Miracle-dz/UniFAR-FTUs huggingface dataset:
Individualized Anterior Cruciate Ligament Graft Matching: In Vivo Comparison of Cross-sectional Areas of Hamstring, Patellar, and Quadriceps Tendon Grafts and ACL Insertion Area

Background: Recent literature correlated anterior cruciate ligament (ACL) reconstruction failure to smaller diameter of the harvested hamstring (HS) autograft. However, this approach may be a simplification, as relation of graft size to native ACL size is not typically assessed and oversized grafts may impart their own complications. Purpose: To evaluate in vivo data to determine if the commonly used autografts reliably restore native ACL size. Study Design: Descriptive laboratory study. Methods: Intraoperative data of the tibial insertion area and HS graft diameter were collected and retrospectively evaluated for 46 patients who underwent ACL reconstruction with HS autografts. Magnetic resonance imaging measurements of the cross-sectional area (CSA) of the possible patellar tendon (PT) and quadriceps tendon (QT) autografts were also done for each patient. The percentages of tibial insertion site area restored by the 3 possible grafts were then calculated and compared for each individual. Results: The mean ACL tibial insertion area was 107.2 mm2 (60.5-155.5 mm2). The mean CSAs of PT, HS, and QT were 33.2, 55.3, and 71.4 mm2, respectively. When all grafts were evaluated, the percentage reconstruction of the insertion area varied from 16.2% to 123.1% on the tibial site and from 25.5% to 176.7% on the femoral site, differing significantly for each graft type (P < .05). On average, 32.8% of the tibial insertion area would have been filled with PT, 53.6% by HS, and 69.5% by QT. Based on previous cadaveric studies indicating that graft size goal should be 50.2% ± 15% of the tibial insertion area, 82.7% of patients in the HS group were within this range (36.9%, QT; 30.5%, PT), while 65.2% in the PT group were below it and 60.9% in the QT group were above it. Conclusion: ACL insertion size and the CSAs of 3 commonly used grafts vary greatly for each patient and are not correlated with one another. Thus, if the reconstructed ACL size is determined by the harvested autograft size alone, native ACL size may not be adequately restored. PT grafts tended to undersize the native ACL, while QT might oversize it. Clinical Relevance: These results may help surgeons in preoperative planning, as magnetic resonance imaging measurements can be helpful in determining individualized graft choice to adequately restore the native ACL.
Linear-time exact sampling of sum-constrained random variables

In the problem of exactly-sampling from a probability distribution, one is often led to sample from a measure of the form µ ( x 1 , . . . , x n ) ∝ (cid:81) i f i ( x i ) × δ x 1 + ··· + x n ,m , with f i ’s integer-valued distributions. When the f i ’s are all equal, an algorithm of Devroye essentially solves this problem in linear time. However, in several important cases these f i ’s are in the same family, but have distinct parameters. A typical example is the sampling of random set partitions, related to a set of f i ’s which are geometric distributions with diﬀerent averages. We describe here a simple algorithmic solution for a large family of n -tuples of functions. At this aim we provide the notion of “positive decomposition” of one probability distribution into another.
Towards User‐Centered Active Learning Algorithms

The labeling of data sets is a time‐consuming task, which is, however, an important prerequisite for machine learning and visual analytics. Visual‐interactive labeling (VIAL) provides users an active role in the process of labeling, with the goal to combine the potentials of humans and machines to make labeling more efficient. Recent experiments showed that users apply different strategies when selecting instances for labeling with visual‐interactive interfaces. In this paper, we contribute a systematic quantitative analysis of such user strategies. We identify computational building blocks of user strategies, formalize them, and investigate their potentials for different machine learning tasks in systematic experiments. The core insights of our experiments are as follows. First, we identified that particular user strategies can be used to considerably mitigate the bootstrap (cold start) problem in early labeling phases. Second, we observed that they have the potential to outperform existing active learning strategies in later phases. Third, we analyzed the identified core building blocks, which can serve as the basis for novel selection strategies. Overall, we observed that data‐based user strategies (clusters, dense areas) work considerably well in early phases, while model‐based user strategies (e.g., class separation) perform better during later phases. The insights gained from this work can be applied to develop novel active learning approaches as well as to better guide users in visual interactive labeling.
Role of Immunotherapy for Oncogene-Driven Non-Small Cell Lung Cancer

