Oglerooney4115

Extensive experiments demonstrated that the proposed IPS2 significantly outperformed previous similarity-based methods on real-world datasets and it was capable of handling the clustering task over under-sampled and noisy datasets.We introduce a novel network, called CO-attention Siamese Network (COSNet), to address the zero-shot video object segmentation task in a holistic fashion. selleck chemicals llc We exploit the inherent correlation among video frames and incorporate a global co-attention mechanism to further improve the state-of-the-art deep learning based solutions that primarily focus on learning discriminative foreground representations over appearance and motion in short-term temporal segments. The co-attention layers in COSNet provide efficient and competent stages for capturing global correlations and scene context by jointly computing and appending co-attention responses into a joint feature space. COSNet is a unified and end-to-end trainable framework where different co-attention variants can be derived for capturing diverse properties of the learned joint feature space. We train COSNet with pairs (or groups) of video frames, and this naturally augments training data and allows increased learning capacity. During the segmentation stage, the co-attention model encodes useful information by processing multiple reference frames together, which is leveraged to infer the frequently reappearing and salient foreground objects better. Our extensive experiments over three large benchmarks demonstrate that COSNet outperforms the current alternatives by a large margin. Our algorithm implementations have been made publicly available at https//github.com/carrierlxk/COSNet.

Magnetoencephalography (MEG) signals typically reflect a mixture of neuromagnetic fields, subject-related artifacts, external interference and sensor noise. Even inside a magnetically shielded room, external interference can be significantly stronger than brain signals. Methods such as signal-space projection (SSP) and signal-space separation (SSS) have been developed to suppress this residual interference, but their performance might not be sufficient in cases of strong interference or when the sources of interference change over time.

Here we suggest a new method, extended signal-space separation (eSSS), which combines a physical model of the magnetic fields (as in SSS) with a statistical description of the interference (as in SSP). We demonstrate the performance of this method via simulations and experimental MEG data.

The eSSS method clearly outperforms SSS and SSP in interference suppression regardless of the extent of a priori information available on the interference sources. We also show that the method does not cause location or amplitude bias in dipole modeling.

Our eSSS method provides better data quality than SSP or SSS and can be readily combined with other SSS-based methods, such as tSSS or head movement compensation. Thus, eSSS extends and complements the interference suppression techniques currently available for MEG.

Due to its ability to suppress external interference to the level of sensor noise, eSSS can facilitate single-trial data analysis, exemplified in automated analysis of epileptic data. Such an enhanced suppression performance is especially important in environments with large interference fields.

Due to its ability to suppress external interference to the level of sensor noise, eSSS can facilitate single-trial data analysis, exemplified in automated analysis of epileptic data. Such an enhanced suppression performance is especially important in environments with large interference fields.Chemoresistance causes tumor recurrence and metastasis, resulting in poor clinical outcomes and low survival, and has been considered an obstacle to tumor therapy. The development of novel therapeutic approaches that can effectively kill chemoresistant tumor cells (CRTCs) is therefore critical to overcoming these obstacles.

Here, we introduce an emerging physical feature-based therapeutic approach based on nanosecond pulsed electric fields (nsPEFs). The goal of this study is to investigate the effect of nsPEFs on CRTCs.

The cell viability, ablation effects on a 3D-cultured scaffold, and lethal thresholds of nsPEFs were evaluated according to fluorescence staining assays.

nsPEF treatment preferentially affected chemoresistant cells (A549/CDDP) with a higher cell viability inhibition ability/cell death rate, larger ablation area, and lower ablation threshold compared to their respective homologous tumor cells (A549). The experimental and theoretical studies suggested that nsPEFs displayed selective behavior toward intracellular structures. With this selective character, nsPEFs can induce higher electroporation effects (e.g., higher pore number, larger electroporation area, and faster fluorescence dissipation on the nuclear envelope) on CRTCs due to their larger nuclear size and cell membrane capacitance.

These findings demonstrated that nsPEFs induced preferential ablation of CRTCs over their respective homologous tumor cells.

This study provides an experimental and theoretical basis for the study of killing CRTCs by electrical treatments and suggests potential applications in the optimization of novel anti-chemoresistance methods.

This study provides an experimental and theoretical basis for the study of killing CRTCs by electrical treatments and suggests potential applications in the optimization of novel anti-chemoresistance methods.

We present a novel, low-profile, scleral-coil based, distance ranging system which is suitable for smart, accommodating contact lenses.

We measure the induced emf between a set of four thin semi-circular coils patterned on flexible Kapton substrates that conform to the eyes' sclera. This induced emf is a function of eye gaze angles. The distance from the eyes to the desired object is next determined via the triangulation of the eye gaze angles Results Experiments on simulated tissue gel eyeballs indicate an accurate prediction of object distance in the 0.1-15 D (diopter) range with a 0.15 D RMS error and object direction in the -15 to 15-degree field of view with 0.4-degree RMS error, respectively. The energy required per range reading was determined to be as low as 20 μJ.

Experimental data shows that the distance ranging system can accurately measure eye-gaze angles and object-distance with very low energy consumption.

The high-accuracy, low-profile and reduced energy requirements make the distance ranger suitable for low-power vision corrective applications such as smart contact lenses.