Miracle-dz/UniFAR-FTUs huggingface dataset:
Incidence of Avascular Necrosis of the Femoral Head After Intramedullary Nailing of Femoral Shaft Fractures

Abstract The goal of this study was to determine the incidence of avascular necrosis of the femoral head (AVNFH) after intramedullary nailing of femoral shaft fractures and to identify risk factors for developing AVNFH. We retrospectively reviewed all patients with femoral shaft fractures treated with antegrade intramedullary nailing at 10 institutions. Among the 703 patients enrolled, 161 patients were excluded leaving 542 patients in the study. Average age was 42.1 years with average follow-up of 26.3 months. Patient characteristics and fracture patterns as well as entry point of femoral nails were identified and the incidence of AVNFH was investigated. Patients were divided into 2 groups according to open versus closed physis, open versus closed fractures, and age (<20 versus ≥20 years). Overall incidence of AVNFH was 0.2% (1 of 542): the patient was 15-year-old boy. Of 25 patients with open physis, the incidence of AVNFH was 4%, whereas none of 517 patients with closed physis developed AVNFH (P < 0.001). The incidence of AVNFH in patients aged < 20 versus ≥20 years was 1.1% (1 of 93) and 0.0% (0 of 449), respectively (P = 0.172), which meant that the incidence of AVNFH was 0% in adult with femur shaft fracture. Of 61 patients with open fractures, the incidence of AVNFH was 0%. The number of cases with entry point at the trochanteric fossa or tip of the greater trochanter (GT) was 324 and 218, respectively, and the incidence of AVNFH was 0.3% and 0.0%, respectively (P = 0.412). In patients aged ≥20 years with isolated femoral shaft fracture, there was no case of AVNFH following antegrade intramedullary nailing regardless of the entry point. Therefore, our findings suggest that the risk of AVNFH following antegrade femoral nailing is extremely low in adult patients.
Leveraging on-chip networks for efficient prediction on multicore coherence

Coherent data prediction is introduced as a promising architectural technique for reducing cache-to-cache accesses in directory protocol. However, limited on-chip resources cause the accuracy of current prediction to be generally low. Low accuracy would result in a large number of unnecessary or incorrect predictions, which would consequently generate excessive network traffic. This leads to large power and performance overhead for coherent memory access. This paper proposes an early abort mechanism (EBT) that leverages NoC design to reduce the negative effect of wrong prediction operations, thus facilitating overall performance improvement and traffic reduction. Using detailed full-system simulations, we conclude that EBT provides a cost-effective solution for designing efficient multicore processors. To the best of our knowledge, this study is the first to leverage on-chip network for the prediction optimization on multicore coherence.
Reactivity of fecapentaene-12 toward thiols, DNA, and these constituents in human fibroblasts.

Micromolar concentrations of fecapentaene-12, a mutagen found in human feces, decrease survival measured as colony-forming efficiency and membrane integrity of cultured human fibroblasts. Fecapentaene-12 also decreases the content of cellular free low-molecular-weight thiols including glutathione. Fecapentaene-12 reacts directly with glutathione by causing both decreased levels of free thiol and some concomitant formation of oxidized glutathione, indicating that thiol depletion is a result of both alkylation and oxidative reactions. Exposure of cells to 2 or 5 microM fecapentaene-12 causes significant amounts of DNA-interstrand cross-links and DNA-single strand breaks, respectively, whereas exposure to a higher concentration of fecapentaene-12, i.e., 10 microM, also causes significant DNA-protein cross-links. Results from the reaction of fecapentaene-12 with isolated plasmid DNA parallel the cellular pattern of DNA damage; primarily interstrand cross-links and strand breaks occur also in plasmid DNA. Taken together, these studies show that fecapentaene-12 is a potent cytotoxic and genotoxic agent which can react with cellular thiols and cause several types of DNA damage.
The SNARE Protein Syntaxin 3 Confers Specificity for Polarized Axonal Trafficking in Neurons

