Miracle-dz/UniFAR-FTUs huggingface dataset:
Vacuum Assisted Closure in the Management of Exposed Hardware for Skeletal Fixation: A Challenging Case

Pectus excavatum is a depression of the sternum and of costal cartilages, with reduction in the anteroposterior diameter of the thorax. It is the most common chest wall deformity requiring surgical correction. As described into the literature by many authors [1], metal supports for internal fixation to stabilize the sternum in the new corrected position are commonly used with an open approach. This sternochondroplasty procedure is performed under general anesthesia. The patient is placed in supine position with arms along the body. The skin incision is clamshell-like. Topographical landmarks for incision are the nipple in men and the inframammary fold in women. The ostheocartilaginous plane is exposed through the bilateral division of the sternal and rib insertions of the pectoralis major and the rectus abdominis muscles. The entire soft tissue plane (subcutaneous, skin and muscles) is mobilized and elevated en-block to avoid fluid collection and seroma formation in the early postoperative period.
PRINCIPLES OF CONSTRUCTION OF ACS OPERATOR TRAINING PERSPECTIVE SIMULATOR SYSTEMS BY DYNAMIC OBJECTS

Управление динамическими объектами с использованием автоматизированных систем управления определяет необходимость подготовки операторов АСУ высокой квалификации. В статье рассмотрены принципы построения тренажных систем подготовки операторов АСУ с использованием интеллектуальных информационных технологий для повышения качества их подготовки. Использование интеллектуальных информационных технологий позволить расширить круг решаемых задач, разнообразить условия проведения тренажей, адаптивно контролировать и урегулировать сложность решения контрольных задач, а также формировать и корректировать индивидуальные программы подготовки обучаемых с учетом их знаний, опыта и навыков.
Amount and Type of Dietary Fat, Postprandial Glycemia, and Insulin Requirements in Type 1 Diabetes: A Randomized Within-Subject Trial

OBJECTIVE The American Diabetes Association recommends individuals with type 1 diabetes (T1D) adjust insulin for dietary fat; however, optimal adjustments are not known. This study aimed to determine 1) the relationship between the amount and type of dietary fat and glycemia and 2) the optimal insulin adjustments for dietary fat. RESEARCH DESIGN AND METHODS Adults with T1D using insulin pump therapy attended the research clinic on 9–12 occasions. On the first six visits, participants consumed meals containing 45 g carbohydrate with 0 g, 20 g, 40 g, or 60 g fat and either saturated, monounsaturated, or polyunsaturated fat. Insulin was dosed using individual insulin/carbohydrate ratio as a dual-wave 50/50% over 2 h. On subsequent visits, participants repeated the 20–60-g fat meals with the insulin dose estimated using a model predictive bolus, up to twice per meal, until glycemic control was achieved. RESULTS With the same insulin dose, increasing the amount of fat resulted in a significant dose-dependent reduction in incremental area under the curve for glucose (iAUCglucose) in the early postprandial period (0–2 h; P = 0.008) and increase in iAUCglucose in the late postprandial period (2–5 h; P = 0.004). The type of fat made no significant difference to the 5-h iAUCglucose. To achieve glycemic control, on average participants required dual-wave insulin bolus: for 20 g fat, +6% insulin, 74/26% over 73 min; 40 g fat, +6% insulin, 63/37% over 75 min; and 60 g fat, +21% insulin, 49/51% over 105 min. CONCLUSIONS This study provides clinical guidance for mealtime insulin dosing recommendations for dietary fat in T1D.
Emotion Detection with Neural Personal Discrimination

There have been a recent line of works to automatically predict the emotions of posts in social media. Existing approaches consider the posts individually and predict their emotions independently. Different from previous researches, we explore the dependence among relevant posts via the authors’ backgrounds, since the authors with similar backgrounds, e.g., gender, location, tend to express similar emotions. However, such personal attributes are not easy to obtain in most social media websites, and it is hard to capture attributes-aware words to connect similar people. Accordingly, we propose a Neural Personal Discrimination (NPD) approach to address above challenges by determining personal attributes from posts, and connecting relevant posts with similar attributes to jointly learn their emotions. In particular, we employ adversarial discriminators to determine the personal attributes, with attention mechanisms to aggregate attributes-aware words. In this way, social correlationship among different posts can be better addressed. Experimental results show the usefulness of personal attributes, and the effectiveness of our proposed NPD approach in capturing such personal attributes with significant gains over the state-of-the-art models.
Central limit theorems and the geometry of polynomials

