Hierarchical ResNeXt Models for Breast Cancer Histology Image Classification

Microscopic histology image analysis is a cornerstone in early detection of breast cancer. However these images are very large and manual analysis is error prone and very time consuming. Thus automating this process is in high demand. We proposed a hierarchical system of convolutional neural networks (CNN) that classifies automatically patches of these images into four pathologies: normal, benign, in situ carcinoma and invasive carcinoma. We evaluated our system on the BACH challenge dataset of image-wise classification and a small dataset that we used to extend it. Using a train/test split of 75%/25%, we achieved an accuracy rate of 0.99 on the test split for the BACH dataset and 0.96 on that of the extension. On the test of the BACH challenge, we've reached an accuracy of 0.81 which rank us to the 8th out of 51 teams

DeepAPT: Nation-State APT Attribution Using End-to-End Deep Neural Networks

In recent years numerous advanced malware, aka advanced persistent threats (APT) are allegedly developed by nation-states. The task of attributing an APT to a specific nation-state is extremely challenging for several reasons. Each nation-state has usually more than a single cyber unit that develops such advanced malware, rendering traditional authorship attribution algorithms useless. Furthermore, those APTs use state-of-the-art evasion techniques, making feature extraction challenging. Finally, the dataset of such available APTs is extremely small. In this paper we describe how deep neural networks (DNN) could be successfully employed for nation-state APT attribution. We use sandbox reports (recording the behavior of the APT when run dynamically) as raw input for the neural network, allowing the DNN to learn high level feature abstractions of the APTs itself. Using a test set of 1,000 Chinese and Russian developed APTs, we achieved an accuracy rate of 94.6%

VConv-DAE: Deep Volumetric Shape Learning Without Object Labels

With the advent of affordable depth sensors, 3D capture becomes more and more ubiquitous and already has made its way into commercial products. Yet, capturing the geometry or complete shapes of everyday objects using scanning devices (e.g. Kinect) still comes with several challenges that result in noise or even incomplete shapes. Recent success in deep learning has shown how to learn complex shape distributions in a data-driven way from large scale 3D CAD Model collections and to utilize them for 3D processing on volumetric representations and thereby circumventing problems of topology and tessellation. Prior work has shown encouraging results on problems ranging from shape completion to recognition. We provide an analysis of such approaches and discover that training as well as the resulting representation are strongly and unnecessarily tied to the notion of object labels. Thus, we propose a full convolutional volumetric auto encoder that learns volumetric representation from noisy data by estimating the voxel occupancy grids. The proposed method outperforms prior work on challenging tasks like denoising and shape completion. We also show that the obtained deep embedding gives competitive performance when used for classification and promising results for shape interpolation

Incoherent Effect of Fe and Ni Substitutions in the Ferromagnetic-Insulator La0.6Bi0.4MnO3+d

A comparative study of the effect of Fe and Ni doping on the bismuth based perovskite La0.6Bi0.4MnO3.1, a projected spintronics magnetic semiconductor has been carried out. The doped systems show an expressive change in magnetic ordering temperature. However, the shifts in ferromagnetic transition (TC) of these doped phases are in opposite direction with respect to the parent phase TC of 115 K. The Ni-doped phase shows an increase in TC ~200 K, whereas the Fe-doped phase exhibits a downward shift to TC~95 K. Moreover, the Fe-doped is hard-type whereas the Ni-doped compound is soft-type ferromagnet. It is observed that the materials are semiconducting in the ferromagnetic phase with activation energies of 77 & 82 meV for Fe & Ni-doped phases respectively. In the presence of external magnetic field of 7 Tesla, they exhibit minor changes in the resistivity behaviours and the maximum isothermal magnetoresistance is around -20 % at 125 K for the Ni-phase. The results are explained on the basis of electronic phase separation and competing ferromagnetic and antiferromagnetic interactions between the various mixed valence cations.Comment: 18 pages including figure

A self assembled monolayer based microfluidic sensor for urea detection

Urease (Urs) and glutamate dehydrogenase (GLDH) have been covalently co-immobilized onto a self-assembled monolayer (SAM) comprising of 10-carboxy-1-decanthiol (CDT) via EDC–NHS chemistry deposited onto one of the two patterned gold (Au) electrodes for estimation of urea using poly(dimethylsiloxane) based microfluidic channels (2 cm × 200 μm × 200 μm). The CDT/Au and Urs-GLDH/CDT/Au electrodes have been characterized using Fourier transform infrared (FTIR) spectroscopy, contact angle (CA), atomic force microscopy (AFM) and electrochemical cyclic voltammetry (CV) techniques. The electrochemical response measurement of a Urs-GLDH/CDT/Au bioelectrode obtained as a function of urea concentration using CV yield linearity as 10 to 100 mg dl−1, detection limit as 9 mg dl−1 and high sensitivity as 7.5 μA mM−1 cm−2

Collaborative Layer-wise Discriminative Learning in Deep Neural Networks

Intermediate features at different layers of a deep neural network are known to be discriminative for visual patterns of different complexities. However, most existing works ignore such cross-layer heterogeneities when classifying samples of different complexities. For example, if a training sample has already been correctly classified at a specific layer with high confidence, we argue that it is unnecessary to enforce rest layers to classify this sample correctly and a better strategy is to encourage those layers to focus on other samples. In this paper, we propose a layer-wise discriminative learning method to enhance the discriminative capability of a deep network by allowing its layers to work collaboratively for classification. Towards this target, we introduce multiple classifiers on top of multiple layers. Each classifier not only tries to correctly classify the features from its input layer, but also coordinates with other classifiers to jointly maximize the final classification performance. Guided by the other companion classifiers, each classifier learns to concentrate on certain training examples and boosts the overall performance. Allowing for end-to-end training, our method can be conveniently embedded into state-of-the-art deep networks. Experiments with multiple popular deep networks, including Network in Network, GoogLeNet and VGGNet, on scale-various object classification benchmarks, including CIFAR100, MNIST and ImageNet, and scene classification benchmarks, including MIT67, SUN397 and Places205, demonstrate the effectiveness of our method. In addition, we also analyze the relationship between the proposed method and classical conditional random fields models.Comment: To appear in ECCV 2016. Maybe subject to minor changes before camera-ready versio

Critical Behavioral Traits Foster Peer Engagement in Online Mental Health Communities

Online Mental Health Communities (OMHCs), such as Reddit, have witnessed a surge in popularity as go-to platforms for seeking information and support in managing mental health needs. Platforms like Reddit offer immediate interactions with peers, granting users a vital space for seeking mental health assistance. However, the largely unregulated nature of these platforms introduces intricate challenges for both users and society at large. This study explores the factors that drive peer engagement within counseling threads, aiming to enhance our understanding of this critical phenomenon. We introduce BeCOPE, a novel behavior encoded Peer counseling dataset comprising over 10,118 posts and 58,279 comments sourced from 21 mental health-specific subreddits. The dataset is annotated using three major fine-grained behavior labels: (a) intent, (b) criticism, and (c) readability, along with the emotion labels. Our analysis indicates the prominence of ``self-criticism'' as the most prevalent form of criticism expressed by help-seekers, accounting for a significant 43% of interactions. Intriguingly, we observe that individuals who explicitly express their need for help are 18.01% more likely to receive assistance compared to those who present ``surveys'' or engage in ``rants.'' Furthermore, we highlight the pivotal role of well-articulated problem descriptions, showing that superior readability effectively doubles the likelihood of receiving the sought-after support. Our study emphasizes the essential role of OMHCs in offering personalized guidance and unveils behavior-driven engagement patterns