Miracle-dz/UniFAR-FTUs huggingface dataset:
WAP: The Warp Feature Aware Prefetching Method for LLC on CPU-GPU Heterogeneous Architecture

Recently, researchers discovered a GPU has some advantages for non-graphic computing. CPU-GPU heterogeneous architecture combines CPU and GPU to a chip and makes GPU easier to run non-graphic programs. Researchers also proposed LLC(last-level cache) to store and exchange data between CPU and GPU. We discover the LLC hit rate has great influence on memory access performance and system's performance. Therefore, we propose the WAP(warp feature aware prefetching method) for improving the LLC hit rate and memory access performance. We combine GPGPU-sim and GEM5 to a CPU-GPU heterogeneous many-core simulator, add an LLC in this simulator and choose 10 representative benchmarks. We compare this method with the MAP method. The experimental result illustrates the WAP improves 11.8% than the MAP on the LLC hit rate and 11.39% on IPC.
Engineering synthetic feedback to promote recovery of self-feeding skills in people with sensory deficits due to stroke

ENGINEERING SYNTHETIC FEEDBACK TO PROMOTE RECOVERY OF SELF-FEEDING SKILLS IN PEOPLE WITH SENSORY DEFICITS DUE TO STROKE Alexis Krueger Marquette University, 2016 Kinesthesia refers to sensations of limb position and movement, and deficits of upper limb kinesthetic feedback are common after stroke, impairing stroke survivors’ ability to perform the fundamental reaching and stabilization behaviors needed for daily functions like self-feeding. I attempt to mitigate the negative impact of post-stroke kinesthesia deficits by evaluating the utility of vibrotactile sensory substitution to restore closed-loop kinesthetic feedback of the upper limb. As a first step, this study evaluated performance in healthy individuals during fundamental reaching, stabilization, and tracking behaviors while using supplemental vibrotactile feedback encoding either limb state information or goal-aware error information. First, I determined that performance in reaching and stabilization tasks varies systematically with the amount of limb position and velocity information encoded in limb state feedback and that there is an optimal combination. Next, I compared the utility of optimal limb state to goalaware error feedback. Both types of feedback reduced error in the reaching and stabilization tasks. Random task-irrelevant sham feedback did not reduce error, demonstrating participants could perceive and understand the information contained within the vibrotactile feedback. Error feedback improved performance more than state feedback; however the relative difficulty of using error feedback outside of a laboratory setting means state feedback should not be discounted. The performance while tracking could not be quantified due to issues with the task design. As a second step, I performed a series of case studies in five chronic stroke survivors. The stroke survivors all tolerated the vibrotactile feedback well and were able to perceive and understand at least one of the limb state or error feedback encodings. Stroke survivors practiced each information encoding type for one session. During this short period our stroke survivors struggled to integrate visual and vibrotactile inputs and motor control in order to use the vibrotactile information to control the arm. However, two additional practice sessions with error feedback for one participant led to a two thirds reduction in reaching error. These results suggest stroke survivors can learn to use supplemental vibrotactile feedback to enhance control of the contralesional arm.
Using x-grams for speech-to-speech translation

In this paper, a statistical speech-to-speech translation system, developed at TALP during the last months, is presented. By adapting well-known speech recognition techniques to the specific translation setting, th e system is able to integrate speech sign al into a fin ite state tran sdu cer th at tran slates statistically domain-constrained Span ish sen tences into En glish on es.
Multi-frequency imaging of the galaxy cluster Abell 2163 using the Sunyaev-Zel'dovich effect

Context. Observations of the Sunyaev-Zel'dovich effect (SZE) from galaxy clusters are emerging as a powerful tool in cosmology. Besides large cluster surveys, resolved SZE images of individual clusters can shed light on the physics of the intra-cluster medium (ICM) and allow accurate measurements of the cluster gas and total masses. Aims. We used the APEX-SZ and LABOCA bolometer cameras on the APEX telescope to map both the decrement of the SZE at 150 GHz and the increment at 345 GHz toward the rich and X-ray luminous galaxy cluster Abell 2163 at redshift 0.203. The SZE images were used, in conjunction with archival XMM-Newton X-ray data, to model the radial density and temperature distribution of the ICM, as well as to derive the gas mass fraction in the cluster under the assumption of hydrostatic equilibrium. Methods. We describe the data analysis techniques developed to extract the faint and extended SZE signal. We used the isothermal model to fit the SZE decrement/increment radial profiles. We performed a simple, non-parametric de-projection of the radial density and temperature profiles, in conjunction with X-ray data, under the simplifying assumption of spherical symmetry. We combined the peak SZE signals derived in this paper with published SZE measurements of this cluster to derive the cluster line-of-sight bulk velocity and the central Comptonization, using priors on the ICM temperature. Results. We find that the best-fit isothermal model to the SZE data is consistent with the ICM properties implied by the X-ray data, particularly inside the central 1 Mpc radius. Inside a radius of ~1500 kpc from the cluster center, the mean gas temperature derived from our SZE/X-ray joint analysis is 10.4 1.4 keV. The error budget for the derived temperature profile is dominated by statistical errors in the 150 GHz SZE image. From the isothermal analysis combined with previously published data, we find a line-of-sight peculiar velocity consistent with zero; vr = -140 460 km s-1, and a central Comptonization y0 = 3.42 0.32 10-4 for Abell 2163. Conclusions. Although the assumptions of hydrostatic equilibrium and spherical symmetry may not be optimal for this complex system, the results obtained under these assumptions are consistent with X-ray and weak-lensing measurements. This shows the applicability of the simple joint SZE and X-ray de-projection technique described in this paper for clusters with a wide range of dynamical states.
Visualization based API usage patterns refining

