Rethinking the Inception Architecture for Computer Vision

Convolutional networks are at the core of most state-of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains in various benchmarks. Although increased model size and computational cost tend to translate to immediate quality gains for most tasks (as long as enough labeled data is provided for training), computational efficiency and low parameter count are still enabling factors for various use cases such as mobile vision and big-data scenarios. Here we explore ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization. We benchmark our methods on the ILSVRC 2012 classification challenge validation set demonstrate substantial gains over the state of the art: 21.2% top-1 and 5.6% top-5 error for single frame evaluation using a network with a computational cost of 5 billion multiply-adds per inference and with using less than 25 million parameters. With an ensemble of 4 models and multi-crop evaluation, we report 3.5% top-5 error on the validation set (3.6% error on the test set) and 17.3% top-1 error on the validation set

Evaluation of engineered nanoparticle toxic effect on wastewater microorganisms: current status and challenges

The use of engineered nanoparticles (ENPs) in a wide range of products is associated with an increased concern for environmental safety due to their potential toxicological and adverse effects. ENPs exert antimicrobial properties through different mechanisms such as the formation of reactive oxygen species, disruption of physiological and metabolic processes. Although there are little empirical evidences on environmental fate and transport of ENPs, biosolids in wastewater most likely would be a sink for ENPs. However, there are still many uncertainties in relation to ENPs impact on the biological processes during wastewater treatment. This review provides an overview of the available data on the plausible effects of ENPs on AS and AD processes, two key biologically relevant environments for understanding ENPs–microbial interactions. It indicates that the impact of ENPs is not fully understood and few evidences suggest that ENPs could augment microbial-mediated processes such as AS and AD. Further to this, wastewater components can enhance or attenuate ENPs effects. Meanwhile it is still difficult to determine effective doses and establish toxicological guidelines, which is in part due to variable wastewater composition and inadequacy of current analytical procedures. Challenges associated with toxicity evaluation and data interpretation highlight areas in need for further research studies

Rethinking Scale Imbalance in Semi-supervised Object Detection for Aerial Images

This paper focuses on the scale imbalance problem of semi-supervised object detection(SSOD) in aerial images. Compared to natural images, objects in aerial images show smaller sizes and larger quantities per image, increasing the difficulty of manual annotation. Meanwhile, the advanced SSOD technique can train superior detectors by leveraging limited labeled data and massive unlabeled data, saving annotation costs. However, as an understudied task in aerial images, SSOD suffers from a drastic performance drop when facing a large proportion of small objects. By analyzing the predictions between small and large objects, we identify three imbalance issues caused by the scale bias, i.e., pseudo-label imbalance, label assignment imbalance, and negative learning imbalance. To tackle these issues, we propose a novel Scale-discriminative Semi-Supervised Object Detection (S^3OD) learning pipeline for aerial images. In our S^3OD, three key components, Size-aware Adaptive Thresholding (SAT), Size-rebalanced Label Assignment (SLA), and Teacher-guided Negative Learning (TNL), are proposed to warrant scale unbiased learning. Specifically, SAT adaptively selects appropriate thresholds to filter pseudo-labels for objects at different scales. SLA balances positive samples of objects at different scales through resampling and reweighting. TNL alleviates the imbalance in negative samples by leveraging information generated by a teacher model. Extensive experiments conducted on the DOTA-v1.5 benchmark demonstrate the superiority of our proposed methods over state-of-the-art competitors. Codes will be released soon

Effects of Asparagopsis taxiformis on enteric methane emission, rumen fermentation and lactational performance of (Norwegian Red) dairy cows

Ruminants with their rumen microbial fermentation contribute to greenhouse gas emissions when producing enteric methane. Feed supplements able to inhibit the formation of methane and thereby reduce the amount of methane emitted from rumen fermentation have been of great interest among researchers. Asparagopsis taxiformis, a red seaweed producing a diverse range of methane analogs, have recently proven its potential as a methane mitigator. The aim of this thesis was to further evaluate the potential mitigating effects of A. taxiformis supplemented to Norwegian red dairy cattle, including its possible effects on rumen fermentation parameters and lactational performance. This study confirms a dose-dependent mitigating potential of A. taxiformis, where methane production (g/day) by dairy cattle fed 0.25% A. taxiformis on an organic matter basis were significantly reduced by 22% (P = 0.037). Including A. taxiformis in the diet also affected feed palatability and significantly reduced dry matter intake (P < 0.001) and further milk yield (P < 0.001). Cows fed A. taxiformis had a significant decrease in rumen acetate-to-propionate ratio (P = 0.028) and displayed a significant (P = 0.026) increase in milk lactose contents (%). While a methane mitigating effect of Asparagopsis taxiformis was obtained, further work is required to assess possible adverse and long-term effects on animal health and productivity.Drøvtyggere og deres mikrobielle vomfermentering bidrar til drivhusgassutslipp når metna produseres. Fôrtilskudd som kan hemme dannelsen av metan og dermed redusere mengden metan frigitt fra vomfermentering har vært av stor interesse blant forskere. Asparagopsis taxiformis, en rødalge som produserer et stort utvalg av metananaloger, har bevist sitt potensiale som en metaninhibator. Målet med denne oppgaven var å videre evaluere mitigeringspotensialet til Asparagopsis taxiformis supplert til melkekyr av rasen Norsk rødt fe, inkludert mulige effekter på vomfermentering og laktasjonsytelse. Denne studien bekrefter et doseavhengig reduserende potensial for Aspragopsis taxiformis, der metanproduksjonen (g/dag) hos melkekyr fôret med 0,25 % Aspragopsis taxiformis basert på organisk materiale ble signifikant redusert med 22 % (P = 0,037). Inkludering av Asparagopsis taxiformis i dietten påvirket også fôrets smak og reduserte tørrstoffinntaket betydelig (P < 0,001) samt melkemengde (P < 0,001). Kyr fôret med Aspragopsis taxiformis hadde en signifikant reduksjon i forholdet av produsert vomacetat og propionat (P = 0,028), i tillegg vistes en signifikant (P = 0,026) økning i innholdet av melkelaktose (%). Samtidig som det ble oppnådd en metanreduserende effekt av Asparagopsis taxiformis, kreves det ytterligere arbeid for å vurdere mulige skadelige og langsiktige effekter på dyrehelse og produktivitet.M-H

