Miracle-dz/UniFAR-FTUs huggingface dataset:
Demarcating Endogenous and Exogenous Opinion Diffusion Process on Social Networks

The networked opinion diffusion in online social networks (OSN) is governed by the two genres of opinions-endogenous opinions that are driven by the influence of social contacts between users, and exogenous opinions which are formed by external effects like news, feeds etc. Such duplex opinion dynamics is led by users belonging to two categories- organic users who generally post endogenous opinions and extrinsic users who are susceptible to externalities, and mostly post the exogenous messages. Precise demarcation of endogenous and exogenous messages offers an important cue to opinion modeling, thereby enhancing its predictive performance. On the other hand, accurate user selection aids to detect extrinsic users, which in turn helps in opinion shaping. In this paper, we design CherryPick, a novel learning machinery that classifies the opinions and users by solving a joint inference task in message and user set, from a temporal stream of sentiment messages. Furthermore, we validate the efficacy of our proposal from both modeling and shaping perspectives. Moreover, for the latter, we formulate the opinion shaping problem in a novel framework of stochastic optimal control, in which the selected extrinsic users optimally post exogenous messages so as to guide the opinions of others in a desired way. On five datasets crawled from Twitter, CherryPick offers a significant accuracy boost in terms of opinion forecasting, against several competitors. Furthermore, it can precisely determine the quality of a set of control users, which together with the proposed online shaping strategy, consistently steers the opinion dynamics more effectively than several state-of-the-art baselines.
Universal Features in Phonological Neighbor Networks

Human speech perception involves transforming a countinuous acoustic signal into discrete linguistically meaningful units (phonemes) while simultaneously causing a listener to activate words that are similar to the spoken utterance and to each other. The Neighborhood Activation Model posits that phonological neighbors (two forms [words] that differ by one phoneme) compete significantly for recognition as a spoken word is heard. This definition of phonological similarity can be extended to an entire corpus of forms to produce a phonological neighbor network (PNN). We study PNNs for five languages: English, Spanish, French, Dutch, and German. Consistent with previous work, we find that the PNNs share a consistent set of topological features. Using an approach that generates random lexicons with increasing levels of phonological realism, we show that even random forms with minimal relationship to any real language, combined with only the empirical distribution of language-specific phonological form lengths, are sufficient to produce the topological properties observed in the real language PNNs. The resulting pseudo-PNNs are insensitive to the level of lingustic realism in the random lexicons but quite sensitive to the shape of the form length distribution. We therefore conclude that “universal” features seen across multiple languages are really string universals, not language universals, and arise primarily due to limitations in the kinds of networks generated by the one-step neighbor definition. Taken together, our results indicate that caution is warranted when linking the dynamics of human spoken word recognition to the topological properties of PNNs, and that the investigation of alternative similarity metrics for phonological forms should be a priority.
Nanoparticle Optical Properties: Size Dependence of a Single Gold Spherical Nanoparticle

The optical properties of a spherical gold nanoparticle have become our interest due to its remarkable characteristics when interacting with the light. In this work, the dependence of surface plasmon resonance on the size of the gold nanosphere has been studied with varying diameter from 20 to 100 nm. The optical spectra of the single isolated gold nanoparticles are simulated by employing the versatile MNPBEM toolbox. The wavelength corresponding to the maximum extinction, absorption as well as scattering redshifts (shift to longer wavelengths) were observed as the nanoparticle size increased. The rate of change of scattering (Δsca) and absorption (Δabs) relative to the extinction is calculated to correlate these two properties. For the nanoparticle with size less than 70 nm, Δabs is larger than Δsca, whereas for the diameter of nanoparticles above 70 nm, they show that Δsca is larger than Δabs. Due to this, it is found that absorption efficiency initially increases with nanoparticle size, which eventually reaches a maximum at a size of 70 nm and then begins to decrease with further increase in the nanoparticle diameter. The observed volcano trend of absorption efficiency is the result of electromagnetic retardation and radiative scattering. It can be concluded that the intermediate-size nanoparticle has the highest absorption efficiency with an optimal size of 70 nm for gold nanoparticles.
Clinical Usefulness of Procalcitonin and C-Reactive Protein as Outcome Predictors in Critically Ill Patients with Severe Sepsis and Septic Shock