Learning to use existing or new software libraries is a difficult task for software developers, which would impede their productivity. Most of existing work provided different techniques to mine API usage patterns from client programs, in order to help developers to understand and use existing libraries. However, considering only client programs to identify API usage patterns, is a strong constraint as collecting several similar client programs for an API is not a trivial task. And even if these clients are available, all the usage scenarios of the API of interest may not be covered by those clients. In this paper, we propose a visualization based approach for the refinement of Client-based Usage Patterns. We first visualize the patterns structure. Then we enrich the patterns with API methods that are semantically related to them, and thus may contribute together to the implementation of a particular functionality for potential client programs.
The Role of Data Mining for Business Intelligence in Knowledge Management

This chapter introduces the role of Data Mining (DM) for Business Intelligence (BI) in Knowledge Management (KM), thus explaining the concept of KM, BI, and DM; the relationships among KM, BI, and DM; the practical applications of KM, BI, and DM; and the emerging trends toward practical results in KM, BI, and DM. In order to solve existing BI problems, this chapter also describes practical applications of KM, BI, and DM (in the fields of marketing, business, manufacturing, and human resources) and the emerging trends in KM, BI, and DM (in terms of larger databases, high dimensionality, over-fitting, evaluation of statistical significance, change of data and knowledge, missing data, relationships among DM fields, understandability of patterns, integration of other DM systems, and users’ knowledge and interaction). Applying DM for BI in the KM environments will enhance organizational performance and achieve business goals in the digital age.
A Large-Scale Survey of NGC 1333

We observed the clustered star forming complex NGC 1333 with the BIMA and FCRAO telescopes in the transitions HCO+(1-0) and N2H+(1-0) over an 11′ × 11′ area with resolution ~10′′ (0.015 pc). The N2H+ emission follows very closely the submillimeter dust continuum emission, while HCO+ emission appears more spatially extended and also traces outflows. We have identified 93 N2H+ cores using the CLUMPFIND algorithm, and we derive N2H+ core masses between 0.05 and 2.5 M☉, with uncertainties of a factor of a few, dominated by the adopted N2H+ abundance. From a comparison with virial masses, we argue that most of these N2H+ cores are likely to be bound, even at the lowest masses, suggesting that the cores do not trace transient structures, and implies the entire mass distribution consists of objects that can potentially form stars. We find that the mass distribution of N2H+ cores resembles the field star IMF, which suggests that the IMF is locked in at the prestellar stage of evolution. We find that the N2H+ cores associated with stars identified from Spitzer infrared images have a flat mass distribution. This might be because lower mass cores lose a larger fraction of their mass when forming a star. Even in this clustered environment, we find no evidence for ballistic motions of the cores relative to their lower density surroundings traced by isotopic CO emission, although this conclusion must remain tentative until the surroundings are observed at the same high resolution as the N2H+.
Bodies and affect in non-traditional learning spaces

Abstract This paper considers the ‘knowledge economy’ as it is used in education rhetoric to establish social and educational consent for significant changes both to the spatial organisation of classrooms and their affective economies. We draw on ethnographic data from a study of ‘non-traditional classroom spaces’, where the spatial organisation of schooling emerged as a potential fulcrum through which the imaginary of the conventional primary classroom was being reconceptualised. Traditionally configured classroom spaces and the learning that takes place within them were being challenged and replaced by notions of twenty-first century learning in ‘agile’ learning environments. In the context of this reform agenda, these open-plan spaces were seen as offering new prospects for participation in a globally connected and competitive economic world that requires students to continuously adapt, innovate and respond creatively to a range of different problems. We consider how these everyday moments function as conceptual encounters between affective, embodied experiences and educational reform discourses that rationalise the implementation of non-traditional classroom spaces in ways that have very little to do with children and their futures. This cultural approach takes a step aside from numerous, and necessary, critiques of recent educational policies per se, in order to consider what might be learned from the uncanny spectres of child bodies that haunt them. The paper draws attention to examples of children’s affect in non-traditional classrooms and what that may tell us about current educational reform when sacrifice forms part of the missing account of educational reorganisation for the knowledge economy.
Spotting a Query Phrase from Polyphonic Music Audio Signals Based on Semi-supervised Nonnegative Matrix Factorization

This paper proposes a query-by-audio system that aims to detect temporal locations where a musical phrase given as a query is played in musical pieces. The “phrase” in this paper means a short audio excerpt that is not limited to a main melody (singing part) and is usually played by a single musical instrument. A main problem of this task is that the query is often buried in mixture signals consisting of various instruments. To solve this problem, we propose a method that can appropriately calculate the distance between a query and partial components of a musical piece. More specifically, gamma process nonnegative matrix factorization (GaP-NMF) is used for decomposing the spectrogram of the query into an appropriate number of basis spectra and their activation patterns. Semi-supervised GaP-NMF is then used for estimating activation patterns of the learned basis spectra in the musical piece by presuming the piece to partially consist of those spectra. This enables distance calculation based on activation patterns. The experimental results showed that our method outperformed conventional matching methods.
Classification method for prediction of human activity using stereo camera

Type any human activity can be tracking to recognize the type of activities. Human motion recognition is a huge discussion on gesture recognition. one to recognize human activity is the activity recognition. There are three basic positions to perform various activities, