Miracle-dz/UniFAR-FTUs huggingface dataset:
Data Poisoning Attack against Unsupervised Node Embedding Methods

Unsupervised node embedding methods (e.g., DeepWalk, LINE, and node2vec) have attracted growing interests given their simplicity and effectiveness. However, although these methods have been proved effective in a variety of applications, none of the existing work has analyzed the robustness of them. This could be very risky if these methods are attacked by an adversarial party. In this paper, we take the task of link prediction as an example, which is one of the most fundamental problems for graph analysis, and introduce a data positioning attack to node embedding methods. We give a complete characterization of attacker's utilities and present efficient solutions to adversarial attacks for two popular node embedding methods: DeepWalk and LINE. We evaluate our proposed attack model on multiple real-world graphs. Experimental results show that our proposed model can significantly affect the results of link prediction by slightly changing the graph structures (e.g., adding or removing a few edges). We also show that our proposed model is very general and can be transferable across different embedding methods. Finally, we conduct a case study on a coauthor network to better understand our attack method.
Azidothymidine Produces Synergistic Activity in Combination with Colistin against Antibiotic-Resistant Enterobacteriaceae

Bacterial infections remain a leading killer worldwide, which is worsened by the continuous emergence of antibiotic resistance. In particular, antibiotic-resistant Enterobacteriaceae are prevalent and extremely difficult to treat. ABSTRACT Bacterial infections remain a leading killer worldwide, which is worsened by the continuous emergence of antibiotic resistance. In particular, antibiotic-resistant Enterobacteriaceae are prevalent and extremely difficult to treat. Repurposing existing drugs and improving the therapeutic potential of existing antibiotics represent an attractive novel strategy. Azidothymidine (AZT) is an antiretroviral drug which is used in combination with other antivirals to prevent and to treat HIV/AIDS. AZT is also active against Gram-negative bacteria but has not been developed for that purpose. Here, we investigated the in vitro and in vivo efficacy of AZT in combination with colistin against antibiotic-resistant Enterobacteriaceae, including strains producing extended-spectrum beta-lactamases (ESBLs) or New Delhi metallo-beta-lactamase 1 (NDM) or carrying mobilized colistin resistance (mcr-1). The MIC was determined using the broth microdilution method. The combined effect of AZT and colistin was examined using the checkerboard method and time-kill analysis. A murine peritoneal infection model was used to test the therapeutic effect of the combination of AZT and colistin. The fractional inhibitory concentration index from the checkerboard assay demonstrated that AZT synergized with colistin against 61% and 87% of ESBL-producing Escherichia coli and Klebsiella pneumoniae strains, respectively, 100% of NDM-1-producing strains, and 92% of mcr-1-producing E. coli strains. Time-kill analysis demonstrated significant synergistic activities when AZT was combined with colistin. In a murine peritoneal infection model, AZT in combination with colistin showed augmented activities of both drugs in the treatment of NDM-1 K. pneumoniae and mcr-1 E. coli infections. The AZT and colistin combination possesses a potential to be used coherently to treat antibiotic-resistant Enterobacteriaceae infections.
General audio tagging with ensembling convolutional neural network and statistical features

Audio tagging aims to infer descriptive labels from audio clips and it is challenging due to the limited size of data and noisy labels. The solution to the tagging task is described in this paper. The main contributions include the following: an ensemble learning framework is applied to ensemble statistical features and the outputs from the deep classifiers, with the goal to utilize complementary information. Moreover, a sample re-weight strategy is employed to address the noisy label problem within the framework. The approach achieves a mean average precision of 0.958, outperforming the baseline system with a large margin.
Integration of Small- and Wide-Field Visual Features in Target-Selective Descending Neurons of both Predatory and Nonpredatory Dipterans

For many animals, target motion carries high ecological significance as this may be generated by a predator, prey, or potential mate. Indeed, animals whose survival depends on early target detection are often equipped with a sharply tuned visual system, yielding robust performance in challenging conditions. For example, many fast-flying insects use visual cues for identifying targets, such as prey (e.g., predatory dragonflies and robberflies) or conspecifics (e.g., nonpredatory hoverflies), and can often do so against self-generated background optic flow. Supporting these behaviors, the optic lobes of insects that pursue targets harbor neurons that respond robustly to the motion of small moving objects, even when displayed against syn-directional background clutter. However, in diptera, the encoding of target information by the descending neurons, which are more directly involved in generating the behavioral output, has received less attention. We characterized target-selective neurons by recording in the ventral nerve cord of male and female predatory Holcocephala fusca robberflies and of male nonpredatory Eristalis tenax hoverflies. We show that both species have dipteran target-selective descending neurons that only respond to target motion if the background is stationary or moving slowly, moves in the opposite direction, or has un-naturalistic spatial characteristics. The response to the target is suppressed when background and target move at similar velocities, which is strikingly different to the response of target neurons in the optic lobes. As the neurons we recorded from are premotor, our findings affect our interpretation of the neurophysiology underlying target-tracking behaviors. SIGNIFICANCE STATEMENT Many animals use sensory cues to detect moving targets that may represent predators, prey, or conspecifics. For example, birds of prey show superb sensitivity to the motion of small prey, and intercept these at high speeds. In a similar manner, predatory insects visually track moving prey, often against cluttered backgrounds. Accompanying this behavior, the brains of insects that pursue targets contain neurons that respond exclusively to target motion. We here show that dipteran insects also have target-selective descending neurons in the part of their nervous system that corresponds to the vertebrate spinal cord. Surprisingly, and in contrast to the neurons in the brain, these premotor neurons are inhibited by background patterns moving in the same direction as the target.
Does patient education improve compliance to routine diabetic retinopathy screening?