Sepsis is a major cause of mortality and morbidity in critically ill patients. Procalcitonin (PCT) and C-reactive protein (CRP) are the most frequently used biomarkers in sepsis. We investigated changes in PCT and CRP concentrations in critically ill patients with sepsis to determine which biochemical marker better predicts outcome. We retrospectively analyzed 171 episodes in 157 patients with severe sepsis and septic shock who were admitted to the Samsung Medical Center intensive care unit from March 2013 to February 2014. The primary endpoint was patient outcome within 7 days from ICU admission (treatment failure). The secondary endpoint was 28-day mortality. Severe sepsis was observed in 42 (25%) episodes from 41 patients, and septic shock was observed in 129 (75%) episodes from 120 patients. Fifty-five (32%) episodes from 42 patients had clinically-documented infection, and 116 (68%) episodes from 99 patients had microbiologically-documented infection. Initial peak PCT and CRP levels were not associated with treatment failure and 28-day mortality. However, PCT clearance (PCTc) and CRP (CRPc) clearance were significantly associated with treatment failure (p = 0.027 and p = 0.030, respectively) and marginally significant with 28-day mortality (p = 0.064 and p = 0.062, respectively). The AUC for prediction of treatment success was 0.71 (95% CI, 0.61–0.82) for PCTc and 0.71 (95% CI, 0.61–0.81) for CRPc. The AUC for survival prediction was 0.77 (95% CI, 0.66–0.88) for PCTc and 0.77 (95% CI, 0.67–0.88) for CRPc. Changes in PCT and CRP concentrations were associated with outcomes of critically ill septic patients. CRP may not be inferior to PCT in predicting outcome in these patients.
Racial Differences in Characteristics and Outcomes of Patients With Heart Failure and Preserved Ejection Fraction in the Treatment of Preserved Cardiac Function Heart Failure Trial

Background: Black patients have been shown to have different baseline characteristics and outcomes compared with nonblack patients in cohort studies. However, few studies have focused on heart failure (HF) with preserved ejection fraction (HFpEF) patients. We aimed to determine the difference in cardiovascular outcomes in black and nonblack patients with HFpEF and to determine the relative efficacy and safety of spironolactone in black and nonblack patients. Methods and Results: Patients with HFpEF, randomized to spironolactone versus placebo in the TOPCAT trial (Treatment of Preserved Cardiac Function Heart Failure With an Aldosterone Antagonist) in North and South America, were grouped according to self-described black and nonblack race. Black HFpEF patients (n=302) were younger and were more likely to have diabetes mellitus and hypertension than nonblack patients but had similar HFpEF severity. Black patients had higher risk for the primary outcome (hazard ratio [HR], 1.34; 95% confidence interval, 1.06–1.71; P=0.02) and first HF hospitalization (HR, 1.51; 95% confidence interval, 1.167–1.97; P=0.002)], but no significant difference in cardiovascular mortality risk (HR, 0.78; 95% confidence interval, 0.51–1.20; P=0.326). In black and nonblack patients, randomization to spironolactone conferred similar efficacy in the primary outcome (HR, 0.83 versus 0.79; P for interaction=0.49), HF hospitalization (HR, 0.67 versus 0.82; P for interaction=0.76), and cardiovascular mortality (P for interaction=0.19). The risk of hyperkalemia and worsening renal function with spironolactone and study drug adherence were also similar. Conclusions: Black patients with HFpEF have a higher HF hospitalization risk than nonblack patients, but spironolactone is similarly effective and safe in both groups. Clinical Trial Registration: URL: https://www.clinicaltrials. gov. Unique identifier: NCT00094302.
The Comparison between Endoscopic Submucosal Dissection and Surgery in Gastric Cancer: A Systematic Review and Meta-Analysis

