Miracle-dz/UniFAR-FTUs huggingface dataset:
Accelerating Handheld Object Pose Estimation on Smartphones in Unity with TensorFlow Lite

Recent developments in computer vision methods and techniques were boosted by the application of machine learning methods to increase precision. In particular, CV techniques found a good number of applications with relation to augmented and mixed reality (AR/MR). However, implementation depends on the frameworks which are often aimed at desktop machines, not mobile devices. In this work, we discuss the 6DoF tracking of handheld objects, which is of particular interest to augmented reality applications. This field requires precise pose estimation obtained in real-time to ensure user-friendly interaction with mixed reality scenes. Also, we focus on smartphones as our target devices, which requires our approach to be low-resource and computationally efficient. In particular, we consider the tracking of a handheld hair curler, to train inexperienced users in its proper operation. We use the C API and JNI of TensorFlow Lite and leverage its Android and iOS GPU support, as well as Unity3D’s support for complex AR scenes. Rapid pose inference is implemented on both smartphone GPUs and MacOS/Windows CPUs through a native C++ Unity3D plugin. Additionally, several custom or off-the-shelf were run through the plugin, with minimal code changes to the Unity scripts.
Automatic Triage and Analysis of Online Suicide Risk with Document Embeddings and Latent Dirichlet Allocation

Machine learning is applied to a dataset of the suicidality of Reddit users in which the suicide risk labels were derived from knowledge of expert clinicians. We present the results of machine learning models based on transfer learning from document embeddings trained on large external corpora, and find that they have very high F1 scores (.83 − .92) in distinguishing which users are most at risk of committing suicide. Thus, these models could potentially provide valuable aid in triaging care for individuals most in danger. We compare the document embedding approach with one which incorporates expert domain knowledge. Word importance is assessed as a way of suggesting signs that could indicate suicide risk in online posts. Finally, we learn a Latent Dirichlet Allocation (LDA) topic model and find that suicidal users post about different topics to the rest of Reddit than non-suicidal users.
A Meta-Analysis of Robotic Surgery in Endometrial Cancer: Comparison with Laparoscopy and Laparotomy

Background The safety and effectiveness of robotic surgery are evaluated by comparing perioperative outcomes with laparoscopy and laparotomy in endometrial cancer. Method PubMed, MEDLINE, Embase, Cochrane, and other databases were searched for eligible studies up to April 2019. Studies that compared robotic surgery with laparoscopy or laparotomy in surgical staging of endometrial cancer were included. The pooled odds ratio and weighted mean difference were calculated using a random-effects or a fixed-effects model to summarize the results. Results Twenty-seven articles were ultimately included, with one randomized controlled trial and 26 observational studies. A total of 6568 patients were included. Meta-analysis showed that robotic surgery had less estimated blood loss (P < 0.001), blood transfusion (P = 0.04), intraoperative complications (P = 0.001), and conversion to open surgery (P = 0.001), and a shorter hospital stay (P = 0.001), but had a longer operation time (P = 0.04) in surgical staging of endometrial cancer compared with laparoscopy. There were no significant differences in postoperative complications, the total number of lymph nodes harvested, the number of pelvic lymph nodes harvested, and the number of para-aortic lymph nodes harvested between techniques. Robotic surgery had a longer operation time (P = 0.008), less estimated blood loss (P < 0.001), blood transfusion (P < 0.001), and postoperative complications (P < 0.001), and a shorter hospital stay (P < 0.001) compared with laparotomy. There were no significant differences in other variables between techniques. Conclusion Robotic surgery is a safer surgical approach than laparoscopy and laparotomy in surgical staging of endometrial cancer, with less estimated blood loss, blood transfusion, and conversion, and the same number of lymph nodes harvested.
OPTH_A_232548 2563..2572

