Miracle-dz/UniFAR-FTUs huggingface dataset:
Endovascular treatment of ruptured posterior circulation cerebral aneurysms. Clinical and angiographic outcomes.

BACKGROUND AND PURPOSE We sought to describe the clinical outcome and angiographic results obtained in the endovascular therapy of ruptured posterior circulation cerebral aneurysms using Guglielmi detachable coils (GDC) over a 7-year period. METHODS A retrospective analysis was performed of 112 patients evaluated at the University of California at San Francisco Medical Center between June 1991 and August 1998. The Hunt-Hess grade at presentation of treated patients was I in 26 patients (24%), II in 24 (22%), III in 27 (25%), IV in 24 (22%), and V in 8 (7%). Clinical follow-up for the total population was achieved in 104 of 109 patients (96%), with a mean duration of 13.1 months. Angiographic follow-up for the subset excluding parent vessel occlusion cases was obtained in 93% of cases, with a mean duration of 7.2 months. RESULTS Technical success, defined as the ability to catheterize and embolize the aneurysm with GDC, was achieved in 109 of 112 of cases (97%). The mean angiographic occlusion rate, or projected area of the aneurysm occluded by the coils, for all 110 successfully treated aneurysms was 94.6%. At latest clinical follow-up, 81 of 109 patients (74%) achieved good recovery with Glasgow Outcome Scale (GOS) score of I, 10 of 109 (9%) were moderately (GOS II) and 5 of 109 (5%) were severely (GOS III) disabled, 1 of 109 (1%) remained in a vegetative state (GOS IV), and 12 of 109 (11%) were dead. Of the subset of 77 patients with Hunt-Hess grades I to III, 68 (88%) achieved a good clinical outcome (GOS I). A statistically significant correlation was demonstrated between Hunt-Hess grade at presentation and final GOS outcome score (chi(2)=41.4, P<0.0005). Procedure-related permanent morbidity was 2.8% (3/109 patients). Repeated hemorrhage was observed in a single patient (0.9%) with a partially treated aneurysm. CONCLUSIONS The observed favorable outcome and low morbidity in this group of high-risk patients point to GDC embolization as an effective method for the endovascular management of patients with ruptured posterior circulation aneurysms.
Fault tolerant cooperative control for multiple unmanned aerial vehicle systems with plugging operations

A tracking problem for a group of multiple unmanned aerial vehicle (Multi-UAV) systems with agent faults is investigated. A neighbouring rule-based cooperative control algorithm is proposed such that the Multi-UAV systems can follow the common reference signals in a fault free situation. Once in the presence of faults that change the dynamics of the agents, a fault tolerant cooperative control strategy based on the plugging operations (with a minor adjustment of connection) is employed in order to make each agent continue to follow the reference signals without increasing the number of weak points. Moreover, the utilised plugging operations do not need to reconfigure any individual’s self control law or stop the faulty agents. Simulation results show the effectiveness of the proposed methods.
Sparsification of motion-planning roadmaps by edge contraction

Roadmaps constructed by the recently introduced PRM* algorithm for asymptotically-optimal motion planning encode high-quality paths yet can be extremely dense. We consider the problem of sparsifying the roadmap, i.e. reducing its size, while minimizing the degradation of the quality of paths that can be extracted from the resulting roadmap. We present a simple, effective sparsifying algorithm, called roadmap sparsification by edgecontraction (RSEC). The primitive operation used by RSEC is edgecontraction —the contraction of a roadmap edge (v′,v″) to a new vertex v ’ and the connection of the new vertex v to the neighboring vertices of the contracted edge’s vertices (i.e. to all neighbors of v ′ and v ′ ). For certain scenarios, we compress more than 97% of the edges and vertices of a given roadmap at the cost of degradation of average shortest path length by at most 4%.
The Influence of Intergroup Comparisons on Africans' Intelligence Test Performance in a Job Selection Context