Let $X \in \{0,\ldots,n \}$ be a random variable, with mean $\mu$ and standard deviation $\sigma$ and let \[f_X(z) = \sum_{k} \mathbb{P}(X = k) z^k, \] be its probability generating function. Pemantle conjectured that if $\sigma$ is large and $f_X$ has no roots close to $1\in \mathbb{C}$ then $X$ must be approximately normal. We completely resolve this conjecture in the following strong quantitative form, obtaining sharp bounds. If $\delta = \min_{\zeta}|\zeta-1|$ over the complex roots $\zeta$ of $f_X$, and $X^{\ast} := (X-\mu)/\sigma$, then \[ \sup_{t \in \mathbb{R}} \left|\mathbb{P}(X^{\ast} \leq t) - \mathbb{P}( Z \leq t) \, \right| = O\left(\frac{\log n}{\delta\sigma} \right) \] where $Z \sim \mathcal{N}(0,1)$ is a standard normal. This gives the best possible version of a result of Lebowitz, Pittel, Ruelle and Speer. We also show that if $f_X$ has no roots with small argument, then $X$ must be approximately normal, again in a sharp quantitative form: if we set $\delta = \min_{\zeta}|\arg(\zeta)|$ then \[ \sup_{t \in \mathbb{R}} \left|\mathbb{P}(X^{\ast} \leq t) - \mathbb{P}( Z \leq t) \, \right| = O\left(\frac{1}{\delta\sigma} \right). \] Using this result, we answer a question of Ghosh, Liggett and Pemantle by proving a sharp multivariate central limit theorem for random variables with real-stable probability generating functions.
Negative Impedances as Power System and Control Elements in EMTP-Type Programs

Negative impedances have long been used in power system analysis for the representation of three-winding transformers as star circuits. They do not cause problems as long as precautions are observed about the correct placement of magnetizing branches. With the addition of ideal operational amplifiers, negative resistances and capacitances have recently been used to represent first-order transfer functions of control circuits, with only one (“non-inverting”) ideal operational amplifier. While not physically realizable, it can be shown that the equations are identical to those of a physically based model with positive impedances and an extra inverting ideal operational amplifier, if the system of equations of the latter is reduced. In the past, some attempts have been made to represent transfer functions of control circuits with R,L,C-elements directly connected to the measuring point of the power system. This R,L,C-circuit does load the power system with an impedance, however, which is unacceptable unless its value is very high. A simple approach for avoiding the load on the power system measuring point is the connection of an equal impedance, but with negative value, to the same point. Examples show that these negative impedances do not cause problems in EMTP simulations.
Testing the Tremaine–Weinberg Method Applied to Integral-field Spectroscopic Data Using a Simulated Barred Galaxy

Tremaine and Weinberg (TW) proposed a conceptually simple procedure relying on long-slit spectroscopy to measure the pattern speeds of bars (Ωp) in disk galaxies. Using a simulated galaxy, we investigate the potential biases and uncertainties of TW measurements using increasingly popular integral-field spectrographs (IFSs), for which multiple pseudo-slits (and thus independent measurements) can be constructed with a single observation. Most importantly, to establish the spatial coverage required and ensure the validity of the measurements, the inferred Ωp must asymptotically converge as the (half-)length of each pseudo-slit used is increased. The requirement for our simulation is to reach ≈1.3 times the half-light radius, but this may vary from galaxy to galaxy. Only those slits located within the bar region yield accurate measurements. We confirm that the position angle of the disk is the dominant source of systematic error in TW Ωp measurements, leading to under/overestimates of tens of percent for inaccuracies of even a few degrees. Recasting the data so that the data grid aligns with the disk major axis leads to slightly reduced uncertainties. Accurate measurements are obtained only for well-defined ranges of the bar angle (relative to the galaxy major axis) ϕbar and the inclination angle i, here 10° ≲ ϕbar ≲ 75° and 105° ≲ ϕbar ≲ 170° and 15° ≲ i ≲ 70°. The adopted (pseudo-)slit widths, spatial resolution, and (unless extremely aggressive) spatial binning of IFS data have no significant impact on the measurements. Our results thus provide useful guidelines for reliable and accurate direct Ωp measurements with IFS observations.
TACKS FOR GRAPH DEEP LEARNING

