Miracle-dz/UniFAR-FTUs huggingface dataset:
VulSeeker: A Semantic Learning Based Vulnerability Seeker for Cross-Platform Binary

Code reuse improves software development efficiency, however, vulnerabilities can be introduced inadvertently. Many existing works compute the code similarity based on CFGs to determine whether a binary function contains a known vulnerability. Unfortunately, their performance in cross-platform binary search is challenged. This paper presents VulSeeker, a semantic learning based vulnerability seeker for cross-platform binary. Given a target function and a vulnerable function, VulSeeker first constructs the labeled semantic flow graphs and extracts basic block features as numerical vectors for both of them. Then the embedding vector of the whole binary function is generated by feeding the numerical vectors of basic blocks to the customized semantics aware DNN model. Finally, the similarity of the two binary functions is measured based on the Cosine distance. The experimental results show that VulSeeker outperforms the state-of-the-art approaches in terms of accuracy. For example, compared to the most recent and related work Gemini, VulSeeker finds 50.00% more vulnerabilities in the top-10 candidates and 13.89% more in the top-50 candidates, and improves the values of AUC and ACC for 8.23% and 12.14% respectively. The video is presented at https://youtu.be/Mw0mr84gpI8.
Are rural residents missing out on teaching? A comparison of teaching opportunities for urban and rural family medicine residents at the University of Manitoba

Background The scholar competency of the CanMEDS-FM framework requires residents to develop teaching skills, and with the number of rural residency positions tripling over the last decade, it is essential for residency programs to provide rural residents with teaching opportunities. The purpose of this study was to investigate the differences in teaching opportunities offered to urban and rural family medicine residents at the University of Manitoba. Methods The 117 urban and rural family medicine residents were surveyed to quantify their interactions with medical students. Specific respondents were interviewed to provide context to the survey. Results On family medicine, only 20% of rural residents reported frequent opportunities to informally teach, compared to 57% of urban residents. Similarly, 86% of urban residents reported organized teaching opportunities compared to only 5% of rural residents. Residents placed high value in receiving additional teaching opportunities. Conclusion This study suggests that there are fewer teaching opportunities for rural family medicine residents compared to urban residents at the University of Manitoba. Given the small sample size, a larger study could determine whether this trend exists across the country. It will be incumbent on residency programs to ensure rural residents have the opportunities to become competent educators.
A Process for Reverse Engineering of Aggregate-Oriented NoSQL Databases with Emphasis on Geographic Data

One main advantage of NoSQL DBs is having no schemas, or allowing ﬂexible ones. Such feature is suitable for various application domains, including social networks, Web applications, and Internet of Things. However, there is a growing interest in the management of NoSQL DB schemas, e.g., for tasks such as DB integration. This work proposes a process for the schema extracting from aggregate-oriented NoSQL DB (with possible geographic data), and the production of RDF views from the created JSON Schemas. To our knowledge, there are no related works that propose such a comprehensive approach and that deal with geographic NoSQL data. We analyze ten related works published in the last years, which present different proposals for NoSQL schema extraction, but few of them focus on the schema integration, demonstrating that there is still much to be done in this area. From this analysis, we present our proposal and some preliminary results.
Unmanned Aerial Vehicle Object Tracking by Correlation Filter with Adaptive Appearance Model

With the increasing availability of low-cost, commercially available unmanned aerial vehicles (UAVs), visual tracking using UAVs has become more and more important due to its many new applications, including automatic navigation, obstacle avoidance, traffic monitoring, search and rescue, etc. However, real-world aerial tracking poses many challenges due to platform motion and image instability, such as aspect ratio change, viewpoint change, fast motion, scale variation and so on. In this paper, an efficient object tracking method for UAV videos is proposed to tackle these challenges. We construct the fused features to capture the gradient information and color characteristics simultaneously. Furthermore, cellular automata is introduced to update the appearance template of target accurately and sparsely. In particular, a high confidence model updating strategy is developed according to the stability function. Systematic comparative evaluations performed on the popular UAV123 dataset show the efficiency of the proposed approach.
Functional brain network centrality is related to APOE genotype in cognitively normal elderly