Sub-Saharan Africans living in Belgium (N = 69) completed a culture-free intelligence test in a simulated job selection environment. Prior to testing, the authors instructed participants that Africans' average performance on this test was generally better (positive comparison), worse (negative comparison), or equal to Belgians' performance. In a control condition, no such information was given. Results indicated that, compared with the equal and control conditions, performance was lower when intergroup comparisons were negative. In the former condition, participants were also more likely to endorse external factors that may account for lower performance. The authors interpreted the findings in line with stereotype threat theory (C. M. Steele & J. Aronson, 1995). In the context of job selection, the validity of intelligence tests conducted with members of stigmatized groups may be affected by the salience of social stereotypes and intergroup social comparisons.
Task-Constrained RBAC Model and Its Privilege Redundancy Analysis

RBAC supports the principle of least privilege by the appropriate combination of roles assigned to users. However, the minimum role set is hard to find. Role hierarchy and inheritance can result in aggregating lots of permissions. To solve this problem, a task-constrained RBAC model is proposed in this paper, which presents four task-constraint rules to restrict the permission inheritance and role activation. An approach to calculate the redundancy of permissions is represented also, which can be used to compare the different opinion on whether single role activation can get less privilege or multiple role activation can.
Glint: An MDS Framework for Costly Distance Functions

Previous algorithms for multidimensional scaling, or MDS, aim for scalable performance as the number of points to lay out increases. However, they either assume that the distance function is cheap to compute, and perform poorly when the distance function is costly, or they leave the precise number of distances to compute as a manual tuning parameter. We present Glint, an MDS algorithm framework that addresses both of these shortcomings. Glint is designed to automatically minimize the total number of distances computed by progressively computing a more and more densely sampled approximation of the distance matrix. We present instantiations of the Glint framework on three different classes of MDS algorithms: force-directed, analytic, and gradient-based. We validate the framework through computational benchmarks on several real-world datasets, and demonstrate substantial performance benefits without sacrificing layout quality.
A Practical Algorithm for Optimal Inference of Haplotypes from Diploid Populations

The next phase of human genomics will involve large-scale screens of populations for significant DNA polymorphisms, notably single nucleotide polymorphisms (SNP's). Dense human SNP maps are currently under construction. However, the utility of those maps and screens will be limited by the fact that humans are diploid, and that it is presently difficult to get separate data on the two "copies". Hence genotype (blended) SNP data will be collected, and the desired haplotype (partitioned) data must then be (partially) inferred. A particular non-deterministic inference algorithm was proposed and studied before SNP data was available, and extensively applied more recently to study the first available SNP data. In this paper, we consider the question of whether we can obtain an efficient, deterministic variant of that method to optimize the obtained inferences. Although we have shown elsewhere that the optimization problem is NP-hard, we present here a practical approach based on (integer) linear programming. The method either returns the optimal answer, and a declaration that it is the optimal, or declares that it has failed to find the optimal. The approach works quickly and correctly, finding the optimal on all simulated data tested, data that is expected to be more demanding than realistic biological data.
Genome Sequencing of Microbacterium sp. Yaish 1, a Bacterial Strain Isolated from the Rhizosphere of Date Palm Trees Affected by Salinity

ABSTRACT Microbacterium sp. strain Yaish 1 is a rhizospheric bacterium isolated from date palm orchards with high soil salinity. The genome was sequenced, and genes coding for growth-promoting 1-aminocyclopropane-1-carboxylate (ACC) deaminase, siderophore-producing proteins, and tryptophan biosynthesis proteins were identified. Here, we report the draft whole-genome sequencing of the strain.
Is it a good idea to design WDM networks to minimize the number of wavelengths used?