The clinical application of immune checkpoint inhibitors (ICIs) has led to dramatic changes in the treatment strategy for patients with advanced non-small cell lung cancer (NSCLC). Despite the observation of improved overall survival in NSCLC patients treated with ICIs, their efficacy varies greatly among different immune and molecular profiles in tumors. Particularly, the clinical significance of ICIs for oncogene-driven NSCLC has been controversial. In this review, we provide recent clinical and preclinical data focused on the relationship between oncogenic drivers and immunological characteristics and discuss the future direction of immunotherapy in NSCLC patients harboring such genetic alterations
2.4 kV Vertical GaN PN Diodes on Free Standing GaN Wafer Using CMOS-Compatible Contact Materials

This paper reports vertical Gallium nitride (GaN) PN diodes on free-standing GaN wafer using a complementary metal-oxide-semiconductor compatible contact materials. Static and switching current-voltage measurements have been carried out to evaluate the fabricated vertical GaN PN diodes. The vertical GaN PN diode in this paper shows turn-on voltage Von of ~3.3–3.4 V, on/off current ratio of ~2.7<inline-formula> <tex-math notation="LaTeX">$\times 10^{7}$ </tex-math></inline-formula>, and ideal factor <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> of ~2.1. The reverse recovery time Trr is 21.2 ns and 23.2 ns, respectively, under a testing temperature of 300 K and 500 K. With an on-state resistance Ron of 3.9 m<inline-formula> <tex-math notation="LaTeX">$\Omega \cdot$ </tex-math></inline-formula>cm<sup>2</sup> and a breakdown voltage <inline-formula> <tex-math notation="LaTeX">$V_{\text BR}$ </tex-math></inline-formula> of 2.4 kV, this device achieves a power device figure-of-merit <inline-formula> <tex-math notation="LaTeX">$V_{\text BR}^{2}/R_{\text on}$ </tex-math></inline-formula> of <inline-formula> <tex-math notation="LaTeX">$1.5\times 10^{9} \text {V}^{2} {\Omega} ^{-1} \text {cm}^{-2}$ </tex-math></inline-formula>.
Report of NEWS 2018 Named Entity Transliteration Shared Task

This report presents the results from the Named Entity Transliteration Shared Task conducted as part of The Seventh Named Entities Workshop (NEWS 2018) held at ACL 2018 in Melbourne, Australia. Similar to previous editions of NEWS, the Shared Task featured 19 tasks on proper name transliteration, including 13 different languages and two different Japanese scripts. A total of 6 teams from 8 different institutions participated in the evaluation, submitting 424 runs, involving different transliteration methodologies. Four performance metrics were used to report the evaluation results. The NEWS shared task on machine transliteration has successfully achieved its objectives by providing a common ground for the research community to conduct comparative evaluations of state-of-the-art technologies that will benefit the future research and development in this area.
The University of Texas System Submission for the Code-Switching Workshop Shared Task 2018

This paper describes the system for the Named Entity Recognition Shared Task of the Third Workshop on Computational Approaches to Linguistic Code-Switching (CALCS) submitted by the Bilingual Annotations Tasks (BATs) research group of the University of Texas. Our system uses several features to train a Conditional Random Field (CRF) model for classifying input words as Named Entities (NEs) using the Inside-Outside-Beginning (IOB) tagging scheme. We participated in the Modern Standard Arabic-Egyptian Arabic (MSA-EGY) and English-Spanish (ENG-SPA) tasks, achieving weighted average F-scores of 65.62 and 54.16 respectively. We also describe the performance of a deep neural network (NN) trained on a subset of the CRF features, which did not surpass CRF performance.
Facility Location with Variable and Dynamic Populations