Amyloid plaque deposition in the brain is an early pathological change in Alzheimer's disease (AD), causing disrupted synaptic connections. Brain network disruptions in AD have been demonstrated with eigenvector centrality (EC), a measure that identifies central regions within networks. Carrying an apolipoprotein (APOE)‐ε4 allele is a genetic risk for AD, associated with increased amyloid deposition. We studied whether APOE‐ε4 carriership is associated with EC disruptions in cognitively normal individuals.
Bioactive Prenyl- and Terpenyl-Quinones/Hydroquinones of Marine Origin †

The sea is a rich source of biological active compounds, among which terpenyl-quinones/hydroquinones constitute a family of secondary metabolites with diverse pharmacological properties. The chemical diversity and bioactivity of those isolated from marine organisms in the last 10 years are summarized in this review. Aspects related to synthetic approaches towards the preparation of improved bioactive analogues from inactive terpenoids are also outlined.
Traffic Sign Recognition Based on Learning Vector Quantization and Convolution Neural Network

This paper analyzes and studies the task of traffic sign recognition in the complex traffic road environment, analyzes the defects of the traditional classification algorithm and CNN (convolutional neural network) algorithm, cannot maximize the elimination of the lack of use of image disturbance information and hidden image information. This paper proposes a traffic sign recognition algorithm based on CNN, PCA (Principal Component Analysis) and LVQ (Learning Vector Quantization) algorithm, and uses PCA algorithm and LVQ algorithm to perform eigenvectors generated by CNN model. Dimensionality reduction and classification processing eliminates picture interference information and in-depth use of hidden information in pictures, further improves the accuracy of identification of traffic signs in complex environments, validates the effectiveness of the proposed method on the GTSRB data set, and relates to depth volumes. The product network model, LeNet-5, performs comparative experiments and proves that this method can further improve the classification accuracy.
Millimeter-Wave Extended NYUSIM Channel Model for Spatial Consistency

Commonly used drop-based channel models cannot satisfy the requirements of spatial consistency for millimeterwave (mmWave) channel modeling where transient motion or closely-spaced users need to be considered. A channel model having spatial consistency can capture the smooth variations of channels, when a user moves, or when multiple users are close to each other in a local area within, say, 10 $m$ in an outdoor scenario. Spatial consistency is needed to support the testing of beamforming and beam tracking for massive multiple-input and multiple-output (MIMO) and multi-user MIMO in fifth-generation (5G) mmWave mobile networks. This paper presents a channel model extension and an associated implementation of spatial consistency in the NYUSIM channel simulation platform [1], [2]. Along with a mathematical model, we use measurements where the user moved along a street and turned at a corner over a path length of 75 $m$ in order to derive realistic values of several key parameters such as correlation distance and the rate of cluster birth and death, that are shown to provide spatial consistency for NYUSIM in an urban microcell street canyon scenario.
Structures of NHBA elucidate a broadly conserved epitope identified by a vaccine induced antibody

Neisserial heparin binding antigen (NHBA) is one of three main recombinant protein antigens in 4CMenB, a vaccine for the prevention of invasive meningococcal disease caused by Neisseria meningitidis serogroup B. NHBA is a surface-exposed lipoprotein composed of a predicted disordered N-terminal region, an arginine-rich region that binds heparin, and a C-terminal domain that folds as an anti-parallel β-barrel and that upon release after cleavage by human proteases alters endothelial permeability. NHBA induces bactericidal antibodies in humans, and NHBA-specific antibodies elicited by the 4CMenB vaccine contribute to serum bactericidal activity, the correlate of protection. To better understand the structural bases of the human antibody response to 4CMenB vaccination and to inform antigen design, we used X-ray crystallography to elucidate the structures of two C-terminal fragments of NHBA, either alone or in complex with the Fab derived from the vaccine-elicited human monoclonal antibody 5H2, and the structure of the unbound Fab 5H2. The structures reveal details on the interaction between an N-terminal β-hairpin fragment and the β-barrel, and explain how NHBA is capable of generating cross-reactive antibodies through an extensive conserved conformational epitope that covers the entire C-terminal face of the β-barrel. By providing new structural information on a vaccine antigen and on the human immune response to vaccination, these results deepen our molecular understanding of 4CMenB, and might also aid future vaccine design projects.
High-performance and flexible photodetectors based on chemical vapor deposition grown two-dimensional In2Se3 nanosheets

