Miracle-dz/UniFAR-FTUs huggingface dataset:
Facilitating Load Distribution Based on Microkernel

The Distributed Scheduling Framework (DSF) is a load distribution (LD) tool built on top of the Mach operating system. It allows the replacement of LD algorithms without the need to shut down the system. It also supports multiple LD algorithms running on the system concurrently so that users can choose a suitable LD algorithm for submitting their tasks according to their choice. Implementing LD algorithm can be easier by using services provided by DSF. This paper describes the design objectives and the architecture of DSF. Methods to make use of DSF are also presented.
A wireless sensor system for motion analysis of Parkinson's disease patients

We describe the initial design, implementation and testing of a wearable sensor system employed for human motion analysis. Our proposed system is part of an ongoing investigation aimed at efficiently timing the self-administration of prescription drugs in Parkinson's disease patients by using wireless sensors to capture distinctive motion patterns that indicate the onset of dyskinesia lapses as the prescription drug wears off. Our prototype incorporates three pairs of accelerometer/gyroscope sensors, each connected to a wireless node equipped with an IEEE 802.15. 4 radio. Sensor data are transmitted to a computer that is employed for visualization. We describe practical experience encountered during the initial development of our prototype, and outline the potential battery and bandwidth conservation benefits introduced by employing popular signal processing methods.
Differential Protein Expression in the Nucleus Accumbens and Amygdala of Lewis and Fischer 344 Rats, and its Relevance in Drug Addiction

The Lewis (LEW) and Fischer 344 (F344) rat strains are widely used as models of genetic vulnerability to drug addiction, par- ticularly since these two strains show different patterns of drug self- administration. We previously performed an unbiased and genome- wide microarray analysis to define the differences in gene expression in the nucleus accumbens (NAcc) and prefrontal cortex between LEW and F344 rats. In this study, we set out to determine the strain differences in the NAcc and amygdala at the protein level using bidimensional gel electrophoresis. As such, 11 proteins were found to be differen- tially expressed between these two strains, the activity of which was mainly related to vesicular trafficking (STXBP1 and GDI1), energy metabolism (ATP5B, ENO2, and ACOT7), and cytoskeletal formation. This information might be useful to search for new molecular targets to screen individuals at high risk of developing an addictive disorder.
IP agnostic real-time traffic filtering and host identification using TCP timestamps

In this work, we describe and evaluate the design and implementation of natfilterd, a flexible and lightweight extension of the Linux netfilter packet filter framework, which enables us to identify hosts completely independent of IP addresses by taking advantage of certain characteristics of TCP timestamps. As an immediate consequence, not only can we count hosts behind a NAT gateway but block TCP traffic from single hosts without blocking the gateway itself. Our work extends ideas from Bursztein, which we improve in terms of performance as well as matching quality and usability in practice. A theoretical runtime of O(log(n)) for matching packets against a database of n hosts is achieved. We empirically verify this result and conclude that our approach scales extremely well and is therefore suitable for at least medium-scale networks of a few thousand hosts.
Modules With Proofs

The ML module system provides proven mechanisms for organizing and maintaining large programs through the use of structures, to implement program units, and signatures, that give an abstract speci cation of structures. A signature is a partial speci cation: it simply lists the components of the structure together with their types. For domains with security or timing requirements, it is important to give stronger guarantees about program behavior. We address this issue by augmenting program signatures with formal speci cations, and structures with proofs. Security speci cations are given in terms of a type theoretic interpretation of the program implementation. The type theory required to support this extension is the translucent sum calculus of Harper and Lillibridge extended with equality and constraint types. The Curry-Howard isomorphism provides a correspondence between executable programs and their proofs, allowing control over the degree of security desired.
A novel framework for Miscellaneous data aggregation in wireless sensor networks paper title

considering the data-centric feature of wireless sensor networks and different data has the different requirement of real-time. A new data aggregation algorithm- Miscellaneous data aggregation algorithm based on timeliness of data in wireless sensor networks (MDA) is proposed. This algorithm can adaptability adjusts the data fusion strategy according to the results of the analysis of data time-lines in Application Layer. Moreover, AIDA Layer can dynamically control the degree of aggregation (DOA) in accordance with changing traffic conditions. According to theoretical analysis and calculation results, the algorithm can both satisfy the realtime requirement of different data and decrease the number of redundant data, save energy.
Vision Based Navigation of Aerial Drones over The Rain Forest : Extracting and Tracking Natural Landmarks

