Miracle-dz/UniFAR-FTUs huggingface dataset:
Coordinated Jamming: A Privacy Preserving Method for C-RAN with Untrusted Radios

In this work, we study the uplink (UL) of a cloud radio access network (C-RAN), under the consideration of information privacy. In particular, we consider a system where the UL communication takes place with the presence of idle users (IUs), which act as the undesired information receivers. Moreover, the central processing unit (CU) utilizes remote radio units (RU)s belonging to the same operator, i.e., the trusted RUs, as well as the RUs belonging to other operators or private owners, i.e., the untrusted RUs. In order to preserve information privacy, we propose a coordinated jamming strategy, where the trusted RUs are enabled with full-duplex (FD) capability and transmit a coordinated jamming signal towards the exotic RUs and the IUs, while receiving and forwarding UL signal to the CU. An optimization problem is then formulated to maximize the sum uplink private information rate by jointly designing the fronthaul compression, as well as the information and jamming transmission strategies. Due to the intractability of the resulting mathematical problem, an iterative solution is proposed with convergence to a point satisfying the Karush-Kuhn-Tucker (KKT) optimality conditions. Numerical simulations illustrate a notable gain obtained via the proposed sharing mechanism under the consideration of information privacy.
ALS-SimVR: Advanced Life Support Virtual Reality Training Application

The delivery of ongoing training and support to Advanced Life Support (ALS) teams poses significant resourcing and logistical challenges. A reduced exposure to cardiac arrests and mandated re-accreditation pose further challenges for educators to overcome. This work presents the ALS-SimVR (Advanced Life Support Simulation in VR) application. The application is intended for use as a supplementary training and refresher asset for ALS team leaders. The purpose of the application is to allow critical care clinicians to rehearse the role of ALS Team leader in their own time and location of choice. The application was developed for the Oculus-Go and ported to the Oculus-Quest. The application is also supported for a desktop and server based streaming release.
Interpreting and Explaining Deep Models Visually

Methods based on deep neural networks (DNNs) have achieved impressive results for several computer vision tasks, such as image classification, object detection, and image generation, etc. Combined with the general tendency in the community of developing methods with a focus on high quantitative performance, this has motivated the wide adoption of DNN-based methods, despite the initial skepticism due to their blackbox characteristics. Our goal is to bridge the gap between methods aiming at model interpretation, i.e., understanding what a given trained model has actually learned, and methods aiming at model explanation, i.e., justifying the decisions made by a model.
Querying Continuous Recurrent Convoys of Interest

Moving objects equipped with location-positioning devices continuously generate a large amount of spatio-temporal trajectory data. An interesting finding over a trajectory stream is a group of objects that are travelling together for a certain period of time. Existing studies on mining co-moving objects do not consider an important correlation between co-moving objects, which is the reoccurrence of the movement pattern. In this study, we define a problem of finding recurrent pattern of co-moving objects from streaming trajectories and propose an efficient solution that enables us to discover recent co-moving object patterns repeated within a given time period. Experimental results on a real-life trajectory database show the efficiency of our method.
Application of deep learning for power control in the interference channel: a RNN-based approach

In this letter, a transmit power control architecture based on the recurrent neural network (RNN) is proposed to solve the power allocation problem for the interference channel to maximize the weighted sum-rate. Compared with the conventional power control schemes, such as weighted minimization mean squared error (WMMSE), which require a considerable number of computations, the RNN-based scheme not only requires much lower computational complexity, but also can well learn and characterize inter-user relationship in the interference channel. The simulation results show that the proposed scheme achieves the sum-rate close to the WMMSE-based scheme with much lower complexity and obtains higher sum-rate than the deep neural network (DNN)-based scheme with much less model parameters.
Impact of Gamified Interaction with Virtual Nature on Sustained Attention and Self-Reported Restoration  —  A Pilot Study

