Miracle-dz/UniFAR-FTUs huggingface dataset:
Decoders Matter for Semantic Segmentation: Data-Dependent Decoding Enables Flexible Feature Aggregation

Recent semantic segmentation methods exploit encoder-decoder architectures to produce the desired pixel-wise segmentation prediction. The last layer of the decoders is typically a bilinear upsampling procedure to recover the final pixel-wise prediction. We empirically show that this oversimple and data-independent bilinear upsampling may lead to sub-optimal results. In this work, we propose a data-dependent upsampling (DUpsampling) to replace bilinear, which takes advantages of the redundancy in the label space of semantic segmentation and is able to recover the pixel-wise prediction from low-resolution outputs of CNNs. The main advantage of the new upsampling layer lies in that with a relatively lower-resolution feature map such as 1/16 or 1/32 of the input size, we can achieve even better segmentation accuracy, significantly reducing computation complexity. This is made possible by 1) the new upsampling layer's much improved reconstruction capability; and more importantly 2) the DUpsampling based decoder's flexibility in leveraging almost arbitrary combinations of the CNN encoders' features. Experiments on PASCAL VOC demonstrate that with much less computation complexity, our decoder outperforms the state-of-the-art decoder. Finally, without any post-processing, the framework equipped with our proposed decoder achieves new state-of-the-art performance on two datasets: 88.1% mIOU on PASCAL VOC with 30% computation of the previously best model; and 52.5% mIOU on PASCAL Context.
Object Counting and Instance Segmentation With Image-Level Supervision

Common object counting in a natural scene is a challenging problem in computer vision with numerous real-world applications. Existing image-level supervised common object counting approaches only predict the global object count and rely on additional instance-level supervision to also determine object locations. We propose an image-level supervised approach that provides both the global object count and the spatial distribution of object instances by constructing an object category density map. Motivated by psychological studies, we further reduce image-level supervision using a limited object count information (up to four). To the best of our knowledge, we are the first to propose image-level supervised density map estimation for common object counting and demonstrate its effectiveness in image-level supervised instance segmentation. Comprehensive experiments are performed on the PASCAL VOC and COCO datasets. Our approach outperforms existing methods, including those using instance-level supervision, on both datasets for common object counting. Moreover, our approach improves state-of-the-art image-level supervised instance segmentation with a relative gain of 17.8% in terms of average best overlap, on the PASCAL VOC 2012 dataset.
An Additional Set of (Automated) Eyes: Chatbots for Agile Retrospectives

Recent advances in natural-language processing and data analysis allow software bots to become virtual team members, providing an additional set of automated eyes and additional perspectives for informing and supporting teamwork. In this paper, we propose employing chatbots in the domain of software development with a focus on supporting analyses and measurements of teams' project data. The software project artifacts produced by agile teams during regular development activities, e.g. commits in a version control system, represent detailed information on how a team works and collaborates. Analyses of this data are especially relevant for agile retrospective meetings, where adaptations and improvements to the executed development process are discussed. Development teams can use these measurements to track the progress of identified improvement actions over development iterations. Chatbots provide a convenient user interface for interacting with the outcomes of retrospectives and the associated measurements in a chat-based channel that is already being employed by team members.
Defining Image Memorability Using the Visual Memory Schema

Memorability of an image is a characteristic determined by the human observers’ ability to remember images they have seen. Yet recent work on image memorability defines it as an intrinsic property that can be obtained independent of the observer. The current study aims to enhance our understanding and prediction of image memorability, improving upon existing approaches by incorporating the properties of cumulative human annotations. We propose a new concept called the Visual Memory Schema (VMS) referring to an organization of image components human observers share when encoding and recognizing images. The concept of VMS is operationalised by asking human observers to define memorable regions of images they were asked to remember during an episodic memory test. We then statistically assess the consistency of VMSs across observers for either correctly or incorrectly recognised images. The associations of the VMSs with eye fixations and saliency are analysed separately as well. Lastly, we adapt various deep learning architectures for the reconstruction and prediction of memorable regions in images and analyse the results when using transfer learning at the outputs of different convolutional network layers.
Efficient Multi-Objective Optimization through Population-based Parallel Surrogate Search

