Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast R-CNN

This paper proposes Fast R-CNN, a clean and fast framework for object detection. Compared to traditional R-CNN, and its accelerated version SPPnet, Fast R-CNN trains networks using a multi-task loss in a single training stage. The multi-task loss simplifies learning and improves detection accuracy. Unlike SPPnet, all network layers can be updated during fine-tuning. We show that this difference has practical ramifications for very deep networks, such as VGG16, where mAP suffers when only the fully-connected layers are updated. Compared to"slow"R-CNN, Fast R-CNN is 9x faster at training VGG16 for detection, 213x faster at test-time, and achieves a significantly higher mAP on PASCAL VOC 2012. Compared to SPPnet, Fast R-CNN trains VGG16 3x faster, tests 10x faster, and is more accurate. Fast R-CNN is implemented in Python and C++ and is available under the open-source MIT License at https://github.com/rbgirshick/fast-rcnn
Learning a Descriptor-Specific 3D Keypoint Detector

Keypoint detection represents the first stage in the majority of modern computer vision pipelines based on automatically established correspondences between local descriptors. However, no standard solution has emerged yet in the case of 3D data such as point clouds or meshes, which exhibit high variability in level of detail and noise. More importantly, existing proposals for 3D keypoint detection rely on geometric saliency functions that attempt to maximize repeatability rather than distinctiveness of the selected regions, which may lead to sub-optimal performance of the overall pipeline. To overcome these shortcomings, we cast 3D keypoint detection as a binary classification between points whose support can be correctly matched by a predefined 3D descriptor or not, thereby learning a descriptor-specific detector that adapts seamlessly to different scenarios. Through experiments on several public datasets, we show that this novel approach to the design of a keypoint detector represents a flexible solution that, nonetheless, can provide state-of-the-art descriptor matching performance.
The relationship between complete blood count parameters and Fontaine’s Stages in patients with peripheral arterial disease

Objective The aim of the present study is to evaluate whether blood count parameters differ according to the stages of Fontaine’s classification and to investigate the relationship between hemogram parameters and the severity of the disease. Method Eighty-two peripheral arterial disease patients were examined prospectively. Patients were classified according to the Fontaine classification system. Fifty newly diagnosed patients were included in the study. The neutrophil-to-lymphocyte ratio, mean platelet volume, and red blood cell distribution width values were recorded. Results Mean neutrophil-to-lymphocyte ratio values were found to be 3.31 ± 1.1% in Stage I, 3.11 ± 1.3% in Stage II, and 3.48 ± 1.1% in Stage III (p > 0.05). Mean platelet volume values were found to be 7.8 ± 0.6 fl (Stage I), 8.2 ± 1.0 fl (Stage II), and 9.0 ± 0.9 fl (Stage III) (p < 0.05). Red blood cell distribution width values were found to be 13.6 ± 1.0% in Stage I, 14.8 ± 1.7% in Stage II, and 15.4 ± 2.3% in Stage III, being significantly different among all three stages (p < 0.05). Conclusion Both red blood cell distribution width and mean platelet volume are found to be associated with the severity of atherosclerotic disease in patients with peripheral arterial disease. This finding hypothesizes that complete blood counting parameters may serve as a beneficial and cost-effective method for monitoring atherosclerotic peripheral disease.
Locating Mobile Agents in a Wide Distributed Environment

Finding the position of a mobile agent in a wide distributed system still represents an open research issue. The paper proposes a naming scheme and a location protocol of general validity for mobile agents able to effectively meet all the typical requirements of mobile agent environments and, thus, easy to integrate into different platforms. The paper identifies the main characteristics which an agent naming scheme and a location protocol of general validity should have, and suggests some properties and parameters to be taken into account to evaluate the effectiveness of naming schemes and location protocols. Then, we propose a "human readable" agent naming scheme based on the distributed environment outlined in MASIF, and a suitable location finding protocol called the Search-By-Path-Chase. Both of them are compared with some of the solutions already provided, using the properties and the parameters suggested. The performances are finally evaluated by means of a set of measurements.
An Application-Centric Characterization of Domain-Based SFC Partitioners for Parallel SAMR

Structured adaptive mesh refinement (SAMR) methods for the numerical solution of partial differential equations yield highly advantageous ratios for cost/accuracy as compared to methods based on static uniform approximations. These techniques are being effectively used in many domains including computational fluid dynamics, numerical relativity, astrophysics, subsurface modeling, and oil reservoir simulation. Distributed implementations of these methods, however, lead to significant challenges in dynamic data-distribution, load-balancing, and runtime management. This paper presents an application-centric characterization of a suite of dynamic domain-based inverse space-filling curve partitioning techniques for the distributed adaptive grid hierarchies that underlie SAMR applications. The overall goal of this research is to formulate policies required to drive a dynamically adaptive metapartitioner for SAMR grid hierarchies capable of selecting the most appropriate partitioning strategy at runtime based on current application and system state. Such a metapartitioner can significantly reduce the execution time of SAMR applications.
The Iron Stimulon of Xylella fastidiosa Includes Genes for Type IV Pilus and Colicin V-Like Bacteriocins

