Miracle-dz/UniFAR-FTUs huggingface dataset:
Workload Partitioning for Accelerating Applications on Heterogeneous Platforms

Heterogeneous platforms composed of multi-core CPUs and different types of accelerators, like GPUs and Xeon Phi, are becoming popular for data parallel applications. The heterogeneity of the hardware mix and the diversity of the applications pose significant challenges to exploiting such platforms. In this situation, an effective workload partitioning between processing units is critically important for improving application performance. This partitioning is a function of the hardware capabilities as well as the application and the dataset to be used. In this work, we present a systematic approach to solve the partitioning problem. Specifically, we use modeling, profiling, and prediction techniques to quickly and correctly predict the optimal workload partitioning and the right hardware configuration to use. Our approach effectively characterizes the platform heterogeneity, efficiently determines the accurate partitioning, and easily adapts to new platforms, different application types, and different datasets. Experimental evaluation on 13 applications shows that our approach delivers excellent performance improvement of 1.2 <inline-formula><tex-math notation="LaTeX">$\times$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="shen-ieq1-2509972.gif"/></alternatives></inline-formula>–14.6<inline-formula> <tex-math notation="LaTeX">$\times$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="shen-ieq2-2509972.gif"/></alternatives></inline-formula> over a single-processor execution, and accurate partitioning with in most cases below 10 percent performance gap versus an oracle-based partitioning.
Dynamic Proteomics Emphasizes the Importance of Selective mRNA Translation and Protein Turnover during Arabidopsis Seed Germination*

During seed germination, the transition from a quiescent metabolic state in a dry mature seed to a proliferative metabolic state in a vigorous seedling is crucial for plant propagation as well as for optimizing crop yield. This work provides a detailed description of the dynamics of protein synthesis during the time course of germination, demonstrating that mRNA translation is both sequential and selective during this process. The complete inhibition of the germination process in the presence of the translation inhibitor cycloheximide established that mRNA translation is critical for Arabidopsis seed germination. However, the dynamics of protein turnover and the selectivity of protein synthesis (mRNA translation) during Arabidopsis seed germination have not been addressed yet. Based on our detailed knowledge of the Arabidopsis seed proteome, we have deepened our understanding of seed mRNA translation during germination by combining two-dimensional gel-based proteomics with dynamic radiolabeled proteomics using a radiolabeled amino acid precursor, namely [35S]-methionine, in order to highlight de novo protein synthesis, stability, and turnover. Our data confirm that during early imbibition, the Arabidopsis translatome keeps reflecting an embryonic maturation program until a certain developmental checkpoint. Furthermore, by dividing the seed germination time lapse into discrete time windows, we highlight precise and specific patterns of protein synthesis. These data refine and deepen our knowledge of the three classical phases of seed germination based on seed water uptake during imbibition and reveal that selective mRNA translation is a key feature of seed germination. Beyond the quantitative control of translational activity, both the selectivity of mRNA translation and protein turnover appear as specific regulatory systems, critical for timing the molecular events leading to successful germination and seedling establishment.
QoS Controllers for the Internet

Abstract In this position paper, we review basic control strategies that machines acting as "traffic controllers" could deploy in order to improve the management of Internet services. Such traffic controllers are likely to spur the widespread emergence of advanced applications, which have (so far) been hindered by the inability of the networking infrastructure to deliver on the promise of Quality-of-Service (QoS).
Mode I crack analysis in single crystals with anisotropic discrete dislocation plasticity: I. Formulation and crack growth

Analyses of monotonic loading of a plane-strain mode I crack in an fcc single crystal under small-scale yielding are carried out using discrete dislocation plasticity (DDP) incorporating anisotropic elasticity. Two crystallographically symmetric crack orientations are considered where plane-strain plastic deformation is achieved by the nucleation and glide of edge dislocations on three effective slip systems. A cohesive surface ahead of the initial crack tip, endowed with the universal binding law, allows for the crack to grow. Simulations are carried out in an incremental way, using the superposition of the singular fields of dislocations in an anisotropic half-space and image fields that enforce boundary conditions and the cohesive properties. Considering a wide range of DDP parameters, i.e. source and obstacle densities, the dependence of fracture characteristics on these parameters is demonstrated. The crack growth resistance is found to be orientation-dependent and slightly smaller if the elastic anisotropy of the crystal is taken into consideration. Investigations of plastic dissipation and dislocation density evolution reveal that the dislocation structure evolving around the crack tip plays a key role in the fracture process.
LINEAR ANISOTROPIC MESH FILTERING