Facility location is a well-studied problem in social choice literature, where agents' preferences are restricted to be single-peaked. When the number of agents is treated as a variable (e.g., not observable a priori), a social choice function must be defined so that it can accept any possible number of preferences as input. Furthermore, there exist cases where multiple choices must be made continuously while agents dynamically arrive/leave. Under such variable and dynamic populations, a social choice function needs to give each agent an incentive to sincerely report her existence. In this paper we investigate facility location models with variable and dynamic populations. For a static, i.e., one-shot, variable population model, we provide a necessary and sufficient condition for a social choice function to satisfy participation, as well as truthfulness, anonymity, and Pareto efficiency. The condition is given as a further restriction on the well-known median voter schemes. For a dynamic model, we first propose an online social choice function, which is optimal for the total sum of the distances between the choices in the previous and current periods, among any Pareto efficient functions. We then define a generalized class of online social choice functions and compare their performances both theoretically and experimentally.
Resource Allocation in Cache-Enabled CRAN with Non-Orthogonal Multiple Access

This paper studies the application of non-orthogonal multiple access (NOMA) to cache-enabled cloud radio access network (CRAN) with mixed multicast and unicast transmission. Users requesting the same content are grouped together and served with a cluster of remote radio heads (RRHs) using distributed beamforming. In addition, the user with better channel condition in each group is allowed to request an extra unicast content via the NOMA protocol. Each RRH has a local cache which enables it to acquire the requested contents either from the local cache or from the central processor via the fronthaul link. Taking the maximum fronthaul capacity into consideration, we investigate the subchannel (SC) allocation problem to both RRHs and multicast groups to improve the weighted network sum rate. The optimal solution requires exhaustive search, which become prohibitively complicated as the number of RRHs and groups increases. To tackle this problem effectively, we formulate this problem as a three-sided matching problem among SCs, RRHs and multicast groups, and propose a novel low-complexity matching algorithm. We prove mathematically that the proposed algorithm converges to a stable matching within limited number of iterations. Numerical results unveil that the proposed algorithm closely approaches the optimal solution and outperforms the conventional orthogonal multiple access (OMA)-based CRAN.
An Optimized Iterative Semantic Compression Algorithm And Parallel Processing for Large Scale Data

With the continuous growth of data size and the use of compression technology, data reduction has great research value and practical significance. Aiming at the shortcomings of the existing semantic compression algorithm, this paper is based on the analysis of ItCompress algorithm, and designs a method of bidirectional order selection based on interval partitioning, which named An Optimized Iterative Semantic Compression Algorithm (Optimized ItCompress Algorithm). In order to further improve the speed of the algorithm, we propose a parallel optimization iterative semantic compression algorithm using GPU ( POICAG ) and an optimized iterative semantic compression algorithm using Spark ( DOICAS ). A lot of valid experiments are carried out on four kinds of datasets, which fully verified the efficiency of the proposed algorithm.
Optimal Renewable Penetration in Energy Procurement and Demand Response

In this paper, joint energy procurement and demand response is studied from the perspective of the operator of a power system. The operator procures energy from both renewable energy sources (RESs) and the spot market. We observe the fact that the RESs may incur considerable infrastructure cost. This cost is taken into account and the optimal planning of renewables is examined by controlling the investment in RES infrastructures. Due to the uncertainty of renewables, the operator can also purchase energy directly from the spot market to compensate for the possible deficit incurred by the realization of the random renewable energy. By setting appropriate prices, the operator sells the collected energy to heterogeneous end users with different demand response characteristics. We model the decision making process of the operator as a two-stage optimization problem. The optimal decisions on the renewable deployment, energy purchase from the spot market and pricing schemes are derived. Several solution structures are observed and a computationally efficient algorithm, requiring only closed-form calculation and simple bisection search, is proposed to compute the optimal decisions. Finally, numerical experiments are conducted to verify the optimality of the proposed algorithm and the solution structures observed theoretically.
Expertise in Professional Overwatch Play