There is a sort of important scenarios, such as long endurance missions, where an autonomous drone would be very effective. One of these scenarios of applications is the long term monitoring of the Amazon rain forest. The uniform pattern of the canopy defines a mission difficult to be performed by a human operator. In such situation, an embedded vision system capable to drive the vehicle while taking decision of what is not fitting to a standard canopy pattern plays a critical role on both remotely operated and autonomous navigation modes. This work presents a scheme based on image processing able to extract natural landmarks in forest areas, and to track them during posterior missions over the same area, as reference for the onboard navigation system. The scheme is composed of two main steps: nonrelevant features suppression based on wavelet, to eliminate the canopy uniform pattern and keypoints extraction by SIFT algorithm, to track natural landmarks. Preliminary results demonstrated that this system can increase the robustness of mission execution in scenarios where usually only GPS references are available. Keywords-component: natural landmark tracking; feature suppression; wavelet transform; unmanned aerial systems
Diierential Frost Heave in Seasonally Frozen Soils

The formation of some forms of patterned ground, notably earth hummocks and stone circles, is associated with seasonal freezing and a spatial instability in the resulting frost heave. We analyse the Miller model of frost heave for such spatial instability, by incorporating three-dimensionsal heat and mass transfer, and allowing the frozen soil to deform as a viscous medium. We nd that the heaving process is generally (but not always) stable, but that if account is taken of a surface snow cover, then the insulating thermal properties of the snow predict that instability will occur if a dimensionless parameter N > 0:02. The parameter N is given by N = f v s =(f gd 2), where f is the frozen soil viscosity, v s is the surface heave rate, f is the frozen soil density, g is gravity, and d is the depth of the freezing front. This implies that the propensity for diierential frost heave depends on the soil heaving characteristics, as well as the rate of frost penetration.
Enhancing Students’ Academic Performance in Malaysian Online Distance Learning Institutions

The increasing number of learner failure rates are alarming in online distance learning. Previous studies have identified the factors that have contributed to online distance learning students’ failure as lack of time and lack of motivation. The purpose of this study is to develop a direct effect understanding of extrinsic motivation, intrinsic motivation, self-efficacy and time management on students’ academic performance in an online distance learning institution in Malaysia. The Structural Equation Model (SEM) was used to analyse the casual relationships between independent variables and dependent variables. The model was developed and later tested by adopting the Partial Least Square (PLS) procedure on data collected from a survey that yielded 210 usable questionnaires. The findings showed that extrinsic motivation, intrinsic motivation, self-efficacy and time management have a significant and positive influence on students’ academic performance in an online distance learning institution. The findings imply that the relationship amongst extrinsic motivation, intrinsic motivation, self-efficacy and time management on a student’s academic performance in an online distance learning institution will lead to the online distance learning institution’s low attrition rate. This study uses SmartPLS 2.0 and SPSS 18.0 to test the hypothesis and analyse respondents’ profile, respectively.
HAGE in Triple-Negative Breast Cancer Is a Novel Prognostic, Predictive, and Actionable Biomarker: A Transcriptomic and Protein Expression Analysis

Purpose: The expression of HAGE as a novel prognostic and predictive tool was assessed in 1,079 triple-negative breast cancers (TNBC). Experimental Design: HAGE protein expression was investigated in an early primary TNBC (EP-TNBC; n = 520) cohort who received adjuvant chemotherapy (ACT) and in a locally advanced primary TNBC cohort who received anthracycline combination Neo-ACT (n = 110; AC-Neo-ACT). HAGE-mRNA expression was evaluated in the METABRIC-TNBC cohort (n = 311) who received ACT and in a cohort of patients with TNBC who received doxorubicin/cyclophosphamide Neo-ACT, followed by 1:1 randomization to ixabepilone (n = 68) or paclitaxel (n = 64) as part of a phase II clinical trial. Furthermore, a cohort of 128 tumors with integrated HAGE gene copy number changes, mRNA, and protein levels were analyzed. Results: In patients with EP-TNBC, who were chemotherapy-naïve, high HAGE protein expression (HAGE+) was associated with a higher risk of death [HR, 1.3; 95% confidence interval (CI), 1.2–1.5; P = 0.000005] when compared with HAGE− cases. Patients who received ACT and expressed mRNA-HAGE+ were at a lower risk of death than those who were mRNA–HAGE− (P = 0.004). The expression of HAGE was linked to the presence of tumor-infiltrating lymphocytes (TIL), and both features were found to be independent predictors for pathologic complete response (pCR, P < 0.001) and associated with prolonged survival (P < 0.01), following AC-Neo-ACT. In patients with residual disease, HAGE+ had a 2-fold death risk increase (P = 0.018) compared with HAGE−. Conclusions: HAGE expression is a potential prognostic marker and a predictor of response to anthracycline treatment in TNBC. A prospective clinical trial to examine the therapeutic value of HAGE for TNBC cases is warranted. Clin Cancer Res; 22(4); 905–14. ©2015 AACR.
Trajectory Optimization Through Contacts and Automatic Gait Discovery for Quadrupeds