Aims There are two treatment modalities for early gastric cancer (EGC)—surgery and endoscopic submucosal dissection (ESD). We aimed to compare the safety and efficacy of ESD with surgery. Method The article was performed by searching PubMed databases. Data were extracted using predefined form and odds ratios (OR) with 95% confidence intervals (CI) calculated and P value. Results 13 studies were identified. The incidence of perforation in two groups was different [OR = 6.18 (95% CI: 1.37–27.98), P = 0.02]. The prevalences of synchronous and metachronous cancer in the ESD group were higher than those in the surgery group [OR = 8.52 (95% CI: 1.99–36.56), P = 0.004 and OR = 7.15 (95% CI: 2.95–17.32), P < 0.0001]. The recurrence and complete resection rates were different [OR = 6.93 (95% CI: 2.83–16.96), P < 0.0001 and OR = 0.32 (95% CI: 0.20–0.52), P < 0.00001]. Compared with the surgery group, the hospital stay was shorter [IV = −7.15 (95% CI: −9.08–5.22), P < 0.00001], the adverse event rate was lower, and the quality of life (QOL) was better in the ESD group. The difference of bleeding was not found. Conclusion ESD appears to be preferable for EGC, due to a lower rate of adverse events, shorter hospital stay, cheaper cost, and higher QOL.
LiDAR and Camera Calibration Using Motions Estimated by Sensor Fusion Odometry

This paper proposes a targetless and automatic camera-LiDAR calibration method. Our approach extends the hand-eye calibration framework to 2D-3D calibration. The scaled camera motions are accurately calculated using a sensor-fusion odometry method. We also clarify the suitable motions for our calibration method. Whereas other calibrations require the LiDAR reflectance data and an initial extrinsic parameter, the proposed method requires only the three-dimensional point cloud and the camera image. The effectiveness of the method is demonstrated in experiments using several sensor configurations in indoor and outdoor scenes. Our method achieved higher accuracy than comparable state-of-the-art methods.
3D tracking of mating events in wild swarms of the malaria mosquito Anopheles gambiae

We describe an automated tracking system that allows us to reconstruct the 3D kinematics of individual mosquitoes in swarms of Anopheles gambiae. The inputs to the tracking system are video streams recorded from a stereo camera system. The tracker uses a two-pass procedure to automatically localize and track mosquitoes within the swarm. A human-in-the-loop step verifies the estimates and connects broken tracks. The tracker performance is illustrated using footage of mating events filmed in Mali in August 2010.
Associations of Topics of Discussion on Twitter With Survey Measures of Attitudes, Knowledge, and Behaviors Related to Zika: Probabilistic Study in the United States

Background Recent outbreaks of Zika virus around the world led to increased discussions about this issue on social media platforms such as Twitter. These discussions may provide useful information about attitudes, knowledge, and behaviors of the population regarding issues that are important for public policy. Objective We sought to identify the associations of the topics of discussions on Twitter and survey measures of Zika-related attitudes, knowledge, and behaviors, not solely based upon the volume of such discussions but by analyzing the content of conversations using probabilistic techniques. Methods Using probabilistic topic modeling with US county and week as the unit of analysis, we analyzed the content of Twitter online communications to identify topics related to the reported attitudes, knowledge, and behaviors captured in a national representative survey (N=33,193) of the US adult population over 33 weeks. Results Our analyses revealed topics related to “congress funding for Zika,” “microcephaly,” “Zika-related travel discussions,” “insect repellent,” “blood transfusion technology,” and “Zika in Miami” were associated with our survey measures of attitudes, knowledge, and behaviors observed over the period of the study. Conclusions Our results demonstrated that it is possible to uncover topics of discussions from Twitter communications that are associated with the Zika-related attitudes, knowledge, and behaviors of populations over time. Social media data can be used as a complementary source of information alongside traditional data sources to gauge the patterns of attitudes, knowledge, and behaviors in a population.
Evaluation of long‐term effects of artificial sweeteners on rat brain: a biochemical, behavioral, and histological study

