What Does CNN Shift Invariance Look Like? A Visualization Study

Feature extraction with convolutional neural networks (CNNs) is a popular method to represent images for machine learning tasks. These representations seek to capture global image content, and ideally should be independent of geometric transformations. We focus on measuring and visualizing the shift invariance of extracted features from popular off-the-shelf CNN models. We present the results of three experiments comparing representations of millions of images with exhaustively shifted objects, examining both local invariance (within a few pixels) and global invariance (across the image frame). We conclude that features extracted from popular networks are not globally invariant, and that biases and artifacts exist within this variance. Additionally, we determine that anti-aliased models significantly improve local invariance but do not impact global invariance. Finally, we provide a code repository for experiment reproduction, as well as a website to interact with our results at https://jakehlee.github.io/visualize-invariance.Comment: Presented at the 2020 ECCV Workshop on Real-World Computer Vision from Inputs with Limited Quality (RLQ-TOD 2020), Glasgow, Scotlan

On Spectral Conditions for Positive Realness of Transfer Function Matrices

Necessary and sufficient conditions for strict positive realness and positive realness of general transfer function matrices are derived. The conditions are expressed in terms of eigenvalues of matrix functions of the state matrices representation of the LTI system. Illustrative numerical examples are provided

A non-invasive method for link upgrade planning using coarse-grained measurements

A basic problem faced by network operators concerns the provisioning of bandwidth to meet quality of service (QoS) requirements. In the network core, the preferred solution is simply to overprovision link bandwidth. We propose a new approach to making link upgrade decisions based only on readily available coarse SNMP measurements

Soil N availability, rather than N deposition, controls indirect N2O emissions

Ammonia volatilised and re-deposited to the landscape is an indirect N2O emission source. This study established a relationship between N2O emissions, low magnitude NH4 deposition (0–30  kg N ha − 1 ), and soil moisture content in two soils using in-vessel incubations. Emissions from the clay soil peaked ( < 0.002 g N [ g soil ] − 1 min − 1 ) from 85 to 93% WFPS (water filled pore space), increasing to a plateau as remaining mineral-N increased. Peak N2O emissions for the sandy soil were much lower ( < 5 × 10 − 5 μg N [ g soil ] − 1 min − 1 ) and occurred at about 60% WFPS, with an indistinct relationship with increasing resident mineral N due to the low rate of nitrification in that soil. Microbial community and respiration data indicated that the clay soil was dominated by denitrifiers and was more biologically active than the sandy soil. However, the clay soil also had substantial nitrifier communities even under peak emission conditions. A process-based mathematical denitrification model was well suited to the clay soil data where all mineral-N was assumed to be nitrified ( R 2 = 90 % ), providing a substrate for denitrification. This function was not well suited to the sandy soil where nitrification was much less complete. A prototype relationship representing mineral-N pool conversions (NO3− and NH4+) was proposed based on time, pool concentrations, moisture relationships, and soil rate constants (preliminary testing only). A threshold for mineral-N was observed: emission of N2O did not occur from the clay soil for mineral-N <70 mg ( kg of soil ) − 1 , suggesting that soil N availability controls indirect N2O emissions. This laboratory process investigation challenges the IPCC approach which predicts indirect emissions from atmospheric N deposition alone

Methods of exposure assessment: lead-contaminated dust in Philadelphia schools.

This study was conducted to develop a method that would accurately assess children's exposure to lead in schools in Philadelphia, Pennsylvania. We examined three wipe sample protocols: one included accessible surfaces such as desktops and windowsills, the second included inaccessible surfaces such as the top of filing cabinets and light fixtures, and the third included hand wipes of the study participants. Surface wipes were collected at 10 locations from accessible and inaccessible classroom surfaces (n = 11 at each location) and from the palms of student subjects in the same locations (n = 168). We found a significant difference in lead dust concentrations determined by the three protocols (F = 4.619; 2,27 degrees of freedom; p = 0.019). Lead dust concentrations were significantly elevated at the inaccessible surfaces yet they were uniformly low on the accessible surfaces and the children's palms. These findings were consistent with observed changes in blood lead levels of study participants: after 6 months of exposure to the study locations, 156 of 168 children experienced no change in blood lead level, whereas 12 experienced only a minimal change of 1-2 microg/dL. The mere presence of lead in inaccessible dust in the school environment does not automatically constitute a health hazard because there may not be a completed exposure pathway