No algorithm for unconstrained polygon mesh denoising can beat Laplacian smoothing in simplicity and ease of implementation. In this paper we introduce a new algorithm for polygon meshes smoothing with vertex position and face normal interpolatory constraints composed of two phases. First the face normals are filtered independently of vertex positions. Then the vertex positions are filtered integrating the face normals in the least squares sense. Laplacian smoothing is used to smooth both the face normal field and the vertex positions, with a properly defined Laplacian operator in each case. We define isotropic, anisotropic, linear, and non-linear Laplacian operators for signals defined in Euclidean space, and on the unit sphere. In addition to the obvious applications to shape design, our algorithm constitutes a new fast and linear solution to the tangential drift problem, observed in meshes with irregular edge length and angle distribution. We show how classical linear filter design techniques can be applied in both cases, and conclude the paper determining integrability conditions for face normal fields. CR
The posture control of a two-link free flying acrobot with initial angular momentum

This note studies the posture control problem of a two-link free flying Acrobot with nonzero initial angular momentum, where the control objective is to design an acceleration based control law such that the robot can pass through a desired posture at a given time. Based on the previous results, this posture control problem can be tackled by solving the problem of stabilizing the origin of a system obtained via an appropriate coordinate transformation. The main contribution of this note is to provide a control scheme which globally asymptotically stabilizes the origin of the system. The numerical simulations are given to validate the provided theoretical results.
Contours and borders in animated mimic displays

Previous research has indicated that ambiguities in apparent motion (e.g., direction, rate) can result when color table techniques are used to produce animation in mimic displays. Two experiments were conducted to investigate alternative display designs in which contours (angled vs. straight) and borders (explicit vs. implicit) were varied. In Experiment 1, contours, borders, and temporal frequency interacted. At 5 Hz angled contours improved accuracy significantly. At 10 Hz explicit borders improved accuracy significantly with angled contours but degraded accuracy significantly with straight contours. In Experiment 2, the design of the angled contours was changed to convey less information and an additional border condition was added. Once again, borders and contours interacted: The widest explicit border degraded accuracy with straight contours. Angled contours also improved latency performance. The results suggest that angled contours can reduce ambiguity and improve the effectiveness of animated mimic.
Estimating the Total Economic Value of Cultivated Flower Land in Taiwan

Many arable land areas have been converted to residential or business uses by Taiwan government authorities, because the low farmland value is associated with the low value of agricultural products. However, agriculture is multifunctional. This study investigates farmland value through Total Economic Value (TEV) for Tianwei Township, which is Taiwan’s largest floral farmland region. Direct use value measures the floral products’ output value and recreational benefit. Recreational benefit from visitors’ flower sightseeing was measured by the travel cost method (TCM). Option value and non-use value, including bequest value and existence value, measure the residents’ willingness to pay through the double-bounded dichotomous contingent valuation method (CVM). The results show that the total floral products’ output is NT$1.441 billion in 2007, recreational benefit is roughly NT$17.757 billion. The intangible value of option value and non-use values are approximately between NT$5 million to 15 million. Therefore, ignoring various values of farmland might lead to an underestimation of farmland value.
A bayesian kernel for the prediction of protein-protein interactions

Understanding proteins functions is a major goal in the post-genomic era. Proteins usually work in context of other proteins and rarely function alone. Therefore, it is highly relevant to study the interaction partners of a protein in order to understand its function. Machine learning techniques have been widely applied to predict protein-protein interactions. Kernel functions play an important role for a successful machine learning technique. Choosing the appropriate kernel function can lead to a better accuracy in a binary classifier such as the support vector machines. In this paper, we describe a Bayesian kernel for the support vector machine to predict protein-protein interactions. The use of Bayesian kernel can improve the classifier performance by incorporating the probability characteristic of the available experimental protein-protein interactions data that were compiled from different sources. In addition, the probabilistic output from the Bayesian kernel can assist biologists to conduct more research on the highly predicted interactions. The results show that the accuracy of the classifier has been improved using the Bayesian kernel compared to the standard SVM kernels. These results imply that protein-protein interaction canbe predicted using Bayesian kernel with better accuracy compared to the standard SVM kernels.
Immunohistochemical Antibodies in Breast Cancer HER2 Diagnostics

Overexpression and/or gene amplification of the HER2 oncogene predicts worse prognosis and altered sensitivity to chemotherapy. Trastuzumab is capable of improving prognosis of HER2-positive breast cancer, but for the success of treatment appropriate HER2 testing is essential. Our aim was to determine the value of immunohistochemical (IHC) screening prior to fluorescence in situ hybridization (FISH). We assessed five conventional IHC assays (NCL-CB11, Pathway CB11, CBE356, CBE1, HercepTest) and the novel rabbit monoclonal antibody, RM-4B5, combined with FISH on 199 invasive breast cancer cases. Taking FISH as the endpoint, we calculated sensitivity, specificity, positive and negative predictive values (PPV, NPV) and accuracy for all IHC assays with either taking both 2+/3+ cases or only 3+ cases as IHC positives. With 2+/3+ cases HercepTest showed 100% sensitivity and NPV, while the highest specificity, PPV and accuracy was associated with RM-4B5 (97.36, 80 and 95.34%, respectively). The second highest values belonged to either NCL-CB11 or Pathway CB11. When calculating only with 3+ cases, the results were reversed with increased specificity, PPV and accuracy. Our findings suggest that improving sensitivity by using two parallel IHC reactions might be beneficial; we recommend primarily HercepTest and Pathway CB11. Nevertheless, we may consider performing FISH analysis without prior IHC screening.
A Benchmark Comparison of Monocular Visual-Inertial Odometry Algorithms for Flying Robots