Two-dimensional (2D) In2Se3 with unique optical and electrical properties has great potential in next generation optoelectronics and multilevel phase-change memories. Here, for the first time, we report high-performance rigid and flexible photodetectors based on chemical vapor deposition (CVD) grown 2D In2Se3. Both rigid and flexible 2D In2Se3 photodetectors show a broadband response range from ultraviolet (254 nm) to visible light (700 nm). High photoresponsivities of 578 and 363 A · W−1 are achieved using rigid and flexible 2D In2Se3 photodetectors, respectively, under 700 nm light illumination, which are higher than those of photodetectors based on mechanically exfoliated 2D In2Se3 and physical vapor deposition grown 2D In2Se3. Furthermore, flexible 2D In2Se3 photodetectors show good mechanical durability and photoresponse stability under repeated bending tests. A high and stable photoresponse provides an opportunity for CVD-grown 2D In2Se3 applications in flexible optoelectronic and photovoltaic devices.
Approximating Poker Probabilities with Deep Learning

Many poker systems, whether created with heuristics or machine learning, rely on the probability of winning as a key input. However calculating the precise probability using combinatorics is an intractable problem, so instead we approximate it. Monte Carlo simulation is an effective technique that can be used to approximate the probability that a player will win and/or tie a hand. However, without the use of a memory-intensive lookup table or a supercomputer, it becomes infeasible to run millions of times when training an agent with self-play. To combat the space-time tradeoff, we use deep learning to approximate the probabilities obtained from the Monte Carlo simulation with high accuracy. The learned model proves to be a lightweight alternative to Monte Carlo simulation, which ultimately allows us to use the probabilities as inputs during self-play efficiently. The source code and optimized neural network can be found at this https URL
Demographic Change Across the Lifespan of Pet Dogs and Their Impact on Health Status

Although dogs' life expectancies are six to twelve times shorter than that of humans, the demographics (e. g., living conditions) of dogs can still change considerably with aging, similarly to humans. Despite the fact that the dog is a particularly good model for human healthspan, and the number of aged dogs in the population is growing in parallel with aged humans, there has been few previous attempts to describe demographic changes statistically. We utilized an on-line questionnaire to examine the link between the age and health of the dog, and owner and dog demographics in a cross-sectional Hungarian sample. Results from univariate analyses revealed that 20 of the 27 demographic variables measured differed significantly between six dog age groups. Our results revealed that pure breed dogs suffered from health problems at a younger age, and may die at an earlier age than mixed breeds. The oldest dog group (>12 years) consisted of fewer pure breeds than mixed breeds and the mixed breeds sample was on average older than the pure breed sample. Old dogs were classified more frequently as unhealthy, less often had a “normal” body condition score, and more often received medication and supplements. They were also more often male, neutered, suffered health problems (such as sensory, joint, and/or tooth problems), received less activity/interaction/training with the owner, and were more likely to have experienced one or more traumatic events. Surprisingly, the youngest age group contained more pure breeds, were more often fed raw meat, and had owners aged under 29 years, reflecting new trends among younger owners. The high prevalence of dogs that had experienced one or more traumatic events in their lifetime (over 40% of the sample), indicates that welfare and health could be improved by informing owners of the greatest risk factors of trauma, and providing interventions to reduce their impact. Experiencing multiple life events such as spending time in a shelter, changing owners, traumatic injury/prolonged disease/surgery, getting lost, and changes in family structure increased the likelihood that owners reported that their dogs currently show behavioral signs that they attribute to the previous trauma.
Glial Cell Expansion Coincides with Neural Circuit Formation in the Developing Auditory Brainstem