Interaction with nature in virtual reality has been shown to induce similar restorative benefits as interaction with real-life nature. Drawing from Attention restoration theory, restorative benefits from being in virtual nature are likely to be improved through greater active engagement techniques with specific virtual natural features. Gamification is the process of adding game design elements in non-game scenarios in order to improve engagement and motivation. In the present pilot study, six participants completed either a gamified interaction with virtual nature, one where game design elements had been added in order to improve engagement with specific virtual nature features and thus possibly further facilitating sustained attention and self-reported restoration, having them pick plants and gain rewards such as a higher level in return, or a non-gamified task, one where they explored the virtual nature environment and looked at plants at their own pace without any game design elements. Gamified interaction improved sustained attention restoration more than non-gamified interaction. Additionally, gamified interaction was also shown to have reduced negative effect in self-reported restoration more than non-gamified interaction. While there are still several limitations, gamified interaction with virtual nature seems to offer vast potential as an engagement technique in improving sustained attention and self-reported restoration.
Data Mining for ADHD & ASD Prediction Based on Resting-State fMRI Signals: A Literature Review

Despite ongoing research advances in the medical ﬁeld, mental disorders remain subject to the absence of a common etiology. The current diagnosis of the syndromes is based on descriptive criteria whose interpretation is often speciﬁc to the clinician. Biomarker research is thus of paramount importance to move forward towards a more objective psychiatric diagnosis. In children, a leap in the knowledge of Attention Deﬁcit Hy-peractivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) would allow to diagnose these highly prevalent disorders better and earlier. There is great hope that computational approaches may tackle this question, notably through the capabilities provided by modern Data Mining (DM). Diagnosis predictors are sought among features extracted from brain data such as resting-state functional Magnetic Resonance Imaging (rs-fMRI) signals. This paper aims to provide a comprehensive synthesis of the recent literature about DM for diagnosis prediction based on rs-fMRI signals extracted from the open and freely available ADHD-200 and ABIDE collections, related respectively to ADHD and ASD. We also present some perspectives for the development of diagnosis aid models with greater applicability and efﬁciency.
Learning Embeddings of Spatial, Textual and Temporal Entities in Geotagged Tweets

With online social networks being extended to geographical space, location context plays a key role in many applications such as local event detection and location recommendation. Geotagged tweets in Twitter serve as an invaluable source to understand people's activities in urban space. Analyzing geotagged tweets to identify implicit contexts among location, time and text is an interesting problem. In this paper, we present LeGo-CM, a methodology for Leearning embeddings of Geotagged tweets for Cross-Modal search such as locations, time units (hour-of-day and day-of-week) and textual words in tweets. The resulting compact vector representations of these entities make it easy to perform searches like "find which locations are mostly related to the given topics". In LeGo-CM, we first build a graph of entities extracted from tweets in which each edge carries the weight of co-occurrences between two entities. The embeddings of graph nodes are then learned in the same latent space under the guidance of approximating stationary residing probabilities between nodes which are computed using personalized random walk procedures. We evaluate LeGo-CM on datasets of New York City and Los Angeles, showing that the proposed method generally outperforms competitive baseline approaches.
Empirical Aspects of Advertiser Preferences and Program Content of Network Television

Abstract Economists have long debated how advertising should be treated in a rational-choice framework. Using unique data from the broadcast networks’ 1995 Fall Season, we find that sitcoms attract premia from advertisers, while news and police programs get discounted. We interpret our findings according to two important theoretical treatments of advertising. Possibly because the broadcast networks forgo advertising-unfriendly program content, the cable channel HBO responds with a deliberate counter-programming niche strategy, explicitly airing programming with “darker” and “more difficult” advertising-unfriendly content. *Senior Economist, Media Bureau. The views expressed are those of the author and do not necessarily reflect the views of the Media Bureau, the Commission, or other members of the Commission’s staff. ** Assistant Professor, Department of Public Administration. The views expressed are those of the author and do not necessarily reflect the views of other faculty, staff, administrators, or trustees of Florida International University. The authors thank Peter Alexander, Jerry Duvall, Kenneth Ferree, Daniel Shiman, and Tracy Waldon for valuable and helpful comments, and Vanessa Lemme for valuable assistance. All errors are our own.
Pneumonitis due to cytomegalovirus in an immunocompromised patient