Introduction Diabetic retinopathy (DR) screening relies on adherence to follow-up eye care. This article assesses if a model of patient education and tele-retina screening among high-risk patients with DR can achieve increased rates of compliance within a one-year follow-up. Methods Between May 2014 and May 2016, DR screening was conducted in a cohort of 101 patients with diabetes in Southern Ontario. Optical coherence tomography and fundus photography images were used to visualize the retina remotely. Enrolled patients participated in an educational seminar at the screening site with the expressed purpose of enhancing patient understanding of DR. A chi-squared test was used to assess patient compliance to follow-up examinations within 6–12 months, while pre-to post-screening HbA1c levels were compared using a dependent t-test. Results Of 101 patients who completed the study, 33 patients (32.6%) have never previously been screened for DR. Baseline compliance to annual screening increased from 36 patients (35.6%) to 51 patients (50.5%) after the tele-retina programme (p = 0.03). Eighty-nine patients (88%) were referred to an optometrist for ongoing care compared with 12 patients (11.9%) to an ophthalmologist for management of DR. Overall, 100 patients (99.0%) were satisfied with the tele-retina screening. There was no significant change in pre- to-post screening HbA1c levels (p = 0.91). Discussion Patient education-focused tele-retina screening for DR significantly increased compliance to follow-up in a high-risk, non-compliant patient population. Management of diabetes as captured by HbA1c levels remain unchanged in the cohort indicating a need for ongoing inter-professional collaboration in education and vision screening.
The First Decade of Web-Based Sports Injury Surveillance: Descriptive Epidemiology of Injuries in US High School Boys' Soccer (2005-2006 Through 2013-2014) and National Collegiate Athletic Association Men's Soccer (2004-2005 Through 2013-2014).

CONTEXT: The advent of Web-based sports injury surveillance via programs such as the High School Reporting Information Online system and the National Collegiate Athletic Association Injury Surveillance Program has aided the acquisition of boys' and men's soccer injury data. OBJECTIVE: To describe the epidemiology of injuries sustained in high school boys' soccer in the 2005-2006 through 2013-2014 academic years and collegiate men's soccer in the 2004-2005 through 2013-2014 academic years using Web-based sports injury surveillance. DESIGN: Descriptive epidemiology study. SETTING: Online injury surveillance from soccer teams of high school boys (annual average = 100) and collegiate men (annual average = 41). PATIENTS OR OTHER PARTICIPANTS: Boys' or men's soccer players who participated in practices and competitions during the 2005-2006 through 2013-2014 academic years in high school and the 2004-2005 through 2013-2014 academic years in college, respectively. MAIN OUTCOME MEASURE(S): Athletic trainers collected time-loss (≥24 hours) injury and exposure data. Injury rates per 1000 athlete-exposures (AEs), injury rate ratios (IRRs) with 95% confidence intervals (CIs), and injury proportions by body site and diagnosis were calculated. RESULTS: High School Reporting Information Online documented 2912 time-loss injuries during 1 592 238 AEs; the National Collegiate Athletic Association Injury Surveillance Program documented 4765 time-loss injuries during 686 918 AEs. The injury rate was higher in college than in high school (6.94 versus 1.83/1000 AEs; IRR = 3.79; 95% CI = 3.62, 3.97). Injury rates increased with smaller school size for high schools and were higher in Division I than in Divisions II and III. The injury rate was higher during competitions than during practices in both high school (IRR = 3.55; 95% CI = 3.30, 3.83) and college (IRR = 3.45; 95% CI = 3.26, 3.65). Most injuries were to the lower extremity. However, concussion was a common injury, particularly in collegiate goalkeepers and at all positions for high school players. Concussions accounted for more than one-fifth of injuries in high school games. CONCLUSIONS: Injury-prevention interventions should be tailored to reflect variations in the incidence and type of injury by level of competition, event type, and position.
Optimal pricing strategy for resource allocation in 5G heterogeneous cellular networks