Neural circuit formation involves maturation of neuronal, glial and vascular cells, as well as cell proliferation and cell death. A fundamental understanding of cellular mechanisms is enhanced by quantification of cell types during key events in synapse formation and pruning and possessing qualified genetic tools for cell type‐specific manipulation. Acquiring this information in turn requires validated cell markers and genetic tools. We quantified changing proportions of neurons, astrocytes, oligodendrocytes, and microglia in the medial nucleus of the trapezoid body (MNTB) during neural circuit development. Cell type‐specific markers, light microscopy and 3D virtual reality software, the latter developed in our laboratory, were used to count cells within distinct cell populations at postnatal days (P)3 and P6, bracketing the period of nerve terminal growth and pruning in this system. These data revealed a change from roughly equal numbers of neurons and glia at P3 to a 1.5:1 ratio of glia to neurons at P6. PCNA and PH3 labeling revealed that proliferation of oligodendrocytes contributed to the increase in glial cell number during this timeframe. We next evaluated Cre driver lines for selectivity in labeling cell populations. En1‐Cre was specific for MNTB neurons. PDGFRα‐Cre and Aldh1L1‐Cre, thought to be mostly specific for oligodendrocyte lineage cells and astrocytes, respectively, both labeled significant numbers of neurons, oligodendrocytes, and astrocytes and are non‐specific genetic tools in this neural system.
European position paper on drug‐induced sleep endoscopy: 2017 Update

The first edition of the European position paper (EPP) on drug‐induced sleep endoscopy (DISE) was published in 2014 with the aim to standardise the procedure, to provide an in‐depth insight into the main aspects of this technique and to have a basis for future research. Since 2014, new studies have been published concerning new sedative agents or new insights into the pattern/levels of the obstruction depending on the depth of sedation. Therefore, an enlarged group of European experts in the field of sleep breathing disorders (SBD), including the most of the first DISE EPP main authors, has decided to publish an update of the European position paper on DISE, in order to include new evidence and to find a common language useful for reporting the findings of this endoscopic evaluation in adult population affected by SBD.
Cytotoxic and Apoptotic Effects of Govaniadine Isolated from Corydalis govaniana Wall. Roots on Human Breast Cancer (MCF-7) Cells

Current breast cancer therapies have limitations in terms of increased drug resistance resulting in short-term efficacy, thus demanding the discovery of new therapeutic agents. In this study, cytotoxic activity and apoptotic effects of govaniadine isolated from Corydalis govaniana Wall. roots were determined on human breast cancer (MCF-7) cells. The SRB assay result revealed that govaniadine led to dose- and time-dependent cytotoxic effect in MCF-7 cells along with less cytotoxicity against MCF-10A cells. Govaniadine-induced apoptosis was also accompanied by upregulation of Bax, p53, and Survivin mRNA expression as assessed by real time PCR analysis. Flow cytometric analysis with Annexin V and PI staining indicated that govaniadine is a potent inducer of apoptosis in MCF-7 cell lines. Distinctive morphological changes contributed to apoptosis and DNA laddering were observed in govaniadine-treated MCF-7 cells. Caspase-7 was significantly activated in treated MCF-7 cells. Govaniadine-treated MCF-7 cells also showed enhanced levels of intracellular reactive oxygen species (ROS) and glutathione S-transferase (GST) and decreased levels of glutathione (GSH). The results indicate that govaniadine has potent and selective cytotoxic effects against MCF-7 cells and the potential to induce caspase 7 dependent apoptosis in MCF-7 cells by activation of pathways that lead to oxidative stress.
Peroxidase-catalyzed microextrusion bioprinting of cell-laden hydrogel constructs in vaporized ppm-level hydrogen peroxide