In this letter, we present a trajectory optimization framework for whole-body motion planning through contacts. We demonstrate how the proposed approach can be applied to automatically discover different gaits and dynamic motions on a quadruped robot. In contrast to most previous methods, we do not prespecify contact-switches, -timings, -points or gait patterns, but they are a direct outcome of the optimization. Furthermore, we optimize over the entire dynamics of the robot, which enables the optimizer to fully leverage the capabilities of the robot. To illustrate the spectrum of achievable motions, we show eight different tasks, which would require very different control structures when solved with state-of-the-art methods. Using our trajectory optimization approach, we are solving each task with a simple, high level cost function and without any changes in the control structure. Furthermore, we fully integrate our approach with the robot's control and estimation framework such that we are able to run the optimization online. Through several hardware experiments, we show that the optimized trajectories and control inputs can be directly applied to physical systems.
Epidemiology of nasopharyngeal carcinoma in the United States: improved survival of Chinese patients within the keratinizing squamous cell carcinoma histology.

BACKGROUND This study examined potential survival differences among nasopharyngeal carcinoma (NPC) patients from various ethnicities in the United States. PATIENTS AND METHODS A total of 2436 newly diagnosed NPC patients from 1992 to 2002 were analyzed from the population-based Surveillance, Epidemiology, and End Results (SEER) program. Five-year survival rate estimates and Kaplan-Meier survival curves were calculated. Cox proportional hazard ratios (HRs) were used to identify independent prognostic factors for survival. RESULTS By multivariate analyses, early age of diagnosis, localized stage at presentation (versus distant, HR=0.35; P<0.0001), radiation therapy (versus none; HR=0.48; P<0.0001), undifferentiated non-keratinizing carcinoma (versus keratinizing squamous cell carcinoma; HR=0.67; P<0.0001), and Chinese ethnicity (versus Caucasian; HR=0.78; P=0.0010) were associated with improved survival. Within keratinizing squamous cell carcinoma histology, the survival advantage of Chinese patients remained even after adjustment for other prognostic factors. CONCLUSIONS The significant survival advantage of Chinese NPC patients within the keratinizing squamous cell carcinoma histology contributed largely to Chinese ethnicity being an independent and favorable prognostic factor for survival in NPC.
Further results on trellis coding of spread spectrum multiple access systems

The performance of trellis coding is investigated when it is applied to a spread spectrum multiple access (SSMA) system using a conventional convolutional code. This comparison is carried out using criteria of equal complexity and throughput. The numerical results illustrate that, for a given complexity, chip rate, and throughput, low rate convolutional codes provide the best performance in a SSMA system. The coding gains range from 1 to 8 dB (depending on the type and complexity of the code) over the corresponding uncoded performances of SSMA in an additive white Gaussian noise channel. The validity of approximating the interuser interference as a Gaussian random variable is also investigated. <<ETX>>
Plasma Levels and Diagnostic Utility of Macrophage Colony-Stimulating Factor, Matrix Metalloproteinase-9, and Tissue Inhibitor of Metalloproteinases-1 as New Biomarkers of Breast Cancer

Background Macrophage colony-stimulating factor (M-CSF), matrix metalloproteinase-9 (MMP-9), and its specific tissue inhibitor - tissue inhibitor of metalloproteinases-1 (TIMP-1) may play an important role in the pathogenesis and spread of cancer. We investigated the plasma levels of M-CSF, MMP-9, and TIMP-1 in comparison with a commonly accepted tumor marker CA 15-3 in breast cancer patients and in control groups. Methods The cohort included 110 breast cancer patients in groups at stages I-IV. The control group consisted of 50 healthy volunteers and 50 benign tumor patients. Plasma levels of M-CSF, MMP-9, and TIMP-1 were determined by using ELISA, while CA 15-3 concentrations were determined by using chemiluminescent microparticle immunoassay (CMIA). Results The results showed significant differences in concentrations of the analyzed parameters and in levels of CA 15-3 between the groups of breast cancer patients and the two control groups. Diagnosis using these markers was equal to that using CA 15-3 in terms of sensitivity, predictive values of positive and negativetest results (PPV, NPV) and area under the ROC curve (AUC) in the studied groups. The diagnostic specificities of MMP-9, TIMP-1, M-CSF, and CA 15-3 showed equally high values (95%). The combined use of all tested parameters with CA 15-3 resulted in increased sensitivity, NPV, and AUC, especially in the combination of M-CSF with tumor markers (76%, 64%, and 0.8653). Conclusions These findings suggest the tested parameters are useful in the diagnosis of breast cancer patients (except stage I), when combined with CA 15-3.
How Big Were the First Planetesimals? Does Size Matter?