ABSTRACT Xylella fastidiosa is the etiologic agent of a wide range of plant diseases, including citrus variegated chlorosis (CVC), a major threat to citrus industry. The genomes of several strains of this phytopathogen were completely sequenced, enabling large-scale functional studies. DNA microarrays representing 2,608 (91.6%) coding sequences (CDS) of X. fastidiosa CVC strain 9a5c were used to investigate transcript levels during growth with different iron availabilities. When treated with the iron chelator 2,2′-dipyridyl, 193 CDS were considered up-regulated and 216 were considered down-regulated. Upon incubation with 100 μM ferric pyrophosphate, 218 and 256 CDS were considered up- and down-regulated, respectively. Differential expression for a subset of 44 CDS was further evaluated by reverse transcription-quantitative PCR. Several CDS involved with regulatory functions, pathogenicity, and cell structure were modulated under both conditions assayed, suggesting that major changes in cell architecture and metabolism occur when X. fastidiosa cells are exposed to extreme variations in iron concentration. Interestingly, the modulated CDS include those related to colicin V-like bacteriocin synthesis and secretion and to functions of pili/fimbriae. We also investigated the contribution of the ferric uptake regulator Fur to the iron stimulon of X. fastidiosa. The promoter regions of the strain 9a5c genome were screened for putative Fur boxes, and candidates were analyzed by electrophoretic mobility shift assays. Taken together, our data support the hypothesis that Fur is not solely responsible for the modulation of the iron stimulon of X. fastidiosa, and they present novel evidence for iron regulation of pathogenicity determinants.
HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition

In image classification, visual separability between different object categories is highly uneven, and some categories are more difficult to distinguish than others. Such difficult categories demand more dedicated classifiers. However, existing deep convolutional neural networks (CNN) are trained as flat N-way classifiers, and few efforts have been made to leverage the hierarchical structure of categories. In this paper, we introduce hierarchical deep CNNs (HD-CNNs) by embedding deep CNNs into a two-level category hierarchy. An HD-CNN separates easy classes using a coarse category classifier while distinguishing difficult classes using fine category classifiers. During HDCNN training, component-wise pretraining is followed by global fine-tuning with a multinomial logistic loss regularized by a coarse category consistency term. In addition, conditional executions of fine category classifiers and layer parameter compression make HD-CNNs scalable for largescale visual recognition. We achieve state-of-the-art results on both CIFAR100 and large-scale ImageNet 1000-class benchmark datasets. In our experiments, we build up three different two-level HD-CNNs, and they lower the top-1 error of the standard CNNs by 2:65%, 3:1%, and 1:1%.
Chemoreceptors and Flagellar Motors Are Subterminally Located in Close Proximity at the Two Cell Poles in Spirochetes

ABSTRACT Green fluorescent protein (GFP) fusions, immunofluorescence microscopy, and cryo-electron tomography revealed that the chemoreceptors of the Lyme disease spirochete Borrelia burgdorferi form long, thin arrays near both cell poles. These arrays are in close proximity to the flagellar motors. This information provides a basis for further understanding motility, chemotaxis, and protein localization in spirochetes.
Increased mRNA expression of cytochrome oxidase in dorsal raphe nucleus of depressive suicide victims

Suicidal behavior is a problem with important social repercussions. Some groups of the population show a higher risk of suicide; for example, depression, alcoholism, psychosis or drug abuse frequently precedes suicidal behavior. However, the relationship between metabolic alterations in the brain and premorbid clinical symptoms of suicide remains uncertain. The serotonergic and noradrenergic systems have frequently been, implicated in suicidal behavior and the amount of serotonin in the brain and CSF of suicide victims has been found to be low compared with normal subjects. However, there are contradictory results regarding the role of noradrenergic neurons in the mediation of suicide attempts, possibly reflecting the heterogeneity of conditions that lead to a common outcome. In the present work we focus on the subgroup of suicide victims that share a common diagnosis of major depression. Based on post-mortem studies analyzing mRNA expression by in situ hybridization, serotonergic neurons from the dorsal raphe nucleus (DRN) from depressive suicide victims are seen to over-express cytochrome oxidase mRNA. However, no corresponding changes were found in the expression of tyrosine hydroxylase (TH) mRNA in the noradrenergic neurons of the Locus Coeruleus (LC). These results suggest that, despite of the low levels of serotonin described in suicide victims, the activity of DRN neurons could increase in the suicidally depressed, probably due to the over activation of serotonin re-uptake. No alteration was found in noradrenergic neurons, suggesting that they play no crucial role in the suicidal behavior of depressive patients.
Inactivation of Ribosomal Protein Genes in Bacillus subtilis Reveals Importance of Each Ribosomal Protein for Cell Proliferation and Cell Differentiation