Cell polarity and precise subcellular protein localization are pivotal to neuronal function. The SNARE machinery underlies intracellular membrane fusion events, but its role in neuronal polarity and selective protein targeting remain unclear. Here we report that syntaxin 3 is involved in orchestrating polarized trafficking in cultured rat hippocampal neurons. We show that syntaxin 3 localizes to the axonal plasma membrane, particularly to axonal tips, whereas syntaxin 4 localizes to the somatodendritic plasma membrane. Disruption of a conserved N-terminal targeting motif, which causes mislocalization of syntaxin 3, results in coincident mistargeting of the axonal cargos neuron-glia cell adhesion molecule (NgCAM) and neurexin, but not transferrin receptor, a somatodendritic cargo. Similarly, RNAi-mediated knockdown of endogenous syntaxin 3 leads to partial mistargeting of NgCAM, demonstrating that syntaxin 3 plays an important role in its targeting. Additionally, overexpression of syntaxin 3 results in increased axonal growth. Our findings suggest an important role for syntaxin 3 in maintaining neuronal polarity and in the critical task of selective trafficking of membrane protein to axons.
( LP ) 2 : Rule Induction for Information Extraction Using Linguistic Constraints

Machine learning has been widely used in information extraction from texts in the last years. Two directions of research can be identified: wrapper induction (WI) and NLP-based methodologies. WI techniques have historically made scarce use of linguistic information and their application is mainly limited to rigidly structured documents. NLP-based methodologies tend to be brittle when linguistic information is unreliable and their application to structured web documents has been largely unsuccessful. In this paper we describe (LP ) , an adaptive algorithm able to cope with a number of document types, from free texts to highly structured web pages. Its main feature is the ability to use both document formatting and linguistic information in the learning phase. In a number of experiments on publicly available corpora, (LP ) reports excellent results with respect to the state of the art. Also the algorithm has been implemented in two successful systems which have been used extensively for both research and application of IE from Web documents. In this paper we describe the algorithm in details focusing on the role of linguistic information in the rule induction process. We also quantify the contribution of linguistic information to the effectiveness and robustness of the analysis, especially in cases where it can be unreliable.
Adenovirus mutants with splice-enhancing mutations in the E3 complex transcription unit are also defective in E3A RNA 3'-end formation

Region E3 of adenovirus encodes about 10 overlapping mRNAs with different spliced structures. The mRNAs are 5' coterminal and form two major 3'-coterminal families termed E3A and E3B. As a group, the mRNAs have two 5' splice sites and four or five 3' splice sites. We previously described a novel class of virus mutants with deletions that enhance distant upstream and downstream 5' and 3' splice sites in region E3 (S. L. Deutscher, B. M. Bhat, M. H. Pursley, C. Cladaras, and W. S. M. Wold, Nucleic Acids Res. 13:5771-5788, 1985). We now report that two of these mutants, dl710 and dl712, are defective in RNA 3'-end formation at the E3A site. This result was surprising because the deletions in dl710 and dl712 are upstream of the putative signal for E3A RNA 3'-end formation. The explanation that we favor for this result is that the enhanced splicing activity in these mutants results in the splicing out of the E3A 3'-end site from the RNA precursor before the E3A 3' ends have a chance to form.
An investigation of general predictors for cognitive–behavioural therapy outcome for anxiety disorders in a routine clinical setting