Background: Cytomegalovirus (CMV) pneumonia is one of the most important infections in immunocompromised host. Immunosuppressive therapy plays a major role in reactivation of CMV. Patient: The patient was a 56-year old lady, known case of chronic lymphocytic leukemia (CLL), had been taking prednisolone and chlorambucil, who presented with dyspnea and productive cough. After bronchoalveolar lavage (BAL), transbronchial lung biopsy (TBLB) and CT-guided biopsy, CMV pneumonia was diagnosed. Conclusion: CMV should be suspected as a cause of pneumonia in immunocompromised patient and diagnosis may require invasive procedures.
An Overview of Mobile Edge Computing: Architecture, Technology and Direction

Modern applications such as augmented reality, connected vehicles, video streaming and gaming have stringent requirements on latency, bandwidth and computation resources. The explosion in data generation by mobile devices has further exacerbated the situation. Mobile Edge Computing (MEC) is a recent addition to the edge computing paradigm that amalgamates the cloud computing capabilities with cellular communications. The concept of MEC is to relocate the cloud capabilities to the edge of the network for yielding ultra-low latency, high computation, high bandwidth, low burden on the core network, enhanced quality of experience (QoE), and efficient resource utilization. In this paper, we provide a comprehensive overview on different traits of MEC including its use cases, architecture, computation offloading, security, economic aspects, research challenges, and potential future directions.
Antimicrobial photodynamic therapy in skin wound healing: A systematic review of animal studies

Bacterial infection is a common wound complication that can significantly delay healing. Classical local therapies for infected wounds are expensive and are frequently ineffective. One alternative therapy is photodynamic therapy (PDT). We conducted a systematic review to clarify whether PDT is useful for bacteria‐infected wounds in animal models. PubMed and Medline were searched for articles on PDT in infected skin wounds in animals. The language was limited to English. Nineteen articles met the inclusion criteria. The overall study methodological quality was moderate, with a low‐moderate risk of bias. The animal models were mice and rats. The wounds were excisional, burn, and abrasion wounds. Wound size ranged from 6 mm in diameter to 1.5 × 1.5 cm2. Most studies inoculated the wounds with Pseudomonas aeruginosa or methicillin‐resistant Staphylococcus aureus. Eleven and 17 studies showed that the PDT of infected wounds significantly decreased wound size and bacterial counts, respectively. Six, four, and two studies examined the effect of PDT on infected wound‐cytokine levels, wound‐healing time, and body weight, respectively. Most indicated that PDT had beneficial effects on these variables. PDT accelerated bacteria‐infected wound healing in animals by promoting wound closure and killing bacteria.
Mitochondrial-specific autophagy linked to mitochondrial dysfunction following traumatic freeze injury in mice.

The objective of this study was to interrogate the link between mitochondrial dysfunction and mitochondrial-specific autophagy in skeletal muscle. C57BL/6J mice were used to establish a timecourse of mitochondrial function and autophagy induction after fatigue (n=12), eccentric contraction-induced injury (n=20), or traumatic freeze injury (FI, n=28); and only FI resulted in a combination of mitochondrial dysfunction, i.e., decreased mitochondrial respiration, and autophagy induction. Moving forward, we tested the hypothesis that mitochondrial-specific autophagy is important for the timely recovery of mitochondrial function after FI. Following FI, there is a significant increase in several mitochondrial-specific autophagy-related protein contents including Drp1, BNIP3, Pink1, and Parkin (~2-fold, p<0.02). Also, mitochondrial-enriched fractions from FI muscles showed LC3II co-localization suggesting autophagosome assembly around the damaged mitochondrial. Unc-51 like autophagy activating kinase (Ulk1) is considered necessary for mitochondrial-specific autophagy and herein we utilized a mouse model with Ulk1 deficiency in adult skeletal muscle (myogenin-cre). While Ulk1 knockouts had contractile weakness compared to littermate controls (-27%, p<0.02) the recovery of mitochondrial function was not different, and this may be due in part to a partial rescue of Ulk1 protein content within the regenerating muscle tissue of knockouts from differentiated satellite cells in which Ulk1 was not genetically altered via myogenin-cre. Lastly, autophagy flux was significantly less in injured vs. uninjured muscles (-26%, p<0.02) despite the increase in autophagy-related protein content. This suggests autophagy flux is not upregulated to match increases in autophagy machinery after injury and represents a potential bottleneck in the clearance of damaged mitochondria by autophagy.
Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation.

