Miracle-dz/UniFAR-FTUs huggingface dataset:
Flexible-Cm GAN: Towards Precise 3D Dose Prediction in Radiotherapy

Deep learning has been utilized in knowledge-based radiotherapy planning in which a system trained with a set of clinically approved plans is employed to infer a three-dimensional dose map for a given new patient. However, previous deep methods are primarily limited to simple scenarios, e.g., a fixed planning type or a consistent beam angle configuration. This in fact limits the usability of such approaches and makes them not generalizable over a larger set of clinical scenarios. Herein, we propose a novel conditional generative model, Flexible-Cm GAN, utilizing additional information regarding planning types and various beam geometries. A miss-consistency loss is proposed to deal with the challenge of having a limited set of conditions on the input data, e.g., incomplete training samples. To address the challenges of including clinical preferences, we derive a differentiable shift-dose-volume loss to incorporate the well-known dose-volume histogram constraints. During inference, users can flexibly choose a specific planning type and a set of beam angles to meet the clinical requirements. We conduct experiments on an illustrative face dataset to show the motivation of Flexible-Cm GAN and further validate our model's potential clinical values with two radiotherapy datasets. The results demonstrate the superior performance of the proposed method in a practical heterogeneous radiotherapy planning application compared to existing deep learning-based approaches.
Using Big Data Analytics to Optimize Practical Large Databases Using Big Data Analytics to Optimize Practical Large Databases

Big data analytics is gaining popularity for enterprises in optimizing their business processes ranging from retailers, supply chains, to online shopping stores. Existing practical raw data are far from usable to achieve the goal. Therefore, a good data pre-processing approach is required and that is a key step to success. We study on the effectiveness of data pre-processing with respect to the optimal business process based on a real world database. Our methodology involves natural language processing and semantic analysis. Our key goal is to study appropriate methods with big data analysis techniques that can handle errors, ambiguity, and repeated descriptions caused by human languages. In this thesis, we study a simple language similarity checking in order to optimize the databases used in daily business process. A logical representation system is built from our database as a proof of concepts.
Vehicle fleet emissions of black carbon, polycyclic aromatic hydrocarbons, and other pollutants measured by a mobile laboratory in Mexico City

Abstract. Black carbon (BC) and polycyclic aromatic hydrocarbons (PAHs) are of concern due to their effects on climate and health. The main goal of this research is to provide the first estimate of emissions of BC and particle-phase PAHs (PPAHs) from motor vehicles in Mexico City. The emissions of other pollutants including carbon monoxide (CO), oxides of nitrogen (NOx), volatile organic compounds (VOCs), and particulate matter of diameter 2.5 μm and less (PM2.5) are also estimated. As a part of the Mexico City Metropolitan Area field campaign in April 2003 (MCMA-2003), a mobile laboratory was driven throughout the city. The laboratory was equipped with a comprehensive suite of gas and particle analyzers, including an aethalometer that measured BC and a photoionization aerosol sensor that measured PPAHs. While driving through traffic, the mobile lab continuously sampled exhaust plumes from the vehicles around it. We have developed a method of automatically identifying exhaust plumes, which are then used as the basis for calculation of fleet-average emissions. In the approximately 75 h of on-road sampling during the field campaign, we have identified ~30 000 exhaust measurement points that represent a variety of vehicle types and driving conditions. The large sample provides a basis for estimating fleet-average emission factors and thus the emission inventory. Motor vehicles in the Mexico City area are estimated to emit 1700±200 metric tons BC, 57±6 tons PPAHs, 1 190 000±40 000 tons CO, 120 000±3000 tons NOx, 240 000±50 000 tons VOCs, and 4400±400 tons PM2.5 per year, not including cold start emissions. The estimates for CO, NOx, and PPAHs may be low by up to 10% due to the slower response time of analyzers used to measure these species. Compared to the government's official motor vehicle emission inventory for the year 2002, the estimates for CO, NOx, VOCs, and PM2.5 are 38% lower, 23% lower, 27% higher, and 25% higher, respectively. The distributions of emission factors of BC, PPAHs, and PM2.5 are highly skewed, i.e. asymmetric, while those for benzene, measured as a surrogate for total VOCs, and NOx are less skewed. As a result, the total emissions of BC, PPAHs, and PM2.5 could be reduced by approximately 50% if the highest 20% of data points were removed, but "super polluters" are less influential on overall NOx and VOC emissions.
Minimally-Invasive Spine Surgery