Heterogeneous networks provide an effective solution to bridge the gap between enormous global traffic demands and limited spectrum resources. In this paper, we investigate the resource allocation problem in fifth‐generation heterogeneous cellular networks from an economic perspective. We consider the open access scenario where any cellular user equipment is allowed to access either the high‐power node or the low‐power node based on its individual payoff. The resource allocation problem is formulated as a pricing problem, which aims at maximizing the total revenue of the service providers under the user admission constraint. Then, the optimal pricing strategies for both the high‐power node and the low‐power node are developed based on the decisions of cellular user equipments. Simulation results demonstrate that the proposed pricing algorithm can achieve the maximum revenue for service providers under various scenarios.
Social Media Brand Engagement as a Proxy for E-Commerce Activities: A Case Study of Sina Weibo and JD

E-commerce platforms facilitate sales of products while product vendors engage in Social Media Activities (SMA) to drive E-commerce Platform Activities (EPA) of consumers, enticing them to search, browse and buy products. The frequency and timing of SMA are expected to affect levels of EPA, increasing the number of brand related queries, clickthrough, and purchase orders. This paper applies cross-sectional data analysis to explore such beliefs and demonstrates weak-to-moderate correlations between daily SMA and EPA volumes. Further correlation analysis, using 30-day rolling windows, shows a high variability in correlation of SMA-EPA pairs and calls into question the predictive potential of SMA in relation to EPA. Considering the moderate correlation of selected SMA and EPA pairs (e.g., Post-Orders), we investigate whether SMA features can predict changes in the EPA levels, instead of precise EPA daily volumes. We define such levels in terms of EPA distribution quantiles (2, 3, and 5 levels) over training data. We formulate the EPA quantile predictions as a multi-class categorization problem. The experiments with Random Forest and Logistic Regression show a varied success, performing better than random for the top quantiles of purchase orders and for the lowest quantile of search and clickthrough activities. Similar results are obtained when predicting multi-day cumulative EPA levels (1, 3, and 7 days). Our results have considerable practical implications but, most importantly, urge the common beliefs to be re-examined, seeking a stronger evidence of SMA effects on EPA.
Allergen Exposure in Lymphopenic Fas-Deficient Mice Results in Persistent Eosinophilia Due to Defects in Resolution of Inflammation

Asthma is characterized by chronic airway type-2 inflammation and eosinophilia, yet the mechanisms involved in chronic, non-resolving inflammation remain poorly defined. Previously, our group has found that when Rag-deficient mice were reconstituted with Fas-deficient B6 LPR T cells and sensitized and challenged, the mice developed a prolonged type-2-mediated airway inflammation that continued for more than 6 weeks after the last antigen exposure. Surprisingly, no defect in resolution was found when intact B6 LPR mice or T cell specific Fas-conditional knockout mice were sensitized and challenged. We hypothesize that the homeostatic proliferation induced by adoptive transfer of T cells into Rag-deficient mice may be an important mechanism involved in the lack of resolution. To investigate the role of homeostatic proliferation, we induced lymphopenia in the T cell-specific Fas-conditional knockout mice by non-lethal irradiation and sensitized them when T cells began to repopulate. Interestingly, we found that defective Fas signaling on T cells plus antigen exposure during homeostatic proliferation was sufficient to induce prolonged eosinophilic airway inflammation. In conclusion, our data show that the combination of transient lymphopenia, abnormal Fas-signaling, and antigen exposure leads to the development of a prolonged airway eosinophilic inflammatory phase in our mouse model of experimental asthma.
On Improving Service Chains Survivability Through Efficient Backup Provisioning

With the growing adoption of Software Defined Networking (SDN) and Network Function Virtualization (NFV), large-scale NFV infrastructure deployments are gaining momentum. Such infrastructures are home to thousands of network Service Function Chains (SFCs), each composed of a chain of virtual network functions (VNFs) that are processing incoming traffic flows. Unfortunately, in such environments, the failure of a single node may break down several VNFs and thereby breaking many service chains at the same time.In this paper, we address this particular problem and investigate possible solutions to ensure the survivability of the affected service chains by provisioning backup VNFs that can take over in case of failure. Specifically, we propose a survivability management framework to efficiently manage SFCs and the backup VNFs. We formulate the SFC survivability problem as an integer linear program that determines the minimum number of required backups to protect all the SFCs in the system and identifies their optimal placement in the infrastructure. We also propose two heuristic algorithms to cope with the large-scale instances of the problem. Through extensive simulations of different deployment scenarios, we show that these algorithms provide near-optimal solutions with minimal computation time.
A neural field model for color perception unifying assimilation and contrast