In this paper, we study the routing and wavelength assignment (RWA) tasks in WDM mesh networks with wavelength conversion capabilities. We solve the routing and wavelength assignment sub-problems as a single problem, rather than separating them into two separate problems. We use two different optimization criteria: (1) minimize the number of wavelength conversions used; and (2) minimize the number of wavelengths used. Each of these problems is solved by using a method based on the Dijkstra algorithm, sequentially on one connection at a time. We study and comment on the performance of the algorithms for different network topologies and traffic scenarios.
Shaping Social Activity by Incentivizing Users

Events in an online social network can be categorized roughly into endogenous events, where users just respond to the actions of their neighbors within the network, or exogenous events, where users take actions due to drives external to the network. How much external drive should be provided to each user, such that the network activity can be steered towards a target state? In this paper, we model social events using multivariate Hawkes processes, which can capture both endogenous and exogenous event intensities, and derive a time dependent linear relation between the intensity of exogenous events and the overall network activity. Exploiting this connection, we develop a convex optimization framework for determining the required level of external drive in order for the network to reach a desired activity level. We experimented with event data gathered from Twitter, and show that our method can steer the activity of the network more accurately than alternatives.
Pronunciation Proficiency Evaluation based on Discriminatively Refined Acoustic Models

The popular MLE (Maximum Likelihood Estimation) is a generative approach for acoustic modeling and ignores the information of other phones during training stage. Therefore, the MLE-trained acoustic models are confusable and unable to distinguish confusing phones well. This paper introduces discriminative measures of minimum phone/word error (MPE/MWE) to refine acoustic models to deal with the problem. Experiments on the database of 498 people's live Putonghua test indicate that: 1) Refined acoustic models are more distinguishable than conventional MLE ones; 2) Even though training and test are mismatch, they still perform significantly better than MLE ones in pronunciation proficiency evaluation. The final performance has approximately 4.5% relative improvement. Index Terms—computer assisted language learning, MPE, MWE, posterior probability, PSC, discriminative training
SOARAN: A Service-oriented Architecture for Radio Access Network Sharing in Evolving Mobile Networks

Mobile networks are undergoing fast evolution to software-defined networking (SDN) infrastructure in order to accommodate the ever-growing mobile traffic and overcome the network management nightmares caused by unremitting acceleration in technology innovations and evolution of the service market.Enabled by virtualized network functionalities, evolving carrier wireless networks tend to share radio access network (RAN) among multiple (virtual) network operators so as to increase network capacity and reduce expenses.However, existing RAN sharing models are operator-oriented, which expose extensive resource details, e.g. infrastructure and spectrum,to participating network operators for resource-sharing purposes. These old-fashioned models violate the design principles of SDN abstraction and are infeasible to manage the thriving traffic of on-demand customized services. This paper presents SOARAN, a service-oriented framework for RAN sharing in mobile networks evolving from LTE/LTE advanced to software-defined carrier wireless networks(SD-CWNs), which decouples network operators from radio resource by providing application-level differentiated services. SOARAN defines a serial of abstract applications with distinct Quality of Experience (QoE) requirements. The central controller periodically computes application-level resource allocation for each radio element with respect to runtime traffic demands and channel conditions, and disseminate these allocation decisions as service-oriented policies to respect element. The radio elements then independently determine flow-level resource allocation within each application to accomplish these policies. We formulate the application-level resource allocation as an optimization problem and develop a fast algorithm to solve it with a provably approximate guarantee.
Mesenchymal stromal cells promote B-cell lymphoma in lacrimal glands by inducing immunosuppressive microenvironment