eSports﻿ is﻿ a﻿ rapidly﻿ growing﻿ phenomenon﻿ in﻿ competitive﻿ gaming. ﻿ Expertise﻿ is﻿ an﻿ interesting﻿topic﻿to﻿study﻿in﻿relation﻿to﻿eSports,﻿as﻿eSports﻿athletes﻿are﻿considered﻿ among﻿the﻿most﻿highly-skilled﻿players﻿of﻿their﻿particular﻿games. ﻿Examining﻿expertise﻿ not﻿only﻿advances﻿the﻿understanding﻿of﻿what﻿skills﻿compose﻿professional﻿play﻿but﻿ enables﻿a﻿deeper﻿study﻿of﻿learning﻿in﻿games;﻿before﻿learning﻿processes﻿are﻿studied,﻿ it﻿is﻿important﻿to﻿detail﻿what﻿learning﻿these﻿processes﻿should﻿produce. ﻿This﻿study﻿ examines﻿expertise﻿through﻿the﻿application﻿of﻿thematic﻿analysis﻿to﻿a﻿series﻿of﻿interviews﻿ with﻿ professional﻿ players﻿ of﻿ the﻿ eSports﻿ game,﻿ Overwatch. ﻿ The﻿ goal﻿ of﻿ this﻿ study﻿ is﻿to﻿identify﻿which﻿skills﻿are﻿perceived﻿to﻿be﻿important﻿to﻿professional-level﻿play﻿ by﻿ professional﻿ players. ﻿ Two﻿ overarching﻿ themes﻿ were﻿ identified,﻿ game﻿ sense﻿ and﻿ mechanics. ﻿A﻿number﻿of﻿sub-themes﻿were﻿identified﻿as﻿important,﻿including﻿survival,﻿ anticipation﻿/﻿prediction
Mitigation of Distribution Level Load Fluctuation by Exploiting Elasticity of Local Area Packetized Power Networks

Featured by distributed energy storing and time division multiplexing transmission, packetized power distribution has great potential in compensating inelastic load fluctuations at the demand side. This paper investigates an AC distribution grid integrated with DC packetized-power distribution networks. The DC sector enables a store-then-consume mechanism, whereby the sectoral loads, characterized as elastic ones, can be manipulated to reduce the distribution level load fluctuation, characterized by peak-to-average ratio (PAR). We develop a day-ahead hierarchical scheduling to shape the distribution level load profile and the DC sector load profile by utilizing the DC sector load elasticity, based on which it further schedules the DC intra-sector energy distribution. The scheduling problems are formulated as PAR minimizations and effectively solved. Simulations demonstrate the effectiveness of the scheme in reducing distribution level load fluctuation and reveal fact on how much DC elastic loads is needed for fluctuation reduction.
Pretraining Sentiment Classifiers with Unlabeled Dialog Data

The huge cost of creating labeled training data is a common problem for supervised learning tasks such as sentiment classification. Recent studies showed that pretraining with unlabeled data via a language model can improve the performance of classification models. In this paper, we take the concept a step further by using a conditional language model, instead of a language model. Specifically, we address a sentiment classification task for a tweet analysis service as a case study and propose a pretraining strategy with unlabeled dialog data (tweet-reply pairs) via an encoder-decoder model. Experimental results show that our strategy can improve the performance of sentiment classifiers and outperform several state-of-the-art strategies including language model pretraining.
Trajectory generation and step planning of a 12 DoF biped robot on uneven surface

SUMMARY One of the primary goals of biped locomotion is to generate and execute joint trajectories on a corresponding step plan that takes the robot from a start point to a goal while avoiding obstacles and consuming as little energy as possible. Past researchers have studied trajectory generation and step planning independently, mainly because optimal generation of robot gait using dynamic formulation cannot be done in real time. Also, most step-planning studies are for flat terrain guided by search heuristics. In the proposed method, a framework for generating trajectories as well as an overall step plan for navigation of a 12 degrees of freedom biped on an uneven terrain with obstacles is presented. In order to accomplish this, a dynamic model of the robot is developed and a trajectory generation program is integrated with it using gait variables. The variables are determined using a genetic algorithm based optimization program with the objective of minimizing energy consumption subject to balance and kinematic constraints of the biped. A database of these variables for various terrain angles and walking motions is used to train two neural networks, one for real-time trajectory generation and another for energy estimation. To develop a global navigation strategy, a weighted A* search is used to generate the footstep plan with energy considerations in sight. The efficacy of the approach is exhibited through simulation-based results on a variety of terrains.
Cryptococcal Lymphadenitis in an HIV-Infected Patient: A Rare Manifestation of Immune Reconstitution Inflammatory Syndrome