Jeremy B Wingard Darcie AP Delzell Nathan V Houlihan Jonathan Lin Jon P Gieser 1Wheaton Eye Clinic, Wheaton, IL 60187, USA; 2Mathematics and Computer Science Department, Wheaton College, Wheaton, IL, 60187, USA Purpose: To estimate the risk of glaucoma or sustained ocular hypertension (OHT) related to anti-vascular endothelial growth factor (VEGF) injections for age-related macular degeneration (AMD). Design: Retrospective chart review. Subjects: Patients who received unilateral anti-VEGF injections for AMD at the Wheaton Eye Clinic (IL). Methods: Chart analysis was performed on 1095 patients, without prior glaucoma or OHT, who received unilateral anti-VEGF injections for AMD from 2005 to 2012, with data collected through 2013. Data collection included demographics, lens status, date and medication type of each injection, and the date of diagnosis of glaucoma or OHT by a treating glaucoma specialist, which was the main outcome measure. Rare events logistic regression was performed to determine the risk of disease development based on sex, lens status, and injection frequency. Results: Unilateral glaucoma or sustained OHT developed in 42 patients over the course of follow-up, with 40 events in the injected eye only, 2 in the contralateral eye only. Statistical modeling predicted elevated risk for onset of glaucomatous disease with a higher maximum frequency of injections (p < 0.0001, odds ratio [OR] 2.18 for each additional injection over the most injection-intense 6 months for a given subject) and with phakic lens status (p = 0.0009, OR 0.33 for pseudophakia). Conclusion: Our results show a significant risk for glaucoma or OHT development in patients undergoing repeated treatments with intravitreal anti-VEGF injections for AMD, establishing the first reliable connection between disease development and a period of highfrequency injections. In addition, we show a significantly increased risk of disease development in phakic patients, which we believe points to a mechanical explanation for this type of secondary glaucoma.
ESTIMATION OF ONLINE PURCHASING INTENTION USING DECISION TREE

Tuketici davranislarini tahmin etmek cok fazla degiskene bagli olmasi nedeniyle oldukca zordur. Ayni zamanda cevrimici tuketiciyle geleneksel tuketici arasinda farkliliklar bulunmaktadir. Online tuketicilerin satin alma davranisini tahmine yonelik bir suredir calismalar olmakla birlikte cok sayida ozellige sahip buyuk verilere ihtiyac duyulmaktadir. Buyuk verilerin, kisisel bilgilere yonelik kisitlamalar nedeniyle elde edilmesi zor olmakta ve analiz sistemlerini uzun sure calismasina sebep olmaktadir. Bu baglamda, calismada online tuketicilerin satin alma davranisini daha az veriyle tahmin ederek anlamli bir kural olusturmak amaclanmistir. Guncel ve acik bir veri tabaninda Fisher skor ozellik secme yapildiktan sonra K fold ile egitim ve test verileri belirlenerek karar agaci ile kural olusturulmustur. Sonuc olarak tek bir ozellik kullanilarak cevrimici tuketicinin satin alma davranisinin yuksek dogruluk oraniyla tespitinin mumkun oldugu gorulmustur.
PRBM_A_228007 89..96

Tingting Yang Lan Guo Feng Hong Ziyun Wang Yao Yu Ciyong Lu 1School of Public Health, The Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang 550025, People’s Republic of China; 2Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-Sen University, Guangzhou 510080, People’s Republic of China; 3The Affiliated Hospital of Guizhou Medical University, Guiyang 550004, People’s Republic of China Background: Suicide has become the leading cause of death among 15–34 years old in China, and more attention should be paid to risk factors and prevention of suicidal ideation and suicide attempts. The school-based survey was conducted to test the associations of bullying behavior with suicidal ideation and suicide attempts, and to assess whether the biological gender can moderate these associations. Methods: A multistage stratified cluster sampling method was used to recruit participants from high schools in Guizhou, China, and univariate and multivariate logistic regression models were fitted. Results: A total of 23,392 students were included in the analysis, and 45.4% were boys. The mean age of the students was 15.2 (SD: 1.9) years. Overall, 3.8% (882) of the students were victims, 4.9% (1144) were bullies, and 3.2% (736) were bully-victims. A total of 18.9% of students reported having suicidal ideation, and 3.9% reported having suicide attempts. Victims (adjusted odds ratio [AOR]=2.15, 95% CI=1.84–2.51), bullies (AOR=3.17, 95% CI=2.78–3.62), and bully-victims (AOR=3.18, 95% CI=2.71–3.73) were at an elevated risk of suicidal ideation than neutrals; similar associations can be found in the associations between bullying behavior and suicide attempts. Further stratification analyses showed that the associations of being bullied or bullying others with suicidal ideation and suicide attempts were slightly stronger in girls than in boys. Conclusion: Prevention or intervention programs are recommended to take the biological gender into consideration, and future studies are also warranted to investigate the reasons of differences by gender in the association of bullying and suicidal behavior.
The Fading Number of Multiple-Access Rician Fading Channel with Memory