Deep learning models have achieved state-of-the-art performance in classifying nodes in graph-structured data. However, recent work has shown that these models are vulnerable to adversarial attacks. In particular, is is possible to adversarially perturb the graph structure and the node features in order to induce classification errors. In this paper, we study the effect of recently proposed attacks on graph models which incorporate structure exploration. We then propose a method for detecting attacks when they occur.
Networked Control of Nonlinear Systems under Partial Observation Using Continuous Deep Q-Learning

In this paper, we propose a design of a model-free networked controller for a nonlinear plant whose mathematical model is unknown. In a networked control system, the controller and plant are located away from each other and exchange data over a network, which causes network delays that may fluctuate randomly due to network routing. So, in this paper, we assume that the current network delay is not known but an estimated maximum value of fluctuating network delays is known beforehand. Moreover, we also assume that the sensor cannot observe all state variables of the plant. Under these assumption, we apply continuous deep Q-learning to the design of the networked controller. Then, we introduce an extended state consisting of a sequence of past control inputs and outputs as inputs to the deep neural network. By simulation, it is shown that, using the extended state, the controller can learn a control policy robust to the fluctuation of the network delays under the partial observation.
Semantic-driven Visualization Techniques for Interactive Exploration of 3D Indoor Models

The availability of detailed virtual 3D building models including representations of indoor elements, allows for a wide number of applications requiring effective exploration and navigation functionality. Depending on the application context, users should be enabled to focus on specific Objects-of-Interests (OOIs) or important building elements. This requires approaches to filtering building parts as well as techniques to visualize important building objects and their relations. For it, this paper explores the application and combination of interactive rendering techniques as well as their semantically-driven configuration in the context of 3D indoor models.
Procella: Unifying serving and analytical data at YouTube

Large organizations like YouTube are dealing with exploding data volume and increasing demand for data driven applications. Broadly, these can be categorized as: reporting and dashboarding, embedded statistics in pages, time-series monitoring, and ad-hoc analysis. Typically, organizations build specialized infrastructure for each of these use cases. This, however, creates silos of data and processing, and results in a complex, expensive, and harder to maintain infrastructure. At YouTube, we solved this problem by building a new SQL query engine - Procella. Procella implements a superset of capabilities required to address all of the four use cases above, with high scale and performance, in a single product. Today, Procella serves hundreds of billions of queries per day across all four workloads at YouTube and several other Google product areas.
Analysis of YouTube DASH Traffic

To design efficient resource allocation algorithms that satisfy users’ quality of service requirements, it is necessary to know or correctly estimate the traffic characteristics. In recent years, the share of video streams in the overall traffic has reached 70% and continues to grow. Most of this type of traffic is generated by popular video hosting services, such as YouTube, NetFlix, and others, which use the Dynamic Adaptive Streaming over HTTP technology. This paper discusses the results of a study on the properties the most popular videos on YouTube, the world’s largest video hosting service. In addition, it is shown how traffic parameters depend on the application used to watched Youtube videos and operating systems of PCs and mobile devices.
Secure and Efficient Data Contribution Using Extended Identity Based Encryption In Cloud Computing