Mesenchymal stromal cells (MSCs) have therapeutic potential for various diseases because of their anti-inflammatory and immunosuppressive properties. However, the immunosuppressive microenvironment allows tumor cells to evade immune surveillance, whereas maintenance of inflammation is required for tumor development and progression. Hence, MSCs may promote or suppress tumors in a context-dependent manner. We here investigated the effects of bone marrow-derived MSCs in a murine model of lacrimal gland B-cell lymphoma. Co-injection of MSCs with B lymphoma cells enhanced tumor growth in lacrimal glands without long-term engraftment. Of note, MSCs induced greater infiltration of immune and immune-regulatory cells near tumor: CD4+ cells, CD11b+ cells, CD4+Foxp3+ regulatory T cells and CD11b+Ly6C+Ly6G− myeloid-derived suppressor cells. Concurrently, there was up-regulation of immune-related molecules including TNF-α, IL-1β, TGF-β1, and arginase in glands treated with MSCs. Apoptosis in tumor was less severe in mice treated with MSCs compared to those without MSCs; however, MSCs did not directly inhibit apoptosis of B lymphoma cells in an in vitro co-culture. Together, data demonstrate that MSCs create immunosuppressive milieu by recruiting regulatory immune cells and promote B-cell lymphoma growth in lacrimal glands.
Near-optimal Differentially Private Principal Components

Principal components analysis (PCA) is a standard tool for identifying good low-dimensional approximations to data sets in high dimension. Many current data sets of interest contain private or sensitive information about individuals. Algorithms which operate on such data should be sensitive to the privacy risks in publishing their outputs. Differential privacy is a framework for developing tradeoffs between privacy and the utility of these outputs. In this paper we investigate the theory and empirical performance of differentially private approximations to PCA and propose a new method which explicitly optimizes the utility of the output. We demonstrate that on real data, there is a large performance gap between the existing method and our method. We show that the sample complexity for the two procedures differs in the scaling with the data dimension, and that our method is nearly optimal in terms of this scaling.
Shaping Socio-technical System Innovation Strategies using a Five Aspects Taxonomy

This paper introduces and describes a five aspect taxonomy for the engineering of complex systems, including structural, behavioral, contextual, temporal and perceptual aspects. The taxonomy has proven useful for (1) characterizing methods to ensure coverage of essential aspects of engineering complex systems; (2) providing a focusing framework to develop and select systems engineering innovation strategies; and (3) providing an organizing structure for classifying methodological research projects. Each of the five aspects is described, and the taxonomy is used to discuss recent and ongoing research on innovation strategies within and across the five aspects.
Trajectory Guided Tracking and Recognition of Actions

Abstract A combined 2D, 3D approach is presented that allows for robust tracking of moving people and recognition of actions. It is assumed that the system observes multiple moving objects via a single, uncalibrated video camera. Low-level features are often insufficient for detection, segmentation, and tracking of non-rigid moving objects. Therefore, an improved mechanism is proposed that integrates low-level (image processing), mid-level (recursive 3D trajectory estimation), and high-level (action recognition) processes. A novel extended Kalman filter formulation is used in estimating the relative 3D motion trajectories up to a scale factor. The recursive estimation process provides a prediction and error measure that is exploited in higher-level stages of action recognition. Conversely, higher-level mechanisms provide feedback that allows the system to reliably segment and maintain the tracking of moving objects before, during, and after occlusion. The 3D trajectory, occlusion, and segmentation information are utilized in extracting stabilized views of the moving object that are then used as input to action recognition modules. Trajectory-guided recognition (TGR) is proposed as a new and efficient method for adaptive classification of action. The TGR approach is demonstrated using ``motion history images'''' that are then recognized via a mixture-of-Gaussians classifier. The system was tested in recognizing various dynamic human outdoor activities: running, walking, roller blading, and cycling. Experiments with real and synthetic data sets are used to evaluate stability of the trajectory estimator with respect to noise. (This technical report supercedes TR''s [98-020] and [98-007])
Molecular basis of agonist docking in a human GPR103 homology model by site‐directed mutagenesis and structure–activity relationship studies

The neuropeptide 26RFa and its cognate receptor GPR103 are involved in the control of food intake and bone mineralization. Here, we have tested, experimentally, the predicted ligand‐receptor interactions by site‐directed mutagenesis of GPR103 and designed point‐substituted 26RFa analogues.
Relative factors in performance analysis of Java virtual machines