The aim of the present study was to compare the effects of artificial sweeteners (aspartame, saccharin, and sucralose) on rat brain. Twenty‐four adult male Sprague–Dawley rats were included in the study. The control group (n = 6) received regular tap water, whereas other groups received aspartame (3 mg/kg/day, n = 6,) or saccharin (3 mg/kg/day, n = 6) or sucralose (1.5 mg/kg/day, n = 6) in the drinking water. Following 6 weeks, the passive avoidance learning (PAL) test was performed to evaluate the neurobehavioral effects of sweeteners. The brains were assessed for lipid peroxides, neuron count, and Glial fibrillary acidic protein (GFAP) immunohistochemistry. Our results demonstrated that chronic intake of sweeteners significantly impaired PAL performance in all groups. Hippocampal CA1–CA3 areas revealed significantly lower neuronal count in aspartame and increased GFAP expression in all groups. Brain lipid peroxides were significantly higher in all groups. Our findings suggest that long‐term consumption of artificial sweeteners may have harmful effects on cognition and hippocampal integrity in rats.
InfoClean: Protecting Sensitive Information in Data Cleaning

Data quality has become a pervasive challenge for organizations as they wrangle with large, heterogeneous datasets to extract value. Given the proliferation of sensitive and confidential information, it is crucial to consider data privacy concerns during the data cleaning process. For example, in medical database applications, varying levels of privacy are enforced across the attribute values. Attributes such as a patient’s country or city of residence may be less sensitive than the patient’s prescribed medication. Traditional data cleaning techniques assume the data is openly accessible, without considering the differing levels of information sensitivity. In this work, we take the first steps toward a data cleaning model that integrates privacy as part of the data cleaning process. We present a privacy-aware data cleaning framework that differentiates the information content among the attribute values during the data cleaning process to resolve data inconsistencies while minimizing the amount of information disclosed. Our data repair algorithm includes a set of data disclosure operations that considers the information content of the underlying attribute values, while maximizing data utility. Our evaluation using real datasets shows that our algorithm scales well, and achieves improved performance and comparable repair accuracy against existing data cleaning solutions.
Full-Duplex Small-Cell Networks: A Physical-Layer Security Perspective

We provide a theoretical study of physical (PHY)-layer security performance in full-duplex (FD) small-cell networks. Here, the multi-antenna base stations (BSs) and user equipments (UEs) follow from the homogeneous Poisson point process-based abstraction model. To facilitate FD communications, we take into account: 1) successive interference cancellation capability at the UE side via guard regions of arbitrary radii and 2) residual self-interference at the BS side using Rician fading distribution with arbitrary statistics. We investigate the small-cell network PHY-layer security performance in the presence of a Poisson field of eavesdroppers, under the different scenarios of passive and colluding eavesdropping. Considering linear zero-forcing beamforming, we characterize the downlink and the uplink ergodic secrecy rates and derive closed-form expressions for the different useful and interference signals statistics. In certain special cases of interest, we apply non-linear curve-fitting techniques to large sets of (exact) theoretical data in order to obtain closed-form approximations for the different ergodic rates and ergodic secrecy rates under consideration. Our findings indicate that the FD functionality, in addition to enhancing the spectral efficiency, can significantly improve the PHY-layer security performance, especially with the aid of multi-antenna communications and interference cancellation schemes.
Efficient Methods for Aggregate Reverse Rank Queries

Given two data sets of user preferences and product attributes in addition to a set of query products, the aggregate reverse rank (ARR) query returns top-k users who regard the given query products as the highest aggregate rank than other users. ARR queries are designed to focus on product bundling in marketing. Manufacturers are mostly willing to bundle several products together for the purpose of maximizing benefits or inventory liquidation. This naturally leads to an increase in data on users and products. Thus, the problem of efficiently processing ARR queries become a big issue. In this paper, we reveal two limitations of the stateof-the-art solution to ARR query; that is, (a) It has poor efficiency when the distribution of the query set is dispersive. (b) It has to process a large portion user data. To address these limitations, we develop a cluster-andprocess method and a sophisticated indexing strategy. From the theoretical analysis of the results and experimental comparisons, we conclude that our proposals have superior performance. key words: similarity search, aggregate reverse rank queries, clustering method, cone tree
UV-Metric, pH-Metric and RP-HPLC Methods to Evaluate the Multiple pKa Values of a Polyprotic Basic Novel Antimalarial Drug Lead, Cyclen Bisquinoline