Cloud computing is used to share computing resources and data over internet. When sensitive data is shared over Internet, it should be transferred in secured manner. Data security is ensured using Identity Based Encryption mechanism. In the existing Cloud Computing system which is implemented using Identity Based Encryption mechanism, authorized user can share their credential to unauthorized user and data can be accessed by unauthorized user who hasn’t subscribed to Cloud Computing System. To overcome this issue, we propose a notion called Extended Identity Based Encryption(E-IBE) system, which ensures authentication and confidentiality. In this approach, we use Kerberos authentication protocol along with Identity Based Encryption methodology to ensure authentication and confidentiality. Keywords— Cloud Computing; Extended Identity Based Encryption;
Size-related non-random mating in a natural population of the ladybird beetle Harmonia axyridis ( Coleoptera : Coccinellidae )

We analyzed the occurrence of non-random mating in relation to body size in a natural population of the ladybird Harmonia axyridis (Pallas, 1773). Mating males were significantly larger than non-mating males in the spring generation. There was no significant difference in the size of mating and non-mating females in the spring generation or among members of either gender in the summer generation. Large males mated more frequently than medium-sized or small males only in the spring generation. Notably, large females mated less frequently than those in other size classes in the summer generation, although the difference was marginally significant, but not in the spring generation. The ratios of the gender size difference in pairs of mating individuals were significantly different among size classes of females and males in both generations. Ratio values may be indicative of the degree to which the mating success of males and/or females depends on mating behaviour (female repulsion vs. male compulsion). Large ratio values may indicate a low degree of mating success because females often refuse males and are more successful in repulsing them when the males that attempt to mate are relatively small. Conversely, when the ratio is small, male mating behaviour may be better able to overcome female refusal, thereby enhancing the degree of mating success. The behaviour that overcomes the differences in the sizes of the different sexes in mating pairs may drive size variability in both genders in natural populations. Mating success also depends on female mating preference, and the condition and activity level of males and females.
Real-time porosity: Combining a computer game engine with environmental sensors to better understand pedestrian movement in public/private space and in real-time

This paper describes the theoretical context, design, implementation and evaluation of a novel method for understanding pedestrian movement in public/private space. It examines the pedestrian counting and tracking methodologies of Space Syntax and proposes an alternative methodology that links sensors embedded in real-world environments and carried by pedestrians with an environment and avatars in a contemporary computer game. In this way observers are able to closely trail pedestrians without affecting their decision making. Results from a field trial are presented where the sensors and computer gaming technology were tested within a challenging real-world environment.
cacy of dinotefuran ( Alpine spray and dust ) on six species of stored product insects

Dinotefuran, an agonist of insect nicotinic acetylcholine receptors, was evaluated both as a 0.5% active ingredient aerosol spray and a dust combined with diatomaceous earth (DE), 5 g/m2 and 10 g/m2, at 45% r.h. and 75% r.h. Target species were six adult stored product insect species: Tribolium castaneum (Herbst), Rhyzopertha dominica (F.), Oryzaephilus surinamensis (L.), Tribolium confusum Jacqueline du Val, Dermestes maculatus (DeGeer), and Mezium affine Boieldieu. Adults were continually exposed for 4 d on the dusts, and assessments were done after 8 h and after 1, 2, 3, and 4 d to determine knockdown and adult survival/mortality. Mortality of T. castaneum, R. dominica, and O. surinamensis generally increased with exposure interval, and was 90% or more after three days of exposure at both dust rates and r.h. levels. Mortality of D. maculatus and T. confusum after three days ranged between 60 and 70% and 50 and 60%, respectively. Mortality of M. affine was 5% or less even after 4 days of exposure. Mortality of all species except M. affine was generally lower when exposed to the spray rather than the dust. No late stage larvae of T. castaneum, T. confusum, O. surinamensis, exposed to either the spray or the dusts emerged as adults, and only 3% of exposed D. maculatus emerged as adults. Results show that dinotefuran could be incorporated into management plans for control of stored product insects. Published by Elsevier Ltd.
Cleanits: A Data Cleaning System for Industrial Time Series