The Fading Number of Multiple-Access Rician Fading Channel with Memory Student: Chou Yu-Hsing Advisor: Prof. Stefan M. Moser Institute of Communications Engineering National Chiao Tung University In this thesis we analyze the sum-rate capacity of the Rician fading multiple-access channel (MAC) with memory. The fading process of the channel is Gaussian in addition to a line-of-sight component. Moreover, there are more than one user sending data at the same time. To simplify our analysis, we consider the single-input single-output (SISO) case, i.e., all the transmitters and the receiver use one antenna. In the analysis of the fading channel capacity, the exact expression of the capacity is not yet known. A way called asymptotic analysis is used to derive the channel capacity in the limit when the available power tends to infinity. It is shown that at high signal-to-noise ratio (SNR), the sum-rate capacity grows to infinity doublelogarithmically. The second term in the high-SNR expansion is a constant called fading number. In our work, we derive an upper bound on the fading number of the general m-user SISO Rician fading MAC with memory. Combining the natural lower bound on the fading number of the single-user SISO channel, we then obtain the exact fading number of the general m-user SISO Rician fading MAC with memory. To achieve the fading number, we have to switch off the worse users and allow the best users communicate by time-sharing.
Preparedness and vulnerability of African countries against introductions of 2019-nCoV

The novel coronavirus (2019-nCoV) epidemic has spread to 23 countries from China. Local cycles of transmission already occurred in 7 countries following case importation. No African country has reported cases yet. The management and control of 2019-nCoV introductions heavily relies on the public health capacity of a country. Here we evaluate the preparedness and vulnerability of African countries against their risk of importation of 2019-nCoV. We used data on air travel volumes departing from airports in the infected provinces in China and directed to Africa to estimate the risk of introduction per country. We determined the countries capacity to detect and respond to cases with two indicators: preparedness, using the WHO International Health Regulation Monitoring and Evaluation Framework; and vulnerability, with the Infectious Disease Vulnerability Index. Countries were clustered according to the Chinese regions contributing the most to their risk. Findings: Countries at the highest importation risk (Egypt, Algeria, Republic of South Africa) have moderate to high capacity to respond to outbreaks. Countries at moderate risk (Nigeria, Ethiopia, Sudan, Angola, Tanzania, Ghana, Kenya) have variable capacity and high vulnerability. Three clusters of countries are identified that share the same exposure to the risk originating from the provinces of Guangdong, Fujian, and Beijing, respectively. Interpretation: Several countries in Africa are stepping up their preparedness to detect and cope with 2019-nCoV importations. Resources and intensified surveillance and capacity capacity should be urgently prioritized towards countries at moderate risk that may be ill-prepared to face the importation and to limit onward transmission.
Viscovery: A Platform for Trend Tracking in Opinion Forums