I recent decades, an increasing number of spinal surgical approaches and techniques have been developed that limit approach-related morbidity to the paraspinal musculature (44,60). Minimally invasive spine (MIS) surgery encompasses a variety of techniques designed to improve perioperative measures (e.g. blood loss, postoperative pain narcotic usage, recovery time) and longterm functional outcomes. Large, randomized, controlled trials (RCTs) regarding the comparative outcomes of MIS surgery vs. traditional or open spinal surgeries are sparse. Many retrospective studies, case series, smaller prospective studies, and some large non-randomized series suggest that for certain indications, MIS surgery is equally safe and efficacious compared to open approaches, and furthermore, may have superior perioperative measures such as blood loss, length of stay, and postoperative pain (21,22,38,41,42, 47,55,58,80,81).
Detection of Pre- and Post-Trigger Atrial Fibrillation in Long-Term Photoplethysmogram Signals Acquired in Free-Living

Modifiable factors, such as alcohol or physical exertion, may trigger atrial fibrillation (AF) episodes. Identifying and eliminating these triggers can lead to effective strategies which reduce risk of AF recurrence. This study aims to evaluate pre- and post-trigger AF in long-term photo-plethysmogram (PPG) signals obtained during daily living from patients with paroxysmal AF. Thirty-seven patients were instructed to wear a wrist-worn device for a week. They were also asked to log suspected triggers using a smartphone app. Of these patients, 15 experienced AF episodes, resulting in an average AF burden of 0.15. The results indicate that longer post-trigger analysis time intervals resulted in better performance of PPG-based AF detection. The sensitivity was highest for the 16-h post-trigger interval (0.76) and lowest for the 4-h interval (0.43). In contrast, the specificity slightly decreased with an increasing longer post-trigger analysis time interval, being 0.98 and 0.95 for the 4-h and 16-h intervals, respectively. The PPG-based post-trigger AF burden was approximately half of that determined by the annotated ECG-based AF pattern. The study suggests that long-term PPG-based monitoring is a suitable alternative for detecting post-trigger AF instead of ECG-based. However, the accuracy of AF burden estimation using PPG-based technology still calls for improvement.
A cross-dialect acoustic description of vowels : Brazilian and European Portuguese

This paper examines four acoustic correlates of vowel identity in Brazilian and European Portuguese: first formant (F1), second formant (F2), duration, and fundamental frequency (F0). Both varieties of Portuguese display some universal phenomena: vowel-intrinsic duration, vowel-intrinsic pitch, gender-dependent size of the vowel space, gender-dependent duration, and an asymmetry in F1 between front and back vowels. Also, the average difference between the vocal tract sizes for /i/ and /u/, as measured from formant analyses, is comparable to the average difference between male and female vocal tract sizes. A languagespecific phenomenon is that both varieties of Portuguese have a large vowelintrinsic duration effect. Differences between Brazilian Portuguese (BP) and European Portuguese (EP) are found in duration (BP has longer stressed vowels than EP), in F1 (the lower mid vowels and possibly the low vowel are more open in BP than in EP), and in the size of the intrinsic pitch effect (larger for BP than for EP).
Iterative maximum a posteriori (IMAP)-DOAS for retrieval of strongly absorbing trace gases: Model studies for CH 4 and CO 2 retrieval from near infrared spectra of SCIAMACHY onboard ENVISAT