Introduction Cognitive–behavioural therapy (CBT) is effective for treating anxiety disorders and is offered in most mental health services around the world. However, a relatively large number of patients with anxiety disorders do not benefit from CBT, experience relapses or drop out. Reliable predictors of treatment effects are lacking. The aim of this study is to investigate the predictive value of emotion regulation and attentional control for CBT outcome in a routine setting. Methods and analysis In this prospective and practice-based study, 112 patients with anxiety disorders referred for manual-based group CBT at two psychiatric outpatient clinics will be recruited. Emotion regulation, severity of anxiety and attentional control will be assessed with self-report measures and with an experimental computer-based attentional control task at baseline, post-treatment and at a 6-month follow-up. Emotion regulation will be measured with Difficulties in Emotion Regulation Questionnaire, severity of anxiety will be assessed with Beck Anxiety Inventory and attentional control will be measured with the self-report questionnaire, Attention Control Scale, and with an experimental computer-based attentional control task based on theory of visual attention. Data will be analysed using multilevel mixed-effects modelling. Ethics and dissemination The study is approved by the Danish National Ethical Board, the Department of Psychology Ethical Board, University of Copenhagen and by the Danish Data Protection Agency. Study findings will be disseminated through peer-reviewed journal publications and conference presentations. The Danish Committee System on Health Research Ethics has been notified about the project. Trial registration number NCT02638363.
Kinematic energy balance approach to submarine landslide evolution

The paper applies the energy balance kinematic method of plasticity theory to the large deformation problem of initiation and propagation of the spreading and ploughing failure outside a failed slab in submarine sediments. The models account for the phenomenon of the progressive propagation of a slope parallel slip surface, which is also quantified using the energy balance approach. In contrast to existing approximate analytical and numerical solutions, the proposed approach provides a theoretical basis for spreading and ploughing criteria as well as the comprehensive dynamic solution of the problem of post-failure landslide evolution. Incremental integration of the derived analytical expressions for kinetic energy in time allows for modelling recurrent initiation of new kinematic failure mechanisms with their subsequent large-scale deformation. Treating the failed slab as well as the spreading and ploughing mechanisms as one composite dynamically evolving mass movement allows for the final post-failure geomorphology of the failed slope to be predicted using basic mechanical principles.
Micro Self-reconfigurable Modular Robot Using Shape Memory Alloy

This paper presents a micro-sized self-reconfigurable modular robotic system using shape memory alloy (SMA) actuators. Composed of identical robotic modules, the system can actively configure various structures. The motion of module is based on two-dimensional rotation by using an actuator mechanism with two SMA torsion coil springs. The micro-sized module measures 2cm cube and weighs 15g, half the size of the previous model developed so far. The feasibility of reconfiguration was demonstrated using the micro-sized robotic modules. We also show an extended three-dimensional (3D) model and discuss a distributed self-reconfiguration algorithm for large-scale modular structures.
Unfolding the 8-bit era

We propose a hardware and software system that transforms 8-bit side-scrolling console video games into immersive multiplayer experiences. We enhance a classic video game console with custom hardware that time-multiplexes eight gamepad inputs to automatically hand off control from one gamepad to the next. Because control transfers quickly, people at a large event can frequently step in and out of a game and naturally call to their peers to join any time a gamepad is vacant. Video from the game console is captured and processed by a vision algorithm that stitches it into a continuous, expanding panoramic texture, which is displayed in real time on a 360 degree projection system at a large event space. With this system, side-scrolling games unfold across the walls of the room to encircle a large party, giving the feeling that the entire party is taking place inside of the game's world. When such a display system is not available, we also provide a virtual reality recreation of the experience. We show results of our system for a number of classic console games tested at a large live event. Results indicate that our work provides a successful recipe to create immersive, multiplayer, interactive experiences that leverage the nostalgic appeal of 8-bit games.
Computing the convolution and the Minkowski sum of surfaces

In many applications, such as NC tool path generation and robot motion planning, it is required to compute the Minkowski sum of two objects. Generally the Minkowski sum of two rational surfaces cannot be expressed in rational form. In this paper we show that for LN spline surfaces (surfaces with a linear field of normal vectors) a closed form representation is available.
Relative Link Quality Assessment and Hybrid Routing Scheme for Wireless Mesh Networks