The great amount of time series generated by machines has enormous value in intelligent industry. Knowledge can be discovered from high-quality time series, and used for production optimization and anomaly detection in industry. However, the original sensors data always contain many errors. This requires a sophisticated cleaning strategy and a well-designed system for industrial data cleaning. Motivated by this, we introduce Cleanits, a system for industrial time series cleaning. It implements an integrated cleaning strategy for detecting and repairing three kinds of errors in industrial time series. We develop reliable data cleaning algorithms, considering features of both industrial time series and domain knowledge. We demonstrate Cleanits with two real datasets from power plants. The system detects and re-pairs multiple dirty data precisely, and improves the quality of industrial time series eﬀectively. Cleanits has a friendly interface for users, and result visualization along with logs are available during each cleaning process.
TextCube: Automated Construction and Multidimensional Exploration

Today’s society is immersed in a wealth of text data, ranging from news articles, to social media, research literature, medical records, and corporate reports. A grand challenge of data science and engineering is to develop e ﬀ ective and scalable methods to extract structures and knowledge from massive text data to satisfy diverse applications, without extensive, corpus-speciﬁc human annotations. In this tutorial, we show that TextCube provides a critical information organization structure that will satisfy such an information need. We overview a set of recently developed data-driven methods that facilitate automated construction of TextCubes from massive, domain-speciﬁc text corpora, and show that TextCubes so constructed will enhance text exploration and analysis for various applications. We focus on new TextCube construction methods that are scalable, weakly-supervised, domain-independent, language-agnostic, and e ﬀ ective (i.e., generating quality TextCubes from large corpora of various domains). We will demonstrate with real datasets (including news articles, scientiﬁc publications, and product reviews) on how TextCubes can be constructed to assist multidimensional analysis of massive text corpora.
Low-Rank Tensor Completion for Image and Video Recovery via Capped Nuclear Norm

Inspired by the robustness and efficiency of the capped nuclear norm, in this paper, we apply it to 3D tensor applications and propose a novel low-rank tensor completion method via tensor singular value decomposition (t-SVD) for image and video recovery. The proposed tensor capped nuclear norm model (TCNN) handles corrupted low-rank tensors by sparsity enhancement via truncating its partial singular values dynamically. We also develop a simple and efficient algorithm to solve the proposed nonconvex and nonsmooth optimization problem using the Majorization-Minimization (MM) framework. Since the proposed algorithm admits a closed-form solution by optimizing a well-selected approximate version of the original objective function at each iteration, it is very efficient. Experimental results on both synthetic and real-world datasets, clearly demonstrate the superior performance of the proposed method.
anogap-based electrical hydrogen sensors fabricated from d-PMMA hybrid thin films

Hybrid thin films, in which a Pd thin film was sandwiched between thin poly(methyl methacrylate) PMMA layers, were prepared on a flexible substrate using a combination of sputtering and spin coating. In realtime electrical response tests to H2, Pd-PMMA hybrid nanogap sensors operated in an on–off manner for both air and N2 carriers. Due to the selective penetration of H2 and the density reduction of the cracks that formed in the trilayer of the hybrid thin films, the detection limit of the hybrid nanogap sensors
Dietcoin: Hardening Bitcoin Transaction Verification Process For Mobile Devices

Distributed ledgers are among the most replicated data repositories in the world. They offer data consistency, immutability, and auditability, based on the assumption that each participating node locally verifies their entire content. Although their content, currently extending up to a few hundred gigabytes, can be accommodated by dedicated commodity hard disks, downloading it, processing it, and storing it in general-purpose desktop and laptop computers can prove largely impractical. Even worse, this becomes a prohibitive restriction for smartphones, mobile devices, and resource-constrained IoT devices. In this demo, we present an implementation of Dietcoin, a Bitcoin protocol extension that allows nodes to perform secure local verification of Bitcoin transactions with small bandwidth and storage requirements. This demo presents and benchmarks the main features of Dietcoin that are important for today's cryptocurrencies and smart contract systems, but are missing in the current state-of-the-art: (i) allowing resource-constrained devices to verify the correctness of selected blocks locally without having to download the complete ledger; (ii) enabling devices to join a blockchain quickly yet securely, dropping bootstrap time from days down to a matter of seconds; (iii) providing a generic solution that can be applied to other distributed ledgers secured with Proof-of-Work.