DeepCapture: Image Spam Detection Using Deep Learning and Data Augmentation

Image spam emails are often used to evade text-based spam filters that detect spam emails with their frequently used keywords. In this paper, we propose a new image spam email detection tool called DeepCapture using a convolutional neural network (CNN) model. There have been many efforts to detect image spam emails, but there is a significant performance degrade against entirely new and unseen image spam emails due to overfitting during the training phase. To address this challenging issue, we mainly focus on developing a more robust model to address the overfitting problem. Our key idea is to build a CNN-XGBoost framework consisting of eight layers only with a large number of training samples using data augmentation techniques tailored towards the image spam detection task. To show the feasibility of DeepCapture, we evaluate its performance with publicly available datasets consisting of 6,000 spam and 2,313 non-spam image samples. The experimental results show that DeepCapture is capable of achieving an F1-score of 88%, which has a 6% improvement over the best existing spam detection model CNN-SVM with an F1-score of 82%. Moreover, DeepCapture outperformed existing image spam detection solutions against new and unseen image datasets.Comment: 15 pages, single column. ACISP 2020: Australasian Conference on Information Security and Privac

The influence of barefoot and barefoot inspired footwear on the kinetics and kinematics of running in comparison to conventional running shoes.

Barefoot running has experienced a resurgence in footwear biomechanics literature, based on the supposition that it serves to reduce the occurrence of overuse injuries in comparison to conventional shoe models. This consensus has lead footwear manufacturers to develop shoes which aim to mimic the mechanics of barefoot locomotion. This study compared the impact kinetics and 3-D joint angular kinematics observed whilst running: barefoot, in conventional cushioned running shoes and in shoes designed to integrate the perceived benefits of barefoot locomotion. The aim of the current investigation was therefore to determine whether differences in impact kinetics exist between the footwear conditions and whether shoes which aim to simulate barefoot movement patterns can closely mimic the 3-D kinematics of barefoot running. Twelve participants ran at 4.0 m.s-1±5% in each footwear condition. Angular joint kinematics from the hip, knee and ankle in the sagittal, coronal and transverse planes were measured using an eight camera motion analysis system. In addition simultaneous tibial acceleration and ground reaction forces were obtained. Impact parameters and joint kinematics were subsequently compared using repeated measures ANOVAs. The kinematic analysis indicates that in comparison to the conventional and barefoot inspired shoes that running barefoot was associated significantly greater plantar-flexion at footstrike and range of motion to peak dorsiflexion. Furthermore, the kinetic analysis revealed that compared to the conventional footwear impact parameters were significantly greater in the barefoot condition. Therefore this study suggests that barefoot running is associated with impact kinetics linked to an increased risk of overuse injury, when compared to conventional shod running. Furthermore, the mechanics of the shoes which aim to simulate barefoot movement patterns do not appear to closely mimic the kinematics of barefoot locomotion

CSC-GAN:Cycle and Semantic Consistency for Dataset Augmentation

Image-to-image translation is a computer vision problem where a task learns a mapping from a source domain A to a target domain B using a training set. However, this translation is not always accurate, and during the translation process, relevant semantic information can deteriorate. To handle this problem, we propose a new cycle-consistent, adversarially trained image-to-image translation with a loss function that is constrained by semantic segmentation. This formulation encourages the model to preserve semantic information during the translation process. For this purpose, our loss function evaluates the accuracy of the synthetically generated image against a semantic segmentation model, previously trained. Reported results show that our proposed method can significantly increase the level of details in the synthetic images. We further demonstrate our method’s effectiveness by applying it as a dataset augmentation technique, for a minimal dataset, showing that it can improve the semantic segmentation accuracy

The Classification and Evolution of Bacterial Cross-Feeding

Bacterial feeding has evolved toward specific evolutionary niches and the sources of energy differ between species and strains. Although bacteria fundamentally compete for nutrients, the excreted products from one strain may be the preferred energy source or a source of essential nutrients for another strain. The large variability in feeding preferences between bacterial strains often provides for complex cross-feeding relationships between bacteria, particularly in complex environments such as the human lower gut, which impacts on the host's digestion and nutrition. Although a large amount of information is available on cross-feeding between bacterial strains, it is important to consider the evolution of cross-feeding. Adaptation to environmental stimuli is a continuous process, thus understanding the evolution of microbial cross-feeding interactions allows us to determine the resilience of microbial populations to changes to this environment, such as changes in nutrient supply, and how new interactions might emerge in the future. In this review, we provide a framework of terminology dividing bacterial cross-feeding into four forms that can be used for the classification and analysis of cross-feeding dynamics. Under the proposed framework, we discuss the evolutionary origins for the four forms of cross-feeding and factors such as spatial structure that influence their emergence and subsequent persistence. This review draws from both the theoretical and experimental evolutionary literature to provide a cross-disciplinary perspective on the evolution of different types of cross-feeding