Multi-Objective Optimization (MOO) is very difficult for expensive functions because most current MOO methods rely on a large number of function evaluations to get an accurate solution. We address this problem with surrogate approximation and parallel computation. We develop an MOO algorithm MOPLS-N for expensive functions that combines iteratively updated surrogate approximations of the objective functions with a structure for efficiently selecting a population of $N$ points so that the expensive objectives for all points are simultaneously evaluated on $N$ processors in each iteration. MOPLS incorporates Radial Basis Function (RBF) approximation, Tabu Search and local candidate search around multiple points to strike a balance between exploration, exploitation and diversification during each algorithm iteration. Eleven test problems (with 8 to 24 decision variables and two real-world watershed problems are used to compare performance of MOPLS to ParEGO, GOMORS, Borg, MOEA/D, and NSGA-III on a limited budget of evaluations with between 1 (serial) and 64 processors. MOPLS in serial is better than all non-RBF serial methods tested. Parallel speedup of MOPLS is higher than all other parallel algorithms with 16 and 64 processors. With both algorithms on 64 processors MOPLS is at least 2 times faster than NSGA-III on the watershed problems.
Abnormal Chest X-Ray Identification With Generative Adversarial One-Class Classifier

Being one of the most common diagnostic imaging tests, chest radiography requires timely reporting of potential findings in the images. In this paper, we propose an end-to-end architecture for abnormal chest X-ray identification using generative adversarial one-class learning. Unlike previous approaches, our method takes only normal chest X-ray images as input. The architecture is composed of three deep neural networks, each of which learned by competing while collaborating among them to model the underlying content structure of the normal chest X-rays. Given a chest X-ray image in the testing phase, if it is normal, the learned architecture can well model and reconstruct the content; if it is abnormal, since the content is unseen in the training phase, the model would perform poorly in its reconstruction. It thus enables distinguishing abnormal chest X-rays from normal ones. Quantitative and qualitative experiments demonstrate the effectiveness and efficiency of our approach, where an AUC of 0.841 is achieved on the challenging NIH Chest X-ray dataset in a one-class learning setting, with the potential in reducing the workload for radiologists.
Optimizing Subgraph Queries by Combining Binary and Worst-Case Optimal Joins

We study the problem of optimizing subgraph queries using the new worst-case optimal join plans. Worst-case optimal plans evaluate queries by matching one query vertex at a time using multi-way intersections. The core problem in optimizing worst-case optimal plans is to pick an ordering of the query vertices to match. We design a cost-based optimizer that (i) picks efficient query vertex orderings for worst-case optimal plans; and (ii) generates hybrid plans that mix traditional binary joins with worst-case optimal style multiway intersections. Our cost metric combines the cost of binary joins with a new cost metric called intersection-cost . The plan space of our optimizer contains plans that are not in the plan spaces based on tree decompositions from prior work. In addition to our optimizer, we describe an adaptive technique that changes the orderings of the worst-case optimal subplans during query execution. We demonstrate the effectiveness of the plans our optimizer picks and the effectiveness of the adaptive technique through extensive experiments. Our optimizer is integrated into the Graphflow DBMS.
Open-Sourced Reinforcement Learning Environments for Surgical Robotics

Reinforcement Learning (RL) is a machine learning framework for artificially intelligent systems to solve a variety of complex problems. Recent years has seen a surge of successes solving challenging games and smaller domain problems, including simple though non-specific robotic manipulation and grasping tasks. Rapid successes in RL have come in part due to the strong collaborative effort by the RL community to work on common, open-sourced environment simulators such as OpenAI's Gym that allow for expedited development and valid comparisons between different, state-of-art strategies. In this paper, we aim to bridge the RL and the surgical robotics communities by presenting the first open-sourced reinforcement learning environments for surgical robotics, called dVRL. Through the proposed RL environment, which are functionally equivalent to Gym, we show that it is easy to prototype and implement state-of-art RL algorithms on surgical robotics problems that aim to introduce autonomous robotic precision and accuracy to assisting, collaborative, or repetitive tasks during surgery. Learned policies are furthermore successfully transferable to a real robot. Finally, combining dVRL with the over 40+ international network of da Vinci Surgical Research Kits in active use at academic institutions, we see dVRL as enabling the broad surgical robotics community to fully leverage the newest strategies in reinforcement learning, and for reinforcement learning scientists with no knowledge of surgical robotics to test and develop new algorithms that can solve the real-world, high-impact challenges in autonomous surgery.
Hybrid LSTM and Encoder–Decoder Architecture for Detection of Image Forgeries

With advanced image journaling tools, one can easily alter the semantic meaning of an image by exploiting certain manipulation techniques such as copy clone, object splicing, and removal, which mislead the viewers. In contrast, the identification of these manipulations becomes a very challenging task as manipulated regions are not visually apparent. This paper proposes a high-confidence manipulation localization architecture that utilizes resampling features, long short-term memory (LSTM) cells, and an encoder–decoder network to segment out manipulated regions from non-manipulated ones. Resampling features are used to capture artifacts, such as JPEG quality loss, upsampling, downsampling, rotation, and shearing. The proposed network exploits larger receptive fields (spatial maps) and frequency-domain correlation to analyze the discriminative characteristics between the manipulated and non-manipulated regions by incorporating the encoder and LSTM network. Finally, the decoder network learns the mapping from low-resolution feature maps to pixel-wise predictions for image tamper localization. With the predicted mask provided by the final layer (softmax) of the proposed architecture, end-to-end training is performed to learn the network parameters through back-propagation using the ground-truth masks. Furthermore, a large image splicing dataset is introduced to guide the training process. The proposed method is capable of localizing image manipulations at the pixel level with high precision, which is demonstrated through rigorous experimentation on three diverse datasets.
How can a knowledge of microbiota-pathogen interactions in cereal cropping systems help us to manage residue-borne fungal diseases?