Many new Java runtime optimizations report relatively small, single-digit performance improvements. On modern virtual and actual hardware, however, the performance impact of an optimization can be influenced by a variety of factors in the underlying systems. Using a case study of a new garbage collection optimization in two different Java virtual machines, we show the relative effects of issues that must be taken into consideration when claiming an improvement. We examine the specific and overall performance changes due to our optimization and show how unintended side-effects can contribute to, and distort the final assessment. Our experience shows that VM and hardware concerns can generate variances of up to 9.5% in whole program execution time. Consideration of these confounding effects is critical to a good, objective understanding of Java performance and optimization.
Soap-bubble optimization of gaits

In this paper, we present a geometric variational algorithm for optimizing the gaits of kinematic locomoting systems. The dynamics of this algorithm are analogous to the physics of a soap bubble, with the system's Lie bracket supplying an “inflation pressure” that is balanced by a “surface tension” term derived from a Riemannian metric on the system's shape space. We demonstrate this optimizer on a variety of system geometries (including Purcell's swimmer) and for optimization criteria that include maximizing displacement and efficiency of motion for both translation and turning motions.
Cognitive activity and suggestions for analgesia in the reduction of reported pain.

Pain magnitude and pain tolerance for arm immersion in ice water were assessed during a baseline and posttest session. Before the posttest half the subjects received (and half did not receive) an analgesia suggestion. On the basis of their written testimony, subjects were classified as having either predominantly coped (e.g., imagined events inconsistent with pain; made positive self-statements) or predominantly exaggerated (e.g., worried about and exaggerated the noxious aspects of the situation) during each immersion. On both immersions copers reported less pain and exhibited higher pain tolerance than exaggerators. Moreover, the suggestion was associated with reductions in reported pain only when it transformed baseline exaggerators into posttest copers. Theoretical implications are discussed.
Design of interlock-free combined allocators for Networks-on-Chip

This paper presents a thorough investigation of the combined allocator design for Networks-on-Chip (NoC). Particularly, we discuss the interlock of the combined NoC allocator, which is caused by the lock mechanism of priority updating between the local and global arbiters. Architectures and implementations of three interlock-free combined allocators are presented in detail. Their cost, critical path, as well as networklevel performance are demonstrated based on 65-nm standard cell technology.
Performance and wake characteristics of a tidal turbine under yaw

In tidal streams and rivers, the flow of water can be at yaw to the turbine rotor plane causing performance degradation and a skewed downstream wake. The current study aims to quantify the performance variation and associated wake behavior caused by a tidal turbine operating in a yawed inflow environment. A three-dimensional computational fluid dynamics study was carried out using multiple reference frame approach using κ-ω SST turbulence model with curvature correction. The computations were validated by comparison with experimental results on a 1:20 scale prototype for a 0° yaw case performed in a laboratory flume. The simulations were performed using a three-bladed, constant chord, untwisted tidal turbine operating at uniform inflow. Yaw effects were observed for angles ranging from 5° to 15°. An increase in yaw over this range caused a power coefficient deficit of 26% and a thrust coefficient deficit of about 8% at a tip speed ratio of 5 that corresponds to the maximum power coefficient for the tested turbine. In addition, wake propagation was studied up to a downstream distance of ten rotor radius, and skewness in the wake, proportional to yaw angle was observed. At higher yaw angles, the flow around the turbine rotor was found to cushion the tip vortices, accelerating the interaction between the tip vortices and the skewed wake, thereby facilitating a faster wake recovery. The center of the wake was tracked using a center of mass technique. The center of wake analysis was used to better quantify the deviation of the wake with increasing yaw angle. It was observed that with an increase in yaw angle, the recovery distance moved closer to the rotor plane. The wake was noticed to meander around the turbine centerline with increasing downstream distance and slightly deviate towards the free surface above the turbine centerline, magnitude of which varied depending on yaw.