Opinions in forums and social networks are released by millions of people due to the increasing number of users that use Web 2.0 platforms to opine about brands and organizations. For enterprises or government agencies it is almost impossible to track what people say producing a gap between user needs/expectations and organizations actions. To bridge this gap we create Viscovery, a platform for opinion summarization and trend tracking that is able to analyze a stream of opinions recovered from forums. To do this we use dynamic topic models, allowing to uncover the hidden structure of topics behind opinions, characterizing vocabulary dynamics. We extend dynamic topic models for incremental learning, a key aspect needed in Viscovery for model updating in near-real time. In addition, we include in Viscovery sentiment analysis, allowing to separate positive/negative words for a specific topic at different levels of granularity. Viscovery allows to visualize representative opinions and terms in each topic. At a coarse level of granularity, the dynamic of the topics can be analyzed using a 2D topic embedding, suggesting longitudinal topic merging or segmentation. In this paper we report our experience developing this platform, sharing lessons learned and opportunities that arise from the use of sentiment analysis and topic modeling in real world applications.
Predictive value of plasma lipid levels for coronary artery disease (CAD)

Coronary artery disease (CAD) is a major cause of mortality and morbidity all over the world including Pakistan. CAD events are caused by the interplay of genetic and environmental factors, the effects of which are mainly mediated through cardiovascular risk factors. The major risk factors for CAD include hypertension, dyslipidemia, family history of premature atheroscleclerotic disease, obesity and sedentary lifestyle. However, it is clear from the existing literature that dyslipidemia plays a central role. This study was designed to examine the plasma lipid profile in coronary artery disease patients of both sexes receiving clinical care at various government hospitals in Lahore. Blood samples were collected from a total of 500 CAD patients and 250 healthy (control) subjects and subsequently analyzed for total cholesterol (TC), triglyceride (TG) and very low-density lipoprotein (VLDL), low-density lipoprotein (LDL) and high-density lipoprotein cholesterol (HDL) levels. These measurements showed that TC, LDL, TG and VLDL levels were significantly elevated, whereas HDL levels were significantly reduced in CAD patients as compared to ethnically matched controls ( p < 0.0001). However, lipid levels did not differ significantly between the male and female patients. Furthermore, the lipid profile parameters were strongly associated with CAD status and association of LDL/HDL remained significant even after making an adjustment for confounders. In addition, patients with comorbidities exhibited severe lipid abnormalities. In conclusion, this study revealed elevated plasma TG, TC, VLDL and LDL levels and attenuated plasma HDL levels in the Pakistani patients with coronary artery disease. Also, plasma lipid levels have the potential to serve as biomarkers for CAD in Pakistani population.
Molecular and morphological identification of Anthocoris spp. (Hemiptera: Anthocoridae) predators of three economically important psyllid species in Razavi Khorasan province, Northeastern Iran

Although correct and precise identification of natural enemies is the bedrock of biological control, traditional identification methods for species may often rely on mundane and time-consuming dissection and rearing methods. The Anthocoris species complex associated with common psyllid species (Cacopsylla pyricola, Agonoscena pistaciae and Psyllopsis repens) in northeastern Iran was investigated. Three species including A. minki (two subspecies: A. minki minki and A. minki pistaciae), A. guentheri and A. nemoralis were identified morphologically (based on male genitalia and other structures used in taxonomic treatments of this genus) and by COI sequence analyses. Species-specific PCR primers were designed and utilized in a multiplex PCR assay for simultaneous identification of species. The primers amplified uniquely sized, species-specific PCR products for each species allowing identification of all species in a single reaction. This multiplex PCR experiments resulted in accurate and swift identification and discrimination of Anthocoris specimens found on main psyllid species in northeastern Iran.
Building the Capability for Benefits realisation: Leading with Benefits

It is now 20 years since the publication of the paper that launched Benefits Management (BM), by Prof. John Ward and colleagues, and the launch of an open course on BM at Cranfield. This paper draws on recent work reflecting on the translation of BM into practice as well as a 10+ year programme of research seeking to understand how to develop the Benefits Realisation Capability of an organisation. In particular it draws on two longitudinal studies exploring the adoption of benefits-driven approaches. An important lesson is that BM needs to reflect a new mindset and is not simply another technique to add to our project management toolbox. Without a shift in mindset, BM becomes another technical, rational, approach that brings a new bureaucracy to the management of Information Technology (IT) investments when the need is speed to benefit. BM is primarily about people – about vision, engagement and motivation. It can contribute to the craft of leading change and help build an organisational change capability.
Automatic Estimation of the Triangular Vowel Space Area from i-Vectors