The purpose of this experiment was to evaluate and compare the pKa values of the poorly water soluble, weakly basic, novel antimalarial drug lead, 4,10-bis (7-chloroquinoline)-1,4,7,10-tetraazacyclododecane (CNBQ). Three separate methods, pH-metric, UV-metric, and Reverse Phase-High Performance Liquid Chromatography (RP-HPLC), were employed to determine the pKa values between 2.0-12.0 pH range. The acetate and phosphate buffers, in addition to methanol and acetonitrile as co-solvents and potassium chloride to maintain the ionic strength, were used as appropriate. In UV-metric method, the drug substance is dissolved in aqueous media eliminating any interference of a co-solvent for measuring the pKa. Consequently, the pKa values obtained by the UV-metric method are considered accurate, as opposed to potentiometric and RP-HPLC methods that require the use of co-solvents. Thus, through the utilization of UV-metric method three pKa values, 5.9, 6.6, and 8.7, were obtained for CNBQ. These studies would be useful to determine the pKa values of the related drug leads under development.
Classification of modal meaning in negotiation dialogues

This paper addresses modality classiﬁcation for multi-issue bargaining dialogues in order to model human-like negotiation behaviour and to efﬁciently compute negotiation strategies. We pro-pose a modality annotation and classiﬁcation scheme comprising semantically distinguishable categories applied reliably by humans and machines. Our classiﬁcation of modality varieties is based on both the traditional dichotomy of epistemic and root modalities, and on the analysis of the available corpus data. Our modality scheme has been used for annotating human-human dialogues and training SVM-based classiﬁers. We built predictive models that show accuracies in the range between 73.3 and 82.6%.
ODDS: Real-Time Object Detection Using Depth Sensors on Embedded GPUs

Detecting objects that are carried when someone enters or exits a room is very useful for a wide range of smart building applications including safety, security, and energy efficiency. While there has been a significant amount of work on object recognition using large-scale RGB image datasets, RGB cameras are too privacy invasive in many smart building applications and they work poorly in the dark. Additionally, deep object detection networks require powerful and expensive GPUs. We propose a novel system that we call ODDS (Object Detector using a Depth Sensor) that can detect objects in real-time using only raw depth data on an embedded GPU, e.g., NVIDIA Jetson TX1. Hence, our solution is significantly less privacy invasive (even if the sensor is compromised) and less expensive, while maintaining a comparable accuracy with state of the art solutions. Specifically, we resort to training a deep convolutional neural network using raw depth images, with curriculum based learning to improve accuracy by considering the complexity and imbalance in object classes and developing a sparse coding based technique that speeds up the system ~2x with minimal loss of accuracy. Based on a complete implementation and real-world evaluation, we see ODDS achieve 80.14% mean average precision in object detection in real-time (5-6 FPS) on a Jetson TX1.
Joint User- and Event- Driven Stable Social Event Organization

The problem of social event organization (SEO) rises with the advent of online web services and plays an important role in helping users discover new offline events. Existing work on SEO only assumes that different users have different preferences towards different events, ignoring the fact that each event (its organizer) may have a separate preference towards every user. In this paper, we investigate joint user- and event- driven SEO by simultaneously considering user preferences (towards events) and event preferences (towards users). A risen challenging problem is that this joint consideration may suffer instabilities between users and events which are NP-hard to handle in SEO. Stability is a desired property that needs to be maintained in SEO, otherwise participants will incline towards changing to other events and trust less the organizer. To the best of our knowledge, we are the first to study SEO with both preferences from users to events and preferences from events to users being considered. In this work, we formulate the stable social event organization (Stable-SEO) problem and prove its NP-hardness. We then propose an efficient greedy heuristic algorithm to solve Stable-SEO, taking both user preferences and event preferences into account. The proposed approach is able to find an assignment of a group of users to a set of events for an event organization, in which there is a minimized number of users involved in user-event pairs such that both the user and event in one such pair will be better off when reassigning the user to this event. Our experiments on two real-world datasets demonstrate the strong superiority of our proposed approach over existing methods.