The epidemiological contribution of crop residues as a source of inoculum for fungal diseases on plants, especially in cereal cropping systems, is well established. However, microbial ecologists have long reported positive effects of crop residues on the stability and productivity of several agrosystems. For this reason, no-till practices have become increasingly widespread, leading to the expression of trade-offs, or even incompatibilities, between different agro-ecological challenges. In this context, we propose a literature-based reflection on how to bridge the gap between 'crop debris management' and 'enhancing the action of microbes as direct or indirect biological control agents'. Most studies focusing on the microbiota have suggested that microbial communities should be taken into account in plant disease management, but we still know much less about their ecological interaction with pathogens in the crop residue compartment than in the phyllosphere or rhizosphere. In this review, we provide an overview of what is currently known about the impact of residues on plant disease epidemics and describe how microbial interactions on these residues could be exploited to develop innovative crop protection strategies. Starting from the classical view of residues as a source of inoculum, we considered possibilities for the quantitative management of residues in crop protection. We describe residues as a transient half-plant/half-soil compartment constituting a key fully fledged microbial ecosystem. We focus on the changes in microbial communities over time and the factors influencing them. Finally, we discuss how these communities present on residues and their interactions with pathogens could be used: from the identification of beneficial groups of microorganisms naturally present to their preservation, rather than the introduction of exogenous biocontrol agents as a 'treatment'.
Investigation of the Proarrhythmic Effects of Antidepressants according to QT Interval, QT Dispersion and T Wave Peak-to-End Interval in the Clinical Setting

Objective Some antidepressants have been implicated as risk factors for QT prolongation, which is a predictor of sudden cardiac death. However, the QT interval is considered an imperfect biomarker for proarrhythmic risk. Therefore, we reevaluated the risk of sudden cardiac death due to antidepressants using improved methods, namely, QT dispersion (QTD), T wave peak-to-end interval (Tp-e), and Tp-e/QT ratio. Methods We compared the effects of antidepressants on QTc (QT/RR1/3), QTD, Tp-e, and Tp-e/QT ratio in 378 patients with mood disorder. We also compared each index between 165 healthy controls and 215 randomly selected age-matched patients. Results Age (p<0.01), sex (p<0.05), tricyclic antidepressant (TCA) use (p<0.05), and clomipramine (p<0.01) and mianserin (p<0.05) use in particular, significantly associated with a prolonged QTc. We also found that age (p<0.01), TCA use (p<0.05), and clomipramine (p<0.01) and mianserin (p<0.05) use in particular, significantly prolonged QTD. However, there was no correlation between each variable and Tp-e or Tp-e/QT ratio. Significant differences in QTc and QTD were found between the patients and healthy controls. Conclusion From our results, prediction of risk of sudden cardiac death by QTD, Tp-e, or Tp-e/QT ratio was inconsistent. Increased QTD may be more suitable for predicting sudden cardiac death due to antidepressants.
Paneth cell α-defensins HD-5 and HD-6 display differential degradation into active antimicrobial fragments