Flying robots require a combination of accuracy and low latency in their state estimation in order to achieve stable and robust flight. However, due to the power and payload constraints of aerial platforms, state estimation algorithms must provide these qualities under the computational constraints of embedded hardware. Cameras and inertial measurement units (IMUs) satisfy these power and payload constraints, so visual-inertial odometry (VIO) algorithms are popular choices for state estimation in these scenarios, in addition to their ability to operate without external localization from motion capture or global positioning systems. It is not clear from existing results in the literature, however, which VIO algorithms perform well under the accuracy, latency, and computational constraints of a flying robot with onboard state estimation. This paper evaluates an array of publicly-available VIO pipelines (MSCKF, OKVIS, ROVIO, VINS-Mono, SVO+MSF, and SVO+GTSAM) on different hardware configurations, including several single-board computer systems that are typically found on flying robots. The evaluation considers the pose estimation accuracy, per-frame processing time, and CPU and memory load while processing the EuRoC datasets, which contain six degree of freedom (6DoF) trajectories typical of flying robots. We present our complete results as a benchmark for the research community.
Cerebrospinal Fluid in Cerebral Hemorrhage and Infarction

Cerebrospinal fluid (CSF) abnormalities were correlated with pathological diagnoses in 61 patients with autopsy-verified intracerebral hemorrhage or cerebral infarction. Lumbar punctures were performed within one week of onset of symptoms. The CSF color and red blood cell counts were the most useful CSF parameters in differentiating between intracerebral hemorrhage and cerebral infarction. In 75% of the patients with intracerebral hemorrhage, the CSF was either grossly bloody or xanthochromic; in 25%, the CSF was clear. In patients with cerebral infarction, the CSF was never grossly bloody; in two patients with hemorrhagic infarction, the CSF was xanthochromic. The CSF pressure, protein values and leukocyte counts were less useful in differentiating intracerebral hemorrhage from cerebral infarction. Cases with hemorrhagic infarction could not be separated from those with ischemic infarction on the basis of CSF analysis. In clear CSF, the polymorphonuclear neutrophilic leukocyte (PNL) counts were never greater than 20 per cubic millimeter. In xanthochromic or cloudy CSF, leukocyte counts, especially PNLs, were frequently elevated, occasionally to high levels.
Genomic Landscape Survey Identifies SRSF1 as a Key Oncodriver in Small Cell Lung Cancer

Small cell lung cancer (SCLC) is an aggressive disease with poor survival. A few sequencing studies performed on limited number of samples have revealed potential disease-driving genes in SCLC, however, much still remains unknown, particularly in the Asian patient population. Here we conducted whole exome sequencing (WES) and transcriptomic sequencing of primary tumors from 99 Chinese SCLC patients. Dysregulation of tumor suppressor genes TP53 and RB1 was observed in 82% and 62% of SCLC patients, respectively, and more than half of the SCLC patients (62%) harbored TP53 and RB1 mutation and/or copy number loss. Additionally, Serine/Arginine Splicing Factor 1 (SRSF1) DNA copy number gain and mRNA over-expression was strongly associated with poor survival using both discovery and validation patient cohorts. Functional studies in vitro and in vivo demonstrate that SRSF1 is important for tumorigenicity of SCLC and may play a key role in DNA repair and chemo-sensitivity. These results strongly support SRSF1 as a prognostic biomarker in SCLC and provide a rationale for personalized therapy in SCLC.
Increased Sorting and Wage Inequality in the Czech Republic

In this paper, we look at the evolution of firms’ wage structures using a linked employeremployee dataset, which has longitudinal information for firms and covers a large fraction of the Czech labor market during the period 1998-2006. We first look at the evolution of individual wage determination and find evidence of slightly increasing returns to human capital and diminishing gender inequality. We then document sharp increases in both within-firm and between-firm inequality. We investigate various hypotheses to explain these patterns: increased domestic and international competition, an increasingly decentralized wage bargaining, skill biased technological change and a changing educational composition of the workforce. We find some support for that all these factors have contributed to the changes in the Czech wage structure, and that increased sorting is strongly associated with the observed changes in wage inequality.
Beyond Conflict Monitoring

