A New Class of Attacks on Time Series Data Mining

Traditional research on preserving privacy in data mining focuses on time-invariant privacy issues. With the emergence of time series data mining, traditional snapshot-based privacy issues need to be extended to be multi-dimensional with the addition of time dimension. We find current techniques to preserve privacy in data mining are not effective in preserving time-domain privacy. We present the data flow separation attack on privacy in time series data mining, which is based on blind source separation techniques from statistical signal processing. Our experiments with real data show that this attack is effective. By combining the data flow separation method and the frequency matching method, an attacker can identify data sources and compromise time-domain privacy. We propose possible countermeasures to the data flow separation attack in the paper

A New Class of Attacks on Time Series Data Mining

Traditional research on preserving privacy in data mining focuses on time-invariant privacy issues. With the emergence of time series data mining, traditional snapshot-based privacy issues need to be extended to be multi-dimensional with the addition of time dimension. We find current techniques to preserve privacy in data mining are not effective in preserving time-domain privacy. We present the data flow separation attack on privacy in time series data mining, which is based on blind source separation techniques from statistical signal processing. Our experiments with real data show that this attack is effective. By combining the data flow separation method and the frequency matching method, an attacker can identify data sources and compromise time-domain privacy. We propose possible countermeasures to the data flow separation attack in the paper

Brain Structure Segmentation from MRI by Geometric Surface Flow

We present a method for semiautomatic segmentation of brain structures such as thalamus from MRI images based on the concept of geometric surface flow. Given an MRI image, the user can interactively initialize a seed model within region of interest. The model will then start to evolve by incorporating both boundary and region information following the principle of variational analysis. The deformation will stop when an equilibrium state is achieved. To overcome the low contrast of the original image data, a nonparametric kernel-based method is applied to simultaneously update the interior probability distribution during the model evolution. Our experiments on both 2D and 3D image data demonstrate that the new method is robust to image noise and inhomogeneity and will not leak from spurious edge gaps

Thalamus Segmentation from Diffusion Tensor Magnetic Resonance Imaging

We propose a semi-automatic thalamus and thalamus nuclei segmentation algorithm from Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) based on the mean-shift algorithm. Comparing with existing thalamus segmentation algorithms which are mainly based on K-means algorithm, our mean-shift based algorithm is more flexible and adaptive. It does not assume a Gaussian distribution or a fixed number of clusters. Furthermore, the single parameter in the mean-shift based algorithm supports hierarchical clustering naturally

A tunable plasmonic refractive index sensor with nanoring-strip graphene arrays

In this paper, a tunable plasmonic refractive index sensor with nanoring-strip graphene arrays is numerically investigated by the finite difference time domain (FDTD) method. The simulation results exhibit that by changing the sensing medium refractive index nmed of the structure, the sensing range of the system is large. By changing the doping level ng, we noticed that the transmission characteristics can be adjusted flexibly. The resonance wavelength remains entirely the same and the transmission dip enhancement over a big range of incidence angles [0,45] for both TM and TE polarizations, which indicates that the resonance of the graphene nanoring-strip arrays is insensitive to angle polarization. The above results are undoubtedly a new way to realize various tunable plasmon devices, and may have a great application prospect in biosensing, detection and imaging

What Goes beyond Multi-modal Fusion in One-stage Referring Expression Comprehension: An Empirical Study

Most of the existing work in one-stage referring expression comprehension (REC) mainly focuses on multi-modal fusion and reasoning, while the influence of other factors in this task lacks in-depth exploration. To fill this gap, we conduct an empirical study in this paper. Concretely, we first build a very simple REC network called SimREC, and ablate 42 candidate designs/settings, which covers the entire process of one-stage REC from network design to model training. Afterwards, we conduct over 100 experimental trials on three benchmark datasets of REC. The extensive experimental results not only show the key factors that affect REC performance in addition to multi-modal fusion, e.g., multi-scale features and data augmentation, but also yield some findings that run counter to conventional understanding. For example, as a vision and language (V&L) task, REC does is less impacted by language prior. In addition, with a proper combination of these findings, we can improve the performance of SimREC by a large margin, e.g., +27.12% on RefCOCO+, which outperforms all existing REC methods. But the most encouraging finding is that with much less training overhead and parameters, SimREC can still achieve better performance than a set of large-scale pre-trained models, e.g., UNITER and VILLA, portraying the special role of REC in existing V&L research