In the past, differential optical absorption spectroscopy (DOAS) has mostly been employed for atmospheric trace gas retrieval in the UV/Vis spectral region. New spectrometers such as SCIAMACHY onboard ENVISAT also provide near infrared channels and thus allow for the detection of greenhouse gases like CH_4, CO_2, or N_2O. However, modifications of the classical DOAS algorithm are necessary to account for the idiosyncrasies of this spectral region, i.e. the temperature and pressure dependence of the high resolution absorption lines. Furthermore, understanding the sensitivity of the measurement of these high resolution, strong absorption lines by means of a non-ideal device, i.e. having finite spectral resolution, is of special importance. This applies not only in the NIR, but can also prove to be an issue for the UV/Vis spectral region.
Economic Commission for Europe Conference of European Statisticians Joint Eurostat/UNECE Work Session on Demographic Projections

Estimates of future mortality often prove inaccurate, as conventional mortality projection methods do not capture (i) the impact of lifestyle ‘epidemics’, different over time between men and women, and across generations and countries; and (ii) the mortality experience of other countries. We coherently projected mortality in selected European countries taking into account the impact of the smoking, obesity and alcohol ‘epidemics’. We used age-, sex-, yearand country-specific (i) all-cause mortality data from the Human Mortality Database, (ii) lung cancer mortality data from WHO to indirectly estimate smoking-attributable mortality, (iii) alcohol-attributable mortality data from the Global Burden of Disease Study (ages 2064) supplemented with WHO cause-specific mortality data, and (iv) obesity prevalence data from the NCD Risk Factor Collaboration study to estimate obesity-attributable mortality. We projected smoking, alcohol and obesity-attributable mortality fractions by novel projection methodologies that account for the wave pattern of lifestyle ‘epidemics’, and combined these into future lifestyle-attributable mortality fractions (LAMF) using a multiplicative approach. Future LAMF estimates are combined with Li-Lee coherent projections of non-lifestyle-attributable mortality. Our projections of life expectancy at birth (e0) up to 2065 are compared with direct individual and coherent all-cause mortality projections. The past increase in e0 (1990-2014) is less strong for non-lifestyle-attributable mortality compared to all-cause mortality among men, but slightly stronger among women. LAMF is projected to further decline among men and to first increase and then decline among women. When we integrate lifestyle epidemics in individual projections, future e0 (eventually) moves back to e0 for non-lifestyleattributable mortality. Including lifestyle epidemics when coherently forecasting mortality results in higher e0 levels and more convergence between men and women. Mortality projections that take into account likely future changes in smoking, alcohol and obesity, thus, result in higher future e0 and when projecting coherently in larger convergence between the sexes.
The Effect of Financial Development on CO2 Emissions in Iran

This study, using ARDL model, examines the relationship between carbon dioxide emissions and indicators of financial development with variables such as real per capita non-oil income, per capita energy consumption and ratio of import and export to GDP in Iran during the period 1352-1390. The results show that long-run elasticity of carbon dioxide emissions with respect to real per capita income, per capita energy consumption and export in Iran are positive and with respect to import is negative. According to the results, the ratio of liquid liabilities to GDP and private sector debt to the banking system to GDP have positive and significant effect on carbon dioxide emissions in the long run, (0.257) and (0.304) and in the short run (0.175) and (0.233) in Iran respectively. In addition, the effect of the ratio of commercial bank domestic assets to central bank and total assets of banking system on CO2 emissions in Iran in the long run is non-significant and in the short run is estimated at 90% significance level and about (-0.205). The Causality test results show that there is a short run one-way causal relationship of the three indicators of financial development on emissions of carbon dioxide in Iran.
Fast Scheduling of Autonomous Mobile Robots Under Task Space Constraints With Priorities