Cryptococcosis is a fungal infection that is typically associated with acquired immunodeficiency syndrome (AIDS). The advent of highly active antiretroviral therapy has decreased the frequency of this infection, but has led to the emergence of atypical cases of immune reconstitution inflammatory syndrome (IRIS). Here, we describe the case of a 40-year-old man who was diagnosed with HIV infection and cryptococcal meningitis. He was successfully treated with antifungals and then started antiretroviral therapy. The patient returned to the hospital 15 months later complaining of fever, pain, and neck swelling. A computed tomography (CT) scan revealed a conglomerate of necrotic lymph nodes in the supraclavicular region. He underwent biopsy and histology showed granulomatous inflammation with fungal elements, consistent with Cryptococcus. He tested positive for serum cryptococcal antigen. The patient was treated with liposomal amphotericin and flucytosine. After induction therapy, he was re-started on fluconazole. The final fungal cultures were negative. We attributed our patient’s clinical presentation to “paradoxical” IRIS, which was associated with his previously treated cryptococcosis. Near resolution of the supraclavicular mass was noted at the 3-month follow-up.
Pushing the Limits of Radiology with Joint Modeling of Visual and Textual Information

Recently, there has been increasing interest in the intersection of computer vision and natural language processing. Researchers have studied several interesting tasks, including generating text descriptions from images and videos and language embedding of images. More recent work has further extended the scope of this area to combine videos and language, learning to solve non-visual tasks using visual cues, visual question answering, and visual dialog. Despite a large body of research on the intersection of vision-language technology, its adaption to the medical domain is not fully explored. To address this research gap, we aim to develop machine learning models that can reason jointly on medical images and clinical text for advanced search, retrieval, annotation and description of medical images.
Improving Initial Access Reliability of 5G mmWave Cellular in Massive V2X Communications Scenarios

Future automotive systems are expected to significantly benefit from a range of diverse mechanisms and capabilities that will be offered by the emerging fifth-generation (5G) cellular technology. In particular, one of the most prominent 5G use cases is represented by the Vehicle-to-Everything (V2X) context, which aims to enhance people's driving experience with collective safety and infotainment applications (e.g., autonomous driving, driver assistance, and contextual information). To achieve the requirements of better reliability, lower latency, and higher data rate, the use of extremely high frequencies (known as millimeter-wave, mmWave) is envisioned as an efficient solution. In fact, very high numbers of sensors deployed on vehicles introduce a serious challenge for the initial access procedure due to likely collisions in case of massive connection attempts. For that reason, the goal of this work is to offer improvements to the reliability of the initial access procedure for 5G mmWave cellular in massive V2X communications scenarios. In doing so, we propose to exploit redundant preamble transmissions in order to faster acquire a data transmission opportunity. Our obtained results indicate that by sending multiple replicas of a random access preamble the success probability to transmit at the first attempt is at least twice higher than that with the legacy approaches where a single random access preamble is being sent.
Tailwind: Fast and Atomic RDMA-based Replication

Replication is essential for fault-tolerance, but it is also a source of high overhead. Some recent in-memory systems use remote direct memory accesses (RDMA) to create redundant copies of data in remote memory. RDMA is attractive, since it is low-latency, high-throughput, and has no CPU overhead at the target. However, even with RDMA, no existing replication protocol avoids redundant data copying and target-side work. Backup servers may receive updates via RDMA, but they must actively incorporate the updates into replicas to update backup data structures and to ensure that only fully received messages are applied. In this paper, we present Tailwind, a zero-copy recovery log replication protocol for scale-out in-memory databases. Tailwind is the first replication protocol that eliminates all CPU-driven data copying and fully bypasses target server CPUs for data replication. In Tailwind , all data movement is done via device DMA; one-sided RDMA writes place data directly in remote I/O buffers in its final form for stable storage. Tailwind substantially improves replication throughput and response latency compared with conventional RPC-based replica-tion. In symmetric systems where servers both serve requests and act as replicas, Tailwind also improves normal-case throughput by freeing server CPU resources for request processing. We implemented and evaluated Tailwind on RAMCloud, a low-latency in-memory storage system. Experiments show Tailwind improves RAMCloud's normal-case request processing through-put by 1.7⇥. It also decreases writes median and 99 th percentile latencies by 2⇥ and 3⇥ respectively.