Visualizing and Modeling Interior Spaces of Dangerous Structures using Lidar

LIght Detection and Ranging (LIDAR) scanning can be used to safely and remotely provide intelligence on the interior of dangerous structures for use by first responders that need to enter these structures. By scanning into structures through windows and other openings or moving the LIDAR scanning into the structure, in both cases carried by a remote controlled robotic crawler, the presence of dangerous items or personnel can be confi rmed or denied. Entry and egress pathways can be determined in advance, and potential hiding/ambush locations identifi ed. This paper describes an integrated system of a robotic crawler and LIDAR scanner. Both the scanner and the robot are wirelessly remote controlled from a single laptop computer. This includes navigation of the crawler with real-time video, self-leveling of the LIDAR platform, and the ability to raise the scanner up to heights of 2.5 m. Multiple scans can be taken from different angles to fi ll in detail and provide more complete coverage. These scans can quickly be registered to each other using user defi ned \u27pick points\u27, creating a single point cloud from multiple scans. Software has been developed to deconstruct the point clouds, and identify specifi c objects in the interior of the structure from the point cloud. Software has been developed to interactively visualize and walk through the modeled structures. Floor plans are automatically generated and a data export facility has been developed. Tests have been conducted on multiple structures, simulating many of the contingencies that a fi rst responder would face

A tunable plasmonic refractive index sensor with nanoring-strip graphene arrays

In this paper, a tunable plasmonic refractive index sensor with nanoring-strip graphene arrays is numerically investigated by the finite difference time domain (FDTD) method. The simulation results exhibit that by changing the sensing medium refractive index nmed of the structure, the sensing range of the system is large. By changing the doping level ng, we noticed that the transmission characteristics can be adjusted flexibly. The resonance wavelength remains entirely the same and the transmission dip enhancement over a big range of incidence angles [0,45] for both TM and TE polarizations, which indicates that the resonance of the graphene nanoring-strip arrays is insensitive to angle polarization. The above results are undoubtedly a new way to realize various tunable plasmon devices, and may have a great application prospect in biosensing, detection and imaging

Photoluminescence mechanism and applications of Zn-doped carbon dots

Heteroatom-doped carbon dots (CDs) with excellent optical characteristics and negligible toxicity have emerged in many applications including bioimaging, biosensing, photocatalysis, and photothermal therapy. The metal-doping of CDs using various heteroatoms results in an enhancement of the photophysics but also imparts them with multifunctionality. However, unlike nonmetal doping, typical metal doping results in low fluorescence quantum yields (QYs), and an unclear photoluminescence mechanism. In this contribution, we detail results concerning zinc doped CDs (Zn-CDs) with QYs of up to 35%. The zinc ion charges serve as a surface passivating agent and prevent the aggregation of graphene p–p stacking, leading to an increase in the QY of the Zn-CDs. Structural and chemical investigations using spectroscopic and first principle simulations further revealed the effects of zinc doping on the CDs. The robust Zn-CDs were used for the ultra-trace detection of Hg2+ with a detection limit of 0.1 mM, and a quench mechanism was proposed. The unique optical properties of the Zn-CDs have promise for use in applications such as in vivo sensing and future phototherapy applications

Seasonal environmental factors drive microbial community succession and flavor quality during acetic acid fermentation of Zhenjiang aromatic vinegar

This study investigated the impact of seasonal environmental factors on microorganisms and flavor compounds during acetic acid fermentation (AAF) of Zhenjiang aromatic vinegar (ZAV). Environmental factors were monitored throughout the fermentation process, which spanned multiple seasons. Methods such as headspace solid phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS), high performance liquid chromatography (HPLC), and high-throughput sequencing were employed to examine how these environmental factors influenced the flavor profile and microbial community of ZAV. The findings suggested that ZAV brewed in autumn had the strongest flavor and sweetness. The key microorganisms responsible for the flavor of ZAV included Lactobacillus acetotolerans, Lactobacillus plantarum, Lactobacillus reuteri, Lactobacillus fermentum, Acetobacter pasteurianus. Moreover, correlation analysis showed that room temperature had a significant impact on the composition of the microbial community, along with other key seasonal environmental factors like total acid, pH, reducing sugar, and humidity. These results provide a theoretical foundation for regulating core microorganisms and environmental factors during fermentation, enhancing ZAV quality