BACKGROUND Optical sensors on wearable devices can detect irregular pulses. The ability of a smartwatch application (app) to identify atrial fibrillation during typical use is unknown. METHODS Participants without atrial fibrillation (as reported by the participants themselves) used a smartphone (Apple iPhone) app to consent to monitoring. If a smartwatch-based irregular pulse notification algorithm identified possible atrial fibrillation, a telemedicine visit was initiated and an electrocardiography (ECG) patch was mailed to the participant, to be worn for up to 7 days. Surveys were administered 90 days after notification of the irregular pulse and at the end of the study. The main objectives were to estimate the proportion of notified participants with atrial fibrillation shown on an ECG patch and the positive predictive value of irregular pulse intervals with a targeted confidence interval width of 0.10. RESULTS We recruited 419,297 participants over 8 months. Over a median of 117 days of monitoring, 2161 participants (0.52%) received notifications of irregular pulse. Among the 450 participants who returned ECG patches containing data that could be analyzed - which had been applied, on average, 13 days after notification - atrial fibrillation was present in 34% (97.5% confidence interval [CI], 29 to 39) overall and in 35% (97.5% CI, 27 to 43) of participants 65 years of age or older. Among participants who were notified of an irregular pulse, the positive predictive value was 0.84 (95% CI, 0.76 to 0.92) for observing atrial fibrillation on the ECG simultaneously with a subsequent irregular pulse notification and 0.71 (97.5% CI, 0.69 to 0.74) for observing atrial fibrillation on the ECG simultaneously with a subsequent irregular tachogram. Of 1376 notified participants who returned a 90-day survey, 57% contacted health care providers outside the study. There were no reports of serious app-related adverse events. CONCLUSIONS The probability of receiving an irregular pulse notification was low. Among participants who received notification of an irregular pulse, 34% had atrial fibrillation on subsequent ECG patch readings and 84% of notifications were concordant with atrial fibrillation. This siteless (no on-site visits were required for the participants), pragmatic study design provides a foundation for large-scale pragmatic studies in which outcomes or adherence can be reliably assessed with user-owned devices. (Funded by Apple; Apple Heart Study ClinicalTrials.gov number, NCT03335800.).
The role of chick Ebf genes in the mediolateral patterning of the somites

This study was conducted to check whether the three chick Early B‐cell Factor (Ebf) genes, particularly cEbf1, would be targets for Shh and Bmp signals during somites mediolateral (ML) patterning. Tissue manipulations and gain and loss of function experiments for Shh and Bmp4 were performed and the results revealed that cEbf1 expression was initiated in the cranial presomitic mesoderm by low dose of Bmp4 from the lateral mesoderm and maintained in the ventromedial part of the epithelial somite and the medial sclerotome by Shh from the notochord; while cEbf2/3 expression was induced and maintained by Bmp4 and inhibited by high dose of Shh. To determine whether Ebf1 plays a role in somite patterning, transfection of a dominant‐negative construct was carried out; this showed suppression of cPax1 expression in the medial sclerotome and upregulation and medial expansion of cEbf3 and cPax3 expression in sclerotome and dermomyotome, respectively, suggesting that Ebf1 is important for ML patterning. Thus, it is possible that low doses of Bmp4 set up Ebf1 expression which, together with Shh from the notochord, leads to establishment of the medial sclerotome and suppression of lateral identities. These data also conclude that Bmp4 is required in both the medial and lateral domain of the somitic mesoderm to keep the ML identity of the sclerotome through maintenance of cEbf gene expression. These striking findings are novel and give a new insight on the role of Bmp4 on mediolateral patterning of somites.
Ras inhibits TGF‐β‐induced KLF5 acetylation and transcriptional complex assembly via regulating SMAD2/3 phosphorylation in epithelial cells