We present a link capacity estimation model and a hybrid routing scheme for wireless mesh networks which aims at improving throughput. The multi-source interference between links and frame capture behaviors of nodes are analyzed. Our model provides estimated values which can indicate the relative quality of wireless links. And we present a routing metric which combines multiple factors based on the estimated values. The proposed routing scheme which is independent of special routing protocols adopts different strategies for the traffic of accessing the Internet and the end-to-end traffic. Besides, we introduce a new gateway-assisted routing technique to further improve the performance. Simulation results show that the estimation model provides accurate estimated values enough for route selection, and the routing scheme improves end-to-end throughput by 185% compared with legacy AODV[14] protocol.
A Survey of Preference-Based Reinforcement Learning Methods

Reinforcement learning (RL) techniques optimize the accumulated long-term reward of a suitably chosen reward function. However, designing such a reward function often requires a lot of task-specific prior knowledge. The designer needs to consider different objectives that do not only influence the learned behavior but also the learning progress. To alleviate these issues, preference-based reinforcement learning algorithms (PbRL) have been proposed that can directly learn from an expert's preferences instead of a hand-designed numeric reward. PbRL has gained traction in recent years due to its ability to resolve the reward shaping problem, its ability to learn from non numeric rewards and the possibility to reduce the dependence on expert knowledge. We provide a unified framework for PbRL that describes the task formally and points out the different design principles that affect the evaluation task for the human as well as the computational complexity. The design principles include the type of feedback that is assumed, the representation that is learned to capture the preferences, the optimization problem that has to be solved as well as how the exploration/exploitation problem is tackled. Furthermore, we point out shortcomings of current algorithms, propose open research questions and briefly survey practical tasks that have been solved using PbRL.
Prediction in M-complete Problems with Limited Sample Size

Abstract. We define a new Bayesian predictor called the posterior weighted median (PWM) and compare its performance to several other predictors including the Bayes model average under squared error loss, the Barbieri-Berger median model predictor, the stacking predictor, and the model average predictor based on Akaike’s information criterion. We argue that PWM generally gives better performance than other predictors over a range of M-complete problems. This range is between theM-closed-M-complete boundary and theM-completeM-open boundary. Indeed, as a problem gets closer to M-open, it seems that M-complete predictive methods begin to break down. Our comparisons rest on extensive simulations and real data examples.
Ieee Transactions on Information Forensics and Security 1 Multi Channel-kernel Canonical Correlation Analysis for Cross-view Person Re-identification

—In this paper we introduce a method to overcome one of the main challenges of person re-identification in multi-camera networks, namely cross-view appearance changes. The proposed solution addresses the extreme variability of person appearance in different camera views by exploiting multiple feature representations. For each feature, Kernel Canonical Correlation Analysis (KCCA) with different kernels is exploited to learn several projection spaces in which the appearance correlation between samples of the same person observed from different cameras is maximized. An iterative logistic regression is finally used to select and weigh the contributions of each feature projections and perform the matching between the two views. Experimental evaluation shows that the proposed solution obtains comparable performance on VIPeR and PRID 450s datasets and improves on PRID and CUHK01 datasets with respect to the state of the art.
Detecting Redundant Unit Tests for AspectJ Programs

Aspect-oriented software development is gaining popularity with the adoption of languages such as AspectJ. Testing is an important part in any software development, including aspect-oriented development. To automate generation of unit tests for AspectJ programs, we can apply the existing tools that automate generation of unit tests for Java programs. However, these tools can generate a large number of test inputs, and manually inspecting the behavior of the software on all these inputs is time consuming. We propose Raspect, a framework for detecting redundant unit tests for AspectJ programs. We introduce three levels of units in AspectJ programs: advised methods, advice, and intertype methods. We show how to detect at each level redundant test that does not exercise new behavior. Our approach selects only non-redundant tests from the automatically generated test suites, thus allowing the developer to spend less time in inspecting this reduced set of tests. We have implemented Raspect and applied it on 12 subjects taken from a variety of sources; our experience shows that Raspect can effectively reduce the size of generated test suites for inspecting AspectJ programs
Differential Topology of the Inverse Kinematic Problem for Redundant Robot Manipulators