We address the question of color-space interactions in the brain, by proposing a neural field model of color perception with spatial context for the visual area V1 of the cortex. Our framework reconciles two opposing perceptual phenomena, known as simultaneous contrast and chromatic assimilation. They have been previously shown to act synergistically, so that at some point in an image, the color seems perceptually more similar to that of adjacent neighbors, while being more dissimilar from that of remote ones. Thus, their combined effects are enhanced in the presence of a spatial pattern, and can be measured as larger shifts in color matching experiments. Our model supposes a hypercolumnar structure coding for colors in V1, and relies on the notion of color opponency introduced by Hering. The connectivity kernel of the neural field exploits the balance between attraction and repulsion in color and physical spaces, so as to reproduce the sign reversal in the influence of neighboring points. The color sensation at a point, defined from a steady state of the neural activities, is then extracted as a nonlinear percept conveyed by an assembly of neurons. It connects the cortical and perceptual levels, because we describe the search for a color match in asymmetric matching experiments as a mathematical projection on color sensations. We validate our color neural field alongside this color matching framework, by performing a multi-parameter regression to data produced by psychophysicists and ourselves. All the results show that we are able to explain the nonlinear behavior of shifts observed along one or two dimensions in color space, which cannot be done using a simple linear model.
Toxic metals (As and Pb) in Sargassum elegans Suhr (1840) and its bioactive compounds

ABSTRACT Sargassum elegans Suhr 1840 (Phaeophyta) is a brown marine macro alga, which is used both nutritionally and medicinally in the coastal areas of Southern Africa. Consequently, we conducted a phytochemical and analytical investigation on samples of this species collected from seven sites along the coast of KwaZulu-Natal, South Africa. Sargassum elegans was found to be rich in β-sitosterol, fucosterol and phaeophytin a as confirmed by spectroscopic techniques. Concentrations of essential and toxic elements varied significantly with location and were in the order of Ca > Mg> Fe > As > Cu > Zn > Mn > Ni > Pb > Co > Se > Cr > Cd. The accumulation of As by S. elegans was also evident as concentrations ranged from 42 to 105 mg kg−1, of which, 21 to 53 mg kg−1 was in inorganic form; consumption of this species may therefore increase dietary exposure to inorganic arsenic. Abbreviation: CRM: Certified reference material; NMR: Nuclear magnetic resonance; ICP – OES: Inductively coupled plasma - optical emission spectroscopy; PCA: Principal component analysis
Differentiable Greedy Networks

Optimal selection of a subset of items from a given set is a hard problem that requires combinatorial optimization. In this paper, we propose a subset selection algorithm that is trainable with gradient-based methods yet achieves near-optimal performance via submodular optimization. We focus on the task of identifying a relevant set of sentences for claim verification in the context of the FEVER task. Conventional methods for this task look at sentences on their individual merit and thus do not optimize the informativeness of sentences as a set. We show that our proposed method which builds on the idea of unfolding a greedy algorithm into a computational graph allows both interpretability and gradient-based training. The proposed differentiable greedy network (DGN) outperforms discrete optimization algorithms as well as other baseline methods in terms of precision and recall.
Preventing DDoS using Bloom Filter: A Survey

Distributed Denial-of-Service (DDoS) is a menace for service provider and prominent issue in network security. Defeating or defending the DDoS is a prime challenge. DDoS make a service unavailable for a certain time. This phenomenon harms the service providers, and hence, loss of business revenue. Therefore, DDoS is a grand challenge to defeat. There are numerous mechanism to defend DDoS, however, this paper surveys the deployment of Bloom Filter in defending a DDoS attack. The Bloom Filter is a probabilistic data structure for membership query that returns either true or false. Bloom Filter uses tiny memory to store information of large data. Therefore, packet information is stored in Bloom Filter to defend and defeat DDoS. This paper presents a survey on DDoS defending technique using Bloom Filter.
Named Entity Analysis and Extraction with Uncommon Words

Most previous research treats named entity extraction and classification as an end-to-end task. We argue that the two sub-tasks should be addressed separately. Entity extraction lies at the level of syntactic analysis while entity classification lies at the level of semantic analysis. According to Noam Chomsky's "Syntactic Structures," pp. 93-94 (Chomsky 1957), syntax is not appealed to semantics and semantics does not affect syntax. We analyze two benchmark datasets for the characteristics of named entities, finding that uncommon words can distinguish named entities from common text; where uncommon words are the words that hardly appear in common text and they are mainly the proper nouns. Experiments validate that lexical and syntactic features achieve state-of-the-art performance on entity extraction and that semantic features do not further improve the extraction performance, in both of our model and the state-of-the-art baselines. With Chomsky's view, we also explain the failure of joint syntactic and semantic parsings in other works.