Cognitive control refers to the processes by which individual cognitive functions are coordinated in the service of higher-level goals. The anterior cingulate cortex (ACC) in the middle front of the brain monitors performance, and it is activated when the need for control increases—for instance, in difficult situations or when errors occur. Since the late 1990s, the ACC has been thought to signal when there is conflict between competing action plans so that the conflict can be resolved. More recently, an alternative model has reconceptualized the role of ACC as that of a predictor and evaluator of the likely outcomes of planned actions before the actions are performed. This new Predicted Response Outcome model accounts for a broader range of findings than the earlier framework and suggests that the ACC might support the cognitive operations by which individuals can “think before they act” in order to avoid risky or otherwise poor choices.
Issues in Stacked Generalization

Stacked generalization is a general method of using a high-level model to combine lower-level models to achieve greater predictive accuracy. In this paper we address two crucial issues which have been considered to be a 'black art' in classification tasks ever since the introduction of stacked generalization in 1992 by Wolpert: the type of generalizer that is suitable to derive the higher-level model, and the kind of attributes that should be used as its input. We find that best results are obtained when the higher-level model combines the confidence (and not just the predictions) of the lower-level ones. We demonstrate the effectiveness of stacked generalization for combining three different types of learning algorithms for classification tasks. We also compare the performance of stacked generalization with majority vote and published results of arcing and bagging.
Composition and Antimicrobial Activity of the Leaf Essential Oil of Litsea Nakaii from Taiwan

The leaf essential oil of Litsea nakaii was isolated by hydrodistillation and analyzed to determine its composition and yield. Fifty-five compounds were identified, the main components being α-humulene (15.5%), δ-cadinene (9.2%), (E)-β-ocimene (8.1%), and δ-selinene (7.1%). The leaf oil exhibited excellent antimicrobial activities.
The Returns to Computer Use Revisited: Have Pencils Changed the Wage Structure Too?

Are the large measured wage differentials for on-the-job computer use a true return to computer skills, or do they just reflect that higher wage workers use computers on their jobs? We examine this issue with three large cross-sectional surveys from Germany. First, we confirm that the estimated wage differential associated with computer use in Germany is very similar to the U. S. differential. Second, we also measure large differentials for on-the-job use of calculators, telephones, pens or pencils, or for those who work while sitting down. We argue that these findings cast some doubt on the literal interpretation of the computer use wage differential as reflecting true returns to computer use or skill.
Gating of Single N-type Calcium Channels Recorded from Bullfrog Sympathetic Neurons

For many neurons, N-type calcium channels provide the primary pathway for calcium influx during an action potential. We investigated the gating properties of single N-type calcium channels using the cell-attached patch technique. With 100 mM Ba2+ in the pipet, mean N-channel open probability (P o, measured over 100 ms) increased with depolarization, but the range at a single voltage was large (e.g., P o at +40 mV ranged from 0.1 to 0.8). The open dwell time histograms were generally well fit by a single exponential with mean open time (τo) increasing from 0.7 ms at +10 mV to 3.1 ms at +40 mV. Shut time histograms were well fit by two exponentials. The brief shut time component (τsh1 = 0.3 ms) did not vary with the test potential, while the longer shut time component (τsh2) decreased with voltage from 18.9 ms at +10 mV to 2.3 ms at +40 mV. Although N-channel P o during individual sweeps at +40 mV was often high (∼0.8), mean P o was reduced by null sweeps, low P o gating, inactivation, and slow activation. The variability in mean P o across patches resulted from differences in the frequency these different gating processes were expressed by the channels. Runs analysis showed that null sweeps tended to be clustered in most patches, but that inactivating and slowly activating sweeps were generally distributed randomly. Low P o gating (P o = 0.2, τo = 1 ms at +40 mV) could be sustained for ∼1 min in some patches. The clustering of null sweeps and sweeps with low P o gating is consistent with the idea that they result from different modes of N-channel gating. While P o of the main N-channel gating state is high, the net P o is reduced to a maximum value of close to 0.5 by other gating processes.
Performance of low-density parity-check coded FFH/BFSK systems under band multitone jamming

Frequency-hopping spread-spectrum (FH-SS) is an efficient technique to combat jamming. Previous works in jamming channels pay more attention to partial band noise jamming (PBNJ). In this paper, Low-Density Parity-Check (LDPC) coded fast frequency hopping binary frequency shift keying (FFH/BFSK) spread-spectrum (SS) systems are investigated under band multitone noise jamming (BMTJ). We take the advantage of both diversity gain and coding gain for enhancing the system performance. We consider maximum-likelihood (ML) diversity combing and square-law with equal gain combing schemes which are used to compute the codeword bit decision reliability. The sum-product algorithm (SPA) based on belief propagation is exploited for iterative decoding. Simulation results reveal that our proposed system provides excellent performance against band multitone noise jamming.