Acetylated Kruppel‐like factor 5 (KLF5) is essential for transforming growth factor‐β (TGF‐β) to properly regulate gene transcription in the inhibition of cell proliferation and tumor growth. Ras oncogenic signaling can convert TGF‐β from a tumor suppressor to a tumor promoter; however, its ability to utilize the KLF5 transcription factor to modulate TGF‐β functions is still unknown. Therefore, in this study, we sought to determine whether Ras signaling altered TGF‐β‐induced KLF5 acetylation and the assembly of the p300‐KLF5‐SMADs transcriptional complex in gene regulation. Not only did we determine that Ras signaling inhibited TGF‐β‐induced KLF5 acetylation and interfered with TGF‐β function in p15 induction and Myc repression, but also TGF‐β‐induced SMAD3 C‐terminal region phosphorylation was necessary for TGF‐β to induce KLF5 acetylation. Moreover, Ras activation further interrupted the interactions amongst p300, KLF5, and SMAD4, as well as the binding of p300‐KLF5‐SMADs complex onto the TGF‐β‐responsive promoter elements for both p15 and Myc. These findings suggested that KLF5 mediated the crosstalk between TGF‐β and Ras signaling, and that suppression of TGF‐β‐induced KLF5 acetylation by Ras activation; this altered TGF‐β‐induced assembly of p300‐KLF5‐SMADs complex onto gene promoters to convert the function of TGF‐β in gene regulation.
RELATIONSHIP AMONG DIFFERENT TESTS OF EVALUATING LOW BACK CORE STABILITY

Purpose: This study aims to determine the relation among the commonly used different tests of evaluating core stability performance. Establishing this relationship is important as different authors have used different types of tests for evaluating core stabilization in their study yet relation among these tests and the individual importance of each of these tests is not clearly established so far. Methods: Among 40 recreationally active subjects the core stability performance was evaluated using variety of different tests reported in the previous literatures. Results: Results show that there was no
Order‐restricted inference for clustered ROC data with application to fingerprint matching accuracy

Receiver operating characteristic (ROC) curve is commonly used to evaluate and compare the accuracy of classification methods or markers. Estimating ROC curves has been an important problem in various fields including biometric recognition and diagnostic medicine. In real applications, classification markers are often developed under two or more ordered conditions, such that a natural stochastic ordering exists among the observations. Incorporating such a stochastic ordering into estimation can improve statistical efficiency (Davidov and Herman, 2012). In addition, clustered and correlated data arise when multiple measurements are gleaned from the same subject, making estimation of ROC curves complicated due to within‐cluster correlations. In this article, we propose to model the ROC curve using a weighted empirical process to jointly account for the order constraint and within‐cluster correlation structure. The algebraic properties of resulting summary statistics of the ROC curve such as its area and partial area are also studied. The algebraic expressions reduce to the ones by Davidov and Herman (2012) for independent observations. We derive asymptotic properties of the proposed order‐restricted estimators and show that they have smaller mean‐squared errors than the existing estimators. Simulation studies also demonstrate better performance of the newly proposed estimators over existing methods for finite samples. The proposed method is further exemplified with the fingerprint matching data from the National Institute of Standards and Technology Special Database 4.