locked Er-doped fiber laser based on nonlinear polarization rotation
Via using an L-band optimized in-fiber polarizing grating device, a GHz L-band femtosecond passively harmonic mode-locked (PHML) Er-doped fiber laser based on nonlinear polarization rotation (NPR) is firstly demonstrated. 4.22 GHz pulses with the duration of 810 fs and super-mode suppression ratio (SMSR) of 32 dB are obtained under the pump power of 712 mW corresponding to 215th harmonic order. The central wavelength of 4.22 GHz pulses is 1581.7 nm with 10.1 nm 3-dB bandwidth. Furthermore, under this fixed pump power, higher harmonic orders can also be attained by rotating the polarization controllers (PCs) properly. The highest repetition rate we obtained is 7.41 GHz with the SMSR of 20.7 dB
Disrupted asymmetry of inter- and intra-hemispheric functional connectivity in patients with drug-naive, first-episode schizophrenia and their unaffected siblings
BACKGROUND: Lack of normal asymmetry in the brain has been reported in patients with schizophrenia. However, it remains unclear whether disrupted asymmetry originates from inter-hemispheric functional connectivity (FC) and/or intra-hemispheric FC in this patient population.
METHODS: Forty-four patients with drug-naive, first-episode schizophrenia, 42 unaffected siblings, and 44 healthy controls underwent resting-state functional magnetic resonance imaging (fMRI) scan. The parameter of asymmetry (PAS) and support vector machine (SVM) were used to analyze the data. Patients were treated with olanzapine for 8 weeks.
FINDINGS: Compared with healthy controls, patients showed lower PAS scores in the left middle temporal gyrus (MTG)/inferior temporal gyrus (ITG), left posterior cingulate cortex (PCC)/precuneus and left angular gyrus, and higher PAS scores in the left precentral gyrus/postcentral gyrus. Unaffected siblings also showed lower PAS scores in the left MTG/ITG and left PCC/precuneus relative to healthy controls. Further, SVM analysis showed that a combination of the PAS scores in these two clusters in patients at baseline was able to predict clinical response after 8weeks of olanzapine treatment with 77.27% sensitivity, 72.73% specificity, and 75.00% accuracy.
INTERPRETATION: The present study suggests disrupted asymmetry of inter- and intra-hemispheric FC in drug-naive, first-episode schizophrenia; in addition, a reduced asymmetry of inter-hemispheric FC in the left MTG/ITG and left PCC/precuneus may serve as an endophenotype for schizophrenia, and may have clinical utility to predict response to olanzapine treatment. FUND: The National Key RandD Program of China and the National Natural Science Foundation of China
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