Automation is becoming more and more important to achieve high efficiency and productivities in manufacturing facilities, and there has been a large increase in the use of autonomous mobile robots (AMRs) for factory automation. With the number of AMRs increasing, how to optimally schedule them in a timely manner such that a large school of AMRs can finish all the assigned tasks within the shortest time presents a significant challenge for control engineers. Exhaustive search can provide an optimal solution. However, its associated computational time is too long to render it feasible for real-time control. This paper introduces a novel two-step algorithm for fast scheduling of AMRs that perform prioritized tasks involving transportation of tools/materials from a pick-up location to a drop-off point on the factory floor. The proposed two-step algorithm first clusters these tasks such that one cluster of tasks is assigned to one single AMR, followed by scheduling of the tasks within a cluster using a model-based learning technique. For the purpose of clustering and scheduling, a task space is defined. The results from the clustering and scheduling algorithms are compared with other widely used heuristic techniques. Both the clustering and the scheduling algorithms are shown to perform better on task sets of relevant sizes and generate real-time solutions for the scheduling of multiple AMRs under task space constraints with priorities.
Development of a recommender system based on life and health sciences literature

Recommender systems are quickly evolving and transforming our daily life by being used to explore large amounts of information and delivering personalized recommendations on several areas like streaming and e-commerce. But in the life and health sciences field, although there is a growing need of new ways to explore information due to the increase of digital information, there is one major issue that is preventing its evolution: the privacy of data. It is necessary to have data about users’ preferences to test and evolve health recommender systems. The main objective of this work is to create an open-source implicit feedback dataset based on life and health sciences literature and test it using a collaborative filtering recommender system. Using the LIBRETTI methodology, we created the dataset, called DisRM, using research articles from PubMed. The dataset is in the format <user, item, rating> where the users are authors of research articles and the items are diseases, and it has 2 309 190 ratings. Two additional datasets were created, DisRM10 and DisRM20, including only the users who have a number of ratings equal to or greater than 10 and 20, respectively. When applying a memory-based CF K-Nearest Neighbors algorithm to DisRM10 and DisRM20 we had the goal of optimizing the recall and the normalized discounted cumulative gain (nDCG), to ensure that most of the relevant items are being recommended and ranked high. We achieved the best recommendation results using the similarity measure PIP, obtaining a recall of 0.81 and a nDCG of 0.87 for DisRM10. When comparing DisRM with other baseline datasets, it performed similarly or better for recall and nDCG. This validates the quality of our dataset.
ODOR AND VOC EMISSIONS FROM SWINE MANURE STORAGES

A power centering apparatus for resisting off-center movement of the steered wheels of a vehicle and returning them to a selected center position after each such movement. The apparatus includes a centering unit for providing the resistance to off-center movement and a trimming unit for varying the center position. The resistance unit includes a linkage extending between the steering system and a fixed vehicle member, and the trimming unit has a trim piston movable relative to a trim cylinder to vary the center position by changing the length of the linkage. A plurality of passages and check valves make the trimming unit operable by a single solenoid valve to supply gas free liquid to and remove gas contaminated liquid from trim chambers on opposite sides of the trim piston without intermixing these two liquids. An annular liquid reservoir is formed in an accumulator by a solid outer wall concentric with a flexible inner wall and permits gas to be separated from returned liquid and pressurized liquid to be supplied to the centering unit. The resistance may be varied remotely and may be greater than a return force for returning the steered wheels to center after off-center movement.
Geometric Modeling Based on Polygonal Meshes