ABSTRACT Among the 57 genes that encode ribosomal proteins in the genome of Bacillus subtilis, a Gram-positive bacterium, 50 genes were targeted by systematic inactivation. Individual deletion mutants of 16 ribosomal proteins (L1, L9, L15, L22, L23, L28, L29, L32, L33.1, L33.2, L34, L35, L36, S6, S20, and S21) were obtained successfully. In conjunction with previous reports, 22 ribosomal proteins have been shown to be nonessential in B. subtilis, at least for cell proliferation. Although several mutants that harbored a deletion of a ribosomal protein gene did not show any significant differences in any of the phenotypes that were tested, various mutants showed a reduced growth rate and reduced levels of 70S ribosomes compared with the wild type. In addition, severe defects in the sporulation frequency of the ΔrplA (L1) mutant and the motility of the ΔrpsU (S21) mutant were observed. These data provide the first evidence in B. subtilis that L1 and S21 are required for the progression of cellular differentiation.
Endovenous ablation of incompetent truncal veins and their perforators with a new radiofrequency system. Mid-term outcomes

This is a prospective trial investigating endovenous radiofrequency ablation with the EVRF® system for the treatment of symptomatic varicose veins. Primary endpoints include one-year anatomical and clinical success and procedure-related complications. Secondary endpoints include adjunctive procedures and recanalization rates, periprocedural pain assessment, and time return to normal activities. In 60 patients with 74 limbs, 58 great saphenous vein, 11 small saphenous vein, 2 anterior accessory saphenous vein, and 3 perforators were ablated. Additional ablations for further improvement were necessary in 28.4%. Clinical success was 94.6%. Anatomical success was 96.0% at one month and 89.2% at one year. Primary ablation success was 77%. Revascularization occurred in 12.1%. Clinically driven repeat ablation rate was 4.0%. Perforator ablation due to segmental revascularization was performed in 5.4%. Complications included one puncture-site infection, three scars, two cases of transient paresthesia, and one skin pigmentation. Periprocedural mean pain score was 2.4 ± 2.6. In 27.0% cases, the patients used analgesics and mean time return was 1.2 ± 0.5 days. The EVRF® system yields satisfactory clinical and anatomical midterm outcomes with very low complication rates.
Learning Visual Clothing Style with Heterogeneous Dyadic Co-Occurrences

With the rapid proliferation of smart mobile devices, users now take millions of photos every day. These include large numbers of clothing and accessory images. We would like to answer questions like 'What outfit goes well with this pair of shoes? ' To answer these types of questions, one has to go beyond learning visual similarity and learn a visual notion of compatibility across categories. In this paper, we propose a novel learning framework to help answer these types of questions. The main idea of this framework is to learn a feature transformation from images of items into a latent space that expresses compatibility. For the feature transformation, we use a Siamese Convolutional Neural Network (CNN) architecture, where training examples are pairs of items that are either compatible or incompatible. We model compatibility based on co-occurrence in large-scale user behavior data, in particular co-purchase data from Amazon.com. To learn cross-category fit, we introduce a strategic method to sample training data, where pairs of items are heterogeneous dyads, i.e., the two elements of a pair belong to different high-level categories. While this approach is applicable to a wide variety of settings, we focus on the representative problem of learning compatible clothing style. Our results indicate that the proposed framework is capable of learning semantic information about visual style and is able to generate outfits of clothes, with items from different categories, that go well together.
Di-Adenosine Tetraphosphate (Ap4A) Metabolism Impacts Biofilm Formation by Pseudomonas fluorescens via Modulation of c-di-GMP-Dependent Pathways

ABSTRACT Dinucleoside tetraphosphates are common constituents of the cell and are thought to play diverse biological roles in organisms ranging from bacteria to humans. In this study we characterized two independent mechanisms by which di-adenosine tetraphosphate (Ap4A) metabolism impacts biofilm formation by Pseudomonas fluorescens. Null mutations in apaH, the gene encoding nucleoside tetraphosphate hydrolase, resulted in a marked increase in the cellular level of Ap4A. Concomitant with this increase, Pho regulon activation in low-inorganic-phosphate (Pi) conditions was severely compromised. As a consequence, an apaH mutant was not sensitive to Pho regulon-dependent inhibition of biofilm formation. In addition, we characterized a Pho-independent role for Ap4A metabolism in regulation of biofilm formation. In Pi-replete conditions Ap4A metabolism was found to impact expression and localization of LapA, the major adhesin regulating surface commitment by P. fluorescens. Increases in the level of c-di-GMP in the apaH mutant provided a likely explanation for increased localization of LapA to the outer membrane in response to elevated Ap4A concentrations. Increased levels of c-di-GMP in the apaH mutant were associated with increases in the level of GTP, suggesting that elevated levels of Ap4A may promote de novo purine biosynthesis. In support of this suggestion, supplementation with adenine could partially suppress the biofilm and c-di-GMP phenotypes of the apaH mutant. We hypothesize that changes in the substrate (GTP) concentration mediated by altered flux through nucleotide biosynthetic pathways may be a significant point of regulation for c-di-GMP biosynthesis and regulation of biofilm formation.
Adversarial PoseNet: A Structure-Aware Convolutional Network for Human Pose Estimation