Hydrogels were prepared by contacting air containing 10–50 ppm H2O2 with an aqueous solution containing polymer(s) possessing phenolic hydroxyl (Ph) moieties (polymer-Ph) and horseradish peroxidase (HRP). In this system, HRP catalyzes cross-linking of the Ph moieties by consuming H2O2 diffused from the air. The hydrogelation rate and mechanical properties of the resultant hydrogels can be tuned by controlling the H2O2 concentration in air, the exposure time of the air containing H2O2 to the solution containing polymer-Phs and HRP, and the HRP concentration. The shortest hydrogelation time of the solution stirred in air containing 16 ppm H2O2 was 6 s. Based on these findings, this hydrogelation system was applied to microextrusion bioprinting, in which bioink containing polymer-Phs, HRP, and cells were extruded into air containing H2O2. The superior cytocompatibility of the bioprinting method was confirmed by more than 90% viability, migration, and the spreading of mouse fibroblast 10T1/2 cells enclosed in the bioprinted hydrogels composed of derivatives of hyaluronic acid and gelatin, both possessing Ph moieties. These results demonstrate the great potency of HRP-catalyzed hydrogelation consuming H2O2 supplied in surrounding air for various biomedical applications, especially bioprinting.
Dual functions of ZmNF-YA3 in photoperiod-dependent flowering and abiotic stress responses in maize

Nuclear factor-Y (NF-Y) transcription factors are important regulators of several essential biological processes, including embryogenesis, drought resistance, meristem maintenance, and photoperiod-dependent flowering in Arabidopsis. However, the regulatory mechanisms of NF-Ys in maize (Zea mays) are not well understood yet. In this study, we identified an NF-Y transcription factor, ZmNF-YA3. Genome-wide analysis showed that ZmNF-YA3 bound to >6000 sites in the maize genome, 2259 of which are associated with genic sequences. ZmNF-YA3 was found to interact with CONSTANS-like (CO-like) and flowering promoting factor1 (FPF1) through yeast two-hybrid and bimolecular fluorescence complementation (BiFC) assays. Quantitative real-time reverse transcription-PCR (qRT-PCR) combined with yeast one-hybrid assay and EMSA suggested that NF-YA3 could promote early flowering by binding to the FLOWERING LOCUS T-like12 (FT-like12) promoter in maize. Morerover, we also showed that ZmNF-YA3 could improve drought and high-temperature tolerance through binding to the promoter regions of bHLH92, FAMA, and the jasmonic acid activator MYC4, respectively. These results contribute to a comprehensive understanding of the molecular mechanisms and regulatory networks of NF-Y transcription factors in regulating maize flowering time and stress response in maize.
DroidMate-2: A Platform for Android Test Generation

Android applications (apps) represent an ever increasing portion of the software market. Automated test input generators are the state of the art for testing and security analysis. We introduceDroidMate-2 (DM-2), a platform to easily assist both developers and researchers to customize, develop and test new test generators. DM-2 can be used without app instrumentation or operating system modifications, as a test generator on real devices and emulators for app testing or regression testing. Additionally, it provides sensitive resource monitoring or blocking capabilities through a lightweight app instrumentation, out-of-the-box statement coverage measurement through a fully-fledged app instrumentation and native experiment reproducibility. In our experiments we compared DM-2 against Droid-Bot, a state-of-the-art test generator by measuring statement coverage. Our results show that DM-2 reached 96% of its peak coverage in less than 2/3 of the time needed by Droid-Bot, allowing for better and more efficient tests. On short runs (5 minutes) DM-2 outperformed Droid-Bot by 7% while in longer runs (1 hour) this difference increases to 8%. ACM DL Artifact: https://www.doi. org/10.1145/3264864. For the details see: https://github.com/uds-se/droidmate/wiki/ASE-2018:-Data