Significance Paneth cells provide intestinal host defense against pathogens and control the healthy microbiota by secreting antimicrobial peptides. We show that the most abundant secreted Paneth cell products, human defensin HD-5 and HD-6, show a distinct susceptibility to proteolytic digestion by human duodenal fluid. While HD-5 is digested in many fragments, HD-6 is stable and still able to form nanonets. The occurring fragments of HD-5 were antimicrobially active against microorganisms. We provide proof of concept about microbiome modulating capacities in vivo, which includes an increase of Akkermansia sp. Our results indicate that fragmentation of defensins increases antimicrobial diversity and further adds to the complexity of host microbial interaction at interfaces. Fragmentation could lead to new antimicrobial peptides with possible therapeutic usage. Antimicrobial peptides, in particular α-defensins expressed by Paneth cells, control microbiota composition and play a key role in intestinal barrier function and homeostasis. Dynamic conditions in the local microenvironment, such as pH and redox potential, significantly affect the antimicrobial spectrum. In contrast to oxidized peptides, some reduced defensins exhibit increased vulnerability to proteolytic degradation. In this report, we investigated the susceptibility of Paneth-cell–specific human α-defensin 5 (HD-5) and -6 (HD-6) to intestinal proteases using natural human duodenal fluid. We systematically assessed proteolytic degradation using liquid chromatography–mass spectrometry and identified several active defensin fragments capable of impacting bacterial growth of both commensal and pathogenic origins. Of note, incubation of mucus with HD-5 resulted in 255–8,000 new antimicrobial combinations. In contrast, HD-6 remained stable with consistent preserved nanonet formation. In vivo studies demonstrated proof of concept that a HD-5 fragment shifted microbiota composition (e.g., increases of Akkermansia sp.) without decreasing diversity. Our data support the concept that secretion of host peptides results in an environmentally dependent increase of antimicrobial defense by clustering in active peptide fragments. This complex clustering mechanism dramatically increases the host’s ability to control pathogens and commensals. These findings broaden our understanding of host modulation of the microbiome as well as the complexity of human mucosal defense mechanisms, thus providing promising avenues to explore for drug development.
Redox traits characterize the organization of global microbial communities

Significance Biological communities are conventionally described as assemblages of species, whose ecological roles are known or predictable from their observable morphology. In microbial ecology, such a taxonomic approach is hindered by limited capacity to discriminate among different microbes, which bear highly dynamic genomes and establish complex associations. Approaches based on culture-independent functional genes profiling might overcome these problems, but a set of usable established genes in a general situation is still lacking. We show that genes related to reduction-oxidation (redox) processes separate microbial communities into their corresponding biomes. This redox-based characterization is linked to the microbial energetics of ecosystems and to most biogeochemical cycles and might be useful for assessing the impact of environmental degradation on the ecosystem services, underpinned by microorganisms. The structure of biological communities is conventionally described as profiles of taxonomic units, whose ecological functions are assumed to be known or, at least, predictable. In environmental microbiology, however, the functions of a majority of microorganisms are unknown and expected to be highly dynamic and collectively redundant, obscuring the link between taxonomic structure and ecosystem functioning. Although genetic trait-based approaches at the community level might overcome this problem, no obvious choice of gene categories can be identified as appropriate descriptive units in a general ecological context. We used 247 microbial metagenomes from 18 biomes to determine which set of genes better characterizes the differences among biomes on the global scale. We show that profiles of oxidoreductase genes support the highest biome differentiation compared with profiles of other categories of enzymes, general protein-coding genes, transporter genes, and taxonomic gene markers. Based on oxidoreductases’ description of microbial communities, the role of energetics in differentiation and particular ecosystem function of different biomes become readily apparent. We also show that taxonomic diversity is decoupled from functional diversity, e.g., grasslands and rhizospheres were the most diverse biomes in oxidoreductases but not in taxonomy. Considering that microbes underpin biogeochemical processes and nutrient recycling through oxidoreductases, this functional diversity should be relevant for a better understanding of the stability and conservation of biomes. Consequently, this approach might help to quantify the impact of environmental stressors on microbial ecosystems in the context of the global-scale biome crisis that our planet currently faces.
Smartphone Addiction in Japanese College Students: Usefulness of the Japanese Version of the Smartphone Addiction Scale as a Screening Tool for a New Form of Internet Addiction

Objective Smartphone use is pervasive among youth in Japan, as with many other countries, and is associated with spending time online and on social media anywhere at any time. This study aimed to test a Japanese version of the Smartphone Addiction Scale-Short Version (SAS-SV) among Japanese college students. Methods The subjects of this study were 602 college students in Japan. The study questionnaire consisted of questions about demographics (age, gender etc.), possession of a smartphone, internet use [length of internet use on weekdays and weekend, favorite social networking service (SNS) etc.], Young’s Internet Addiction Test (IAT), and the Smartphone Addiction Scale-Short Version (SAS-SV) translated into Japanese. Results There was a total of 573 respondents (180 male, 393 female) who completed the questionnaire (mean 19.3±1.3 years). LINE was the most popular social media platform (52.0%) followed by Twitter (36.3%). The overall Internet Addiction Test (IAT) score was 45.3±13.2, with 4.5% classified as having severe addiction (IAT ≥70). The mean SAS-SV scores were 24.4±10.0 for males and 26.8±9.9 for females. Based on proposed cutoff scores, 22.8% of males and 28.0% of females screened positive for smartphone addiction. The total scores of the SAS-SV and the IAT was correlated significantly. Conclusion As the number of smartphone users becomes higher, problems related to smartphone use also become more serious. Our results suggest that the Japanese version of SAS-SV may assist in early detection of problematic use of smartphones.