For human pose estimation in monocular images, joint occlusions and overlapping upon human bodies often result in deviated pose predictions. Under these circumstances, biologically implausible pose predictions may be produced. In contrast, human vision is able to predict poses by exploiting geometric constraints of joint inter-connectivity. To address the problem by incorporating priors about the structure of human bodies, we propose a novel structure-aware convolutional network to implicitly take such priors into account during training of the deep network. Explicit learning of such constraints is typically challenging. Instead, we design discriminators to distinguish the real poses from the fake ones (such as biologically implausible ones). If the pose generator (G) generates results that the discriminator fails to distinguish from real ones, the network successfully learns the priors. ,,To better capture the structure dependency of human body joints, the generator G is designed in a stacked multi-task manner to predict poses as well as occlusion heatmaps. Then, the pose and occlusion heatmaps are sent to the discriminators to predict the likelihood of the pose being real. Training of the network follows the strategy of conditional Generative Adversarial Networks (GANs). The effectiveness of the proposed network is evaluated on two widely used human pose estimation benchmark datasets. Our approach significantly outperforms the state-of-the-art methods and almost always generates plausible human pose predictions.
Modeling Propagation Dynamics of Social Network Worms

Social network worms, such as email worms and facebook worms, pose a critical security threat to the Internet. Modeling their propagation dynamics is essential to predict their potential damages and develop countermeasures. Although several analytical models have been proposed for modeling propagation dynamics of social network worms, there are two critical problems unsolved: temporal dynamics and spatial dependence. First, previous models have not taken into account the different time periods of Internet users checking emails or social messages, namely, temporal dynamics. Second, the problem of spatial dependence results from the improper assumption that the states of neighboring nodes are independent. These two problems seriously affect the accuracy of the previous analytical models. To address these two problems, we propose a novel analytical model. This model implements a spatial-temporal synchronization process, which is able to capture the temporal dynamics. Additionally, we find the essence of spatial dependence is the spreading cycles. By eliminating the effect of these cycles, our model overcomes the computational challenge of spatial dependence and provides a stronger approximation to the propagation dynamics. To evaluate our susceptible-infectious-immunized (SII) model, we conduct both theoretical analysis and extensive simulations. Compared with previous epidemic models and the spatial-temporal model, the experimental results show our SII model achieves a greater accuracy. We also compare our model with the susceptible-infectious-susceptible and susceptible-infectious-recovered models. The results show that our model is more suitable for modeling the propagation of social network worms.
Genetic CNN

The deep convolutional neural network (CNN) is the state-of-the-art solution for large-scale visual recognition. Following some basic principles such as increasing network depth and constructing highway connections, researchers have manually designed a lot of fixed network architectures and verified their effectiveness. ,,In this paper, we discuss the possibility of learning deep network structures automatically. Note that the number of possible network structures increases exponentially with the number of layers in the network, which motivates us to adopt the genetic algorithm to efficiently explore this large search space. The core idea is to propose an encoding method to represent each network structure in a fixed-length binary string. The genetic algorithm is initialized by generating a set of randomized individuals. In each generation, we define standard genetic operations, e.g., selection, mutation and crossover, to generate competitive individuals and eliminate weak ones. The competitiveness of each individual is defined as its recognition accuracy, which is obtained via a standalone training process on a reference dataset. We run the genetic process on CIFAR10, a small-scale dataset, demonstrating its ability to find high-quality structures which are little studied before. The learned powerful structures are also transferrable to the ILSVRC2012 dataset for large-scale visual recognition.
Using Vignettes to Assist Reflection within an Action Research Study on a Preoperative Education Programme

This paper describes an exploratory use of vignettes in action research. The study was with a group of health care professionals, who were working together to improve their preoperative education programme for patients awaiting a total hip replacement. One vignette, written after the observation of an education programme, is presented to illustrate how the health care professionals used it within an action research cycle in order to reflect on practice and then to implement changes. These changes were mostly to the programme organisation and in preparing the venue for the education. The vignette enabled the health care professionals to appreciate a different perspective to their own lived experience. The value of vignettes used in this way is recognised in this paper because they facilitated the health care professionals' reflections within the action research process.