Introduction: The terrestrial planets formed by collisional accretion of small planetesimals [1,2]. Stochastic coagulation and runaway growth yield an “oligarchy” of embryos with similar sub-planetary masses [3] that then collide to yield planets [4-5]. A common assumption is km-sized initial planetesimals, based on the scale of gravitational instability in a dust layer in a quiescent solar nebula [6-7]. This mechanism is untenable due to shear-induced turbulence [8]. Alternative mechanisms [9-11] depend on poorly constrained properties of particles (sticking efficiency, impact strength) and/or the nebula (turbulent velocity, eddy timescale), and do not predict planetesimal sizes from first principles. Here we investigate by numerical modeling effects of initial planetesimal size on outcomes of accretion. C o llis io na l O utco m es: C o ll is ion between planetesimals yield net growth or destruction, depending on size, strength, and impact velocity. Impact strength Q* is defined as specific energy to remove half of the target's mass. This involves fracturing the target and partitioning kinetic energy to fragments that escape their mutual gravity. Small bodies are dominated by material strength; large ones by self-gravity. Scaling laws yield Q* with a minimum at roughly km size [12]. We assume 1% of impact energy is partitioned into fragment kinetic energy. Collisional Modeling: We use the PSI multi-zone accretion code [13]. The swarm has surface density
Exploiting coupled joints - anatomic control of the spine with ik through linearly coupled joints

In this paper we propose a simple model for the coupling behavior of the human spine that is capable of exhibiting anatomically correct motions of the vertebrae in virtual mannequins. Such a model transparently integrates in our inverse kinematics framework as it couples standard swing and revolute joint models. The adjustment of the joints due to the coupling is made with several simple (in)equality constraints, resulting in a reduction of the solution space dimensionality for the inverse kinematics solver. A key benefit is to prevent the inverse kinematics algorithm from providing infeasible postures. We exploit how to apply these simple constraints to the human spine by a strict decoupling of the swing and twist motion of the vertebrae and demonstrate the validity of our approach on various experiments.
The influence of group and context on information technology development creativity

The purpose of this paper is to help understand how to foster the kind of IT development creativity that can lead to dramatic organizational change. To this end, a creativity model focusing on the impact of group and contextual characteristics on group creative processes is adapted from the organizational literature. This model is employed to help understand the lack of creativity exhibited in an imaging technology development project. The model is then used to identify variables important in IT development creativity research, and provide an understanding of how, they work to influence creativity. This understanding can aid in the future creation and testing of IT development creativity models. <<ETX>>
The Nonlinear Rescaling

Because the process of observation deenes a natural topological nesting of geometric structures of the jet bundle, we propose to model this process by a nonlinear evolution equation that we may obtain upon changing gradually the metric underlying the linear isotropic rescaling process. We a propose to derive a nonlinear rescaling process that is connected to the linear isotropic rescaling process by an one-to-one mapping. In order to study the scale space generated by this nonlinear rescaling process, we propose to use the linear rescaling process.
Efficacy of hyperbaric oxygen treatment for depression in the convalescent stage following cerebral hemorrhage

The present study aimed to evaluate the clinical efficacy of hyperbaric oxygen (HBO) treatment for depression in the convalescent stage following cerebral hemorrhage. A total of 60 cases of patients with depression in the convalescent stage following cerebral hemorrhage (2–6 months) were randomly divided into the treatment group (treated with HBO, 30 cases) and the control group (treated with Deanxit, 30 cases). Prior to treatment and at 4 weeks post-treatment, efficacy was evaluated by the Hamilton Depression Scale (HAMD) and nerve function defect scores. There was a significant difference in the total efficacy between the two groups (P<0.05), and a significant difference in the HAMD scores (P<0.05). There were also significant differences between the pre- and post-treatment HAMD scores within the two groups (both P<0.05). HBO is able to significantly improve the degree of depression in the convalescent stage following cerebral hemorrhage and also promote nerve function recovery.
Stratified sampling and quasi-Monte Carlo simulation of Lévy processes

We provide a method for the generation of paths of Lévy processes which allows for more efficient simulation than crude step-by-step generation. We show how, using our method, one can apply stratified sampling and quasi-Monte Carlo methods to obtain better numerical schemes analog to the Brownian case. As a numerical example we consider the problem of pricing an asian option in the so-called hyperbolic market model.
Diversity of Motivations to Engage in Tax Evasion

This paper identifies types of tax evaders and explores their divergent motivations for evading taxation. The data derives from two large-scale population surveys conducted in 2008 and 2012 in Hungary. Cluster analysis captured two distinct types of tax evaders: 1) poverty escape-type and 2) better-off fake entrepreneurship-type, partially confirming existing typologies. Logistic regression analysis reveals that these two groups are characterised by distinct motivations to evade taxation: the first group acts predominantly out of necessity; the second group’s choice is primarily driven by rational calculation.