In this paper a program of singularity theory is pro claimed to be of systematic use in robotics. Complete lists of normal forms are proposed and are regarded as candi date models of kinematics of robot manipulators. Argu ments for the applicability of candidate normal forms to manipulator kinematics are provided. Singularities and bifurcation diagrams of the normal forms are examined and consequences derived for the inverse kinematic prob lem in redundant kinematics with singularities.
Graph-based re-ranking using acoustic feature similarity between search results for spoken term detection on low-resource languages

Acoustic feature similarity between search results has been shown to be very helpful for the task of spoken term detection (STD). A graph-based re-ranking approach for STD has been proposed based on the concept that search results, which are acoustically similar to other results with higher confidence scores, should have higher scores themselves. In this approach, the similarity between all search results of a given term are considered as a graph, and the confidence scores of the search results propagate through this graph. Since this approach can improve STD results without any additional labelled data, it is especially suitable for STD on languages with limited amounts of annotated data. However, its performance has not been widely studied on benchmark corpora. In this paper, we investigate the effectiveness of the graph-based reranking approach on limited language data from the IARPA Babel program. Experiments on the low-resource languages, Assamese, Bengali and Lao, show that graph-based re-ranking improves STD systems using fuzzy matching, and lattices based on different kinds of units including words, subwords, and hybrids. Index Terms: Random Walk, Spoken Term Detection
Peer-to-peer Communities: Formation and Discovery

Current peer-to-peer systems are targeted for information sharing, file storage, searching and indexing often using an overlay network. In this paper we expand the scope of peer-to-peer systems to include the concept of " communities ". Communities are like interest groups, modeled after human communities and can overlap. They can also exist without anyone knowing about their existence. Communities are created, implicitly when one or more entities claim an interest in the same topic. Our work focuses on efficient methods to discover the formation of these self-configuring communities. We investigate the behavior of randomly created communities and model the complexity of discovery algorithms. Discovering communities on the fly is essential to being able to perform community directed searching. In addition, efficient discovery algorithms allow us to manage quickly changing community structures (dynamic communities, failures, mobile nodes and so on). We use some simulations to discover the architecture of randomly created communities and then perform studies on techniques for discovering communities.
Pricing to Accelerate Demand Learning in Dynamic Assortment Planning for Perishable Products

Retailers, from fashion stores to grocery stores, have to decide what range of products to offer, i.e., their product assortment. Frequent introduction of new products, a recent business trend, makes predicting demand more difficult, which in turn complicates assortment planning. We propose and study a stochastic dynamic programming model for simultaneously making assortment and pricing decisions which incorporates demand learning using Bayesian updates. We show analytically that it is profitable for the retailer to use price reductions early in the sales season to accelerate demand learning. A computational study demonstrates the benefits of such a policy and provides managerial insights that may help improve a retailer’s profitability.
Low-cost compact diffuse speckle contrast flowmeter using small laser diode and bare charge-coupled-device.

We report a low-cost compact diffuse speckle contrast flowmeter (DSCF) consisting of a small laser diode and a bare charge-coupled-device (CCD) chip, which can be used for contact measurements of blood flow variations in relatively deep tissues (up to ∼ 8 mm). Measurements of large flow variations by the contact DSCF probe are compared to a noncontact CCD-based diffuse speckle contrast spectroscopy and a standard contact diffuse correlation spectroscopy in tissue phantoms and a human forearm. Bland–Altman analysis shows no significant bias with good limits of agreement among these measurements: 96.5%±2.2% (94.4% to 100.0%) in phantom experiments and 92.8% in the forearm test. The relatively lower limit of agreement observed in the in vivo measurements (92.8%) is likely due to heterogeneous reactive responses of blood flow in different regions/volumes of the forearm tissues measured by different probes. The low-cost compact DSCF device holds great potential to be broadly used for continuous and longitudinal monitoring of blood flow alterations in ischemic/hypoxic tissues, which are usually associated with various vascular diseases.