In the last years triangle meshes have become increasingly popular and are nowadays intensively used in many different areas of computer graphics and geometry processing. In classical CAGD irregular triangle meshes developed into a valuable alternative to traditional spline surfaces, since their conceptual simplicity allows for more flexible and highly efficient processing. Moreover, the consequent use of triangle meshes as surface representation avoids error-prone conversions, e.g., from CAD surfaces to mesh-based input data of numerical simulations. Besides classical geometric modeling, other major areas frequently employing triangle meshes are computer games and movie production. In this context geometric models are often acquired by 3D scanning techniques and have to undergo post-processing and shape optimization techniques before being actually used in production. This course discusses the whole geometry processing pipeline based on triangle meshes. We will first introduce general concepts of surface representations and point out the advantageous properties of triangle meshes in Chapter 2, and present efficient data structures for their implementation in Chapter 3. The different sources of input data and types of geometric and topological degeneracies and inconsistencies are described in Chapter 4, as well as techniques for their removal, resulting in clean two-manifold meshes suitable for further processing. Mesh quality criteria measuring geometric smoothness and element shape together with the corresponding analysis techniques are presented in Chapter 6. Mesh smoothing reduces noise in scanned surfaces by generalizing signal processing techniques to irregular triangle meshes (Chapter 7). Similarly, the underlying concepts from differential geometry are useful for surface parametrization as well (Chapter 8). Due to the enormous complexity of meshes acquired by 3D scanning, mesh decimation techniques are required for error-controlled simplification (Chapter 9). The shape of triangles, which is important for the robustness of numerical simulations, can be optimized by general remeshing methods (Chapter 10). After optimizing meshes with respect to the different quality criteria, we finally present techniques for intuitive and interactive shape deformation (Chapter 11). Since solving linear systems is a commonly required component for many of the presented mesh processing algorithms, we will discuss their efficient solution and compare several existing libraries in Chapter 12.
UNITED NATIONS ECONOMIC AND SOCIAL COUNCIL ECONOMIC COMMISSION FOR AFRICA AFRICAN UNION

1. The Statistical Commission held its seventh session at United Nations Headquarters, New York, from 2 to 13 February 1953. 2. The following representatives and alternates attended the session : Argentina: Mr. Cecilio J. Morales, Mr. Jose Maria Rivera (alternate), Mr. L. J. Cafiero (alternate) Australia: Mr. S. R. Carver, Mr. Donald V. Youngman (alternate) Canada: Mr. Herbert Marshall China: Mr. Choh-Ming Li Czechoslovakia: Mr. J. Rybar (alternate), Mr. F. Lukes (alternate) Denmark: Mrs. Rigmor Skade (alternate) Egypt: Mr. A. Ghorbal (alternate) France: Mr. R. Rivet India: Mr. P. C. Mahalanobis Netherlands: Mr. Ph. J. Idenburg Panama: Miss Carmen A. Miro Ukrainian Soviet Socialist Republic: Mr. V. A. Krivitski (alternate) Union of Soviet Socialist Republics: Mr. T. V. Ryabushkin United Kingdom of Great Britain and Northern Ireland: Mr. H. Campion, Mr. J. Stafford (alternate) United States of America: Mr. S. A. Rice 3. The representative of the Union of Soviet Socialist Republics submitted the following draft resolution: (a) To exclude the representative of the Kuomintang group from membership in the Statistical Commission; (b) To invite a representative of the People's Republic of China, appointed by the Central People's Government of China, to participate in the work of the seventh session of the Statistical Commission. Th~ Chairman ruled the proposal out of order, as the question of the representation of China had been considered by the Economic and Social Council, and the Statist! cal Commission, as a functional organ of the Counc1l, was not competent to deal with the question.
2 Benchmark Specification 2 . 1 Graph Kernels

We present a graph processing benchmark suite targeting shared memory platforms. The goal of this benchmark is to help standardize graph processing evaluations, making it easier to compare different research efforts and quantify improvements. The benchmark not only specifies kernels, input graphs, and evaluation methodologies, but it also provides optimized baseline implementations. These baseline implementations are representative of state-of-the-art performance, and thus new contributions should beat their performance to demonstrate an improvement. This benchmark suite can be used in a variety of settings. Graph framework developers can demonstrate the generality of their programming model by implementing all of the bench-mark's kernels and delivering competitive performance on all of the benchmark's graphs. Algorithm designers can use the input graphs and the baseline implementations to demonstrate their contribution. Platform designers and performance analysts can use the suite as a workload representative of graph processing.
Inductive Biases for Object-Centric Representations of Complex Textures

Understanding which inductive biases could be useful for the unsupervised learning of object-centric representations of natural scenes is challenging. Here, we use neural style transfer to generate datasets where objects have complex textures while still retaining ground-truth annotations. We ﬁnd that, when a model eﬀectively balances the importance of shape and appearance in the training objective, it can achieve better separation of the objects and learn more useful object representations.