Diverse Target and Contribution Scheduling for Domain Generalization

Generalization under the distribution shift has been a great challenge in computer vision. The prevailing practice of directly employing the one-hot labels as the training targets in domain generalization~(DG) can lead to gradient conflicts, making it insufficient for capturing the intrinsic class characteristics and hard to increase the intra-class variation. Besides, existing methods in DG mostly overlook the distinct contributions of source (seen) domains, resulting in uneven learning from these domains. To address these issues, we firstly present a theoretical and empirical analysis of the existence of gradient conflicts in DG, unveiling the previously unexplored relationship between distribution shifts and gradient conflicts during the optimization process. In this paper, we present a novel perspective of DG from the empirical source domain's risk and propose a new paradigm for DG called Diverse Target and Contribution Scheduling (DTCS). DTCS comprises two innovative modules: Diverse Target Supervision (DTS) and Diverse Contribution Balance (DCB), with the aim of addressing the limitations associated with the common utilization of one-hot labels and equal contributions for source domains in DG. In specific, DTS employs distinct soft labels as training targets to account for various feature distributions across domains and thereby mitigates the gradient conflicts, and DCB dynamically balances the contributions of source domains by ensuring a fair decline in losses of different source domains. Extensive experiments with analysis on four benchmark datasets show that the proposed method achieves a competitive performance in comparison with the state-of-the-art approaches, demonstrating the effectiveness and advantages of the proposed DTCS

RePOR: Mimicking humans on refactoring tasks. Are we there yet?

Refactoring is a maintenance activity that aims to improve design quality while preserving the behavior of a system. Several (semi)automated approaches have been proposed to support developers in this maintenance activity, based on the correction of anti-patterns, which are `poor' solutions to recurring design problems. However, little quantitative evidence exists about the impact of automatically refactored code on program comprehension, and in which context automated refactoring can be as effective as manual refactoring. Leveraging RePOR, an automated refactoring approach based on partial order reduction techniques, we performed an empirical study to investigate whether automated refactoring code structure affects the understandability of systems during comprehension tasks. (1) We surveyed 80 developers, asking them to identify from a set of 20 refactoring changes if they were generated by developers or by a tool, and to rate the refactoring changes according to their design quality; (2) we asked 30 developers to complete code comprehension tasks on 10 systems that were refactored by either a freelancer or an automated refactoring tool. To make comparison fair, for a subset of refactoring actions that introduce new code entities, only synthetic identifiers were presented to practitioners. We measured developers' performance using the NASA task load index for their effort, the time that they spent performing the tasks, and their percentages of correct answers. Our findings, despite current technology limitations, show that it is reasonable to expect a refactoring tools to match developer code

ATTA: Anomaly-aware Test-Time Adaptation for Out-of-Distribution Detection in Segmentation

Recent advancements in dense out-of-distribution (OOD) detection have primarily focused on scenarios where the training and testing datasets share a similar domain, with the assumption that no domain shift exists between them. However, in real-world situations, domain shift often exits and significantly affects the accuracy of existing out-of-distribution (OOD) detection models. In this work, we propose a dual-level OOD detection framework to handle domain shift and semantic shift jointly. The first level distinguishes whether domain shift exists in the image by leveraging global low-level features, while the second level identifies pixels with semantic shift by utilizing dense high-level feature maps. In this way, we can selectively adapt the model to unseen domains as well as enhance model's capacity in detecting novel classes. We validate the efficacy of our proposed method on several OOD segmentation benchmarks, including those with significant domain shifts and those without, observing consistent performance improvements across various baseline models. Code is available at ${\href{https://github.com/gaozhitong/ATTA}{https://github.com/gaozhitong/ATTA}}$.Comment: Published in NeurIPS 202