Parkinson’s Disease (PD) is a neurodegenerative disorder which gradually effects the neurological condition of the patient. In many cases the disease impairs the reliability of the articulatory system and the ability to pronounce vowels normally. One prominent way to measure the degree of the functioning of the articulatory system is the Vowel Space Area (VSA). However, the typical way to measure it, is to manually annotate sustained vowel recordings or phonetically annotated speech utterances of a speaker and then analyze the signals. However, it is often desirable to measure the VSA directly from unlabeled natural speech. Therefore an automatic model-based system is proposed in this paper to estimate the triangular Vowels Space Area (tVSA) and the underlying corner vowel formant frequencies directly from unlabeled natural speech. The proposed algorithm is able to estimate the tVSA automatically from the speech signals without the need of phonetical or vowel transcriptions. The i-Vectors are extracted from the signals as the speaker’s characteristic representation, from which the speaker’s corner vowel formant frequencies are estimated by regression classiﬁers. Two regression classiﬁers, namely Deep Neural Networks (DNN) and Support Vector Regression (SVR), are investigated in this work. The proposed conﬁguration employs the SVR classiﬁer, which is able to predict the corner vowel formant frequencies of the test speakers with R 2 up to 0.56719 and ρ up to 0.76485.
Many-Body Dynamics and Decoherence of the XXZ Central Spin Model in External Magnetic Field

The many-body dynamics of an electron spin−1/2 qubit coupled to a bath of nuclear spins by hyperfine interactions, as described by the central spin model in two kinds of external field, are studied in this paper. In a completely polarized bath, we use the state recurrence method to obtain the exact solution of the XXZ central spin model in a constant magnetic field and numerically analyze the influence of the disorder strength of the magnetic field on fidelity and entanglement entropy. For a constant magnetic field, the fidelity presents non-attenuating oscillations. The anisotropic parameter λ and the magnetic field strength B significantly affect the dynamic behaviour of the central spin. Unlike the periodic oscillation in the constant magnetic field, the decoherence dynamics of the central spin act like a damping oscillation in a disordered field, where the central spin undergoes a relaxation process and eventually reaches a stable state. The relaxation time of this process is affected by the disorder strength and the anisotropic parameter, where a larger anisotropic parameter or disorder strength can speed up the relaxation process. Compared with the constant magnetic field, the disordered field can regulate the decoherence over a large range, independent of the anisotropic parameter.
Introduction to Extreme Seeking Entropy

Recently, the concept of evaluating an unusually large learning effort of an adaptive system to detect novelties in the observed data was introduced. The present paper introduces a new measure of the learning effort of an adaptive system. The proposed method also uses adaptable parameters. Instead of a multi-scale enhanced approach, the generalized Pareto distribution is employed to estimate the probability of unusual updates, as well as for detecting novelties. This measure was successfully tested in various scenarios with (i) synthetic data, (ii) real time series datasets, and multiple adaptive filters and learning algorithms. The results of these experiments are presented.
Detection of Hypoglycemia Using Measures of EEG Complexity in Type 1 Diabetes Patients

Previous literature has demonstrated that hypoglycemic events in patients with type 1 diabetes (T1D) are associated with measurable scalp electroencephalography (EEG) changes in power spectral density. In the present study, we used a dataset of 19-channel scalp EEG recordings in 34 patients with T1D who underwent a hyperinsulinemic–hypoglycemic clamp study. We found that hypoglycemic events are also characterized by EEG complexity changes that are quantifiable at the single-channel level through empirical conditional and permutation entropy and fractal dimension indices, i.e., the Higuchi index, residuals, and tortuosity. Moreover, we demonstrated that the EEG complexity indices computed in parallel in more than one channel can be used as the input for a neural network aimed at identifying hypoglycemia and euglycemia. The accuracy was about 90%, suggesting that nonlinear indices applied to EEG signals might be useful in revealing hypoglycemic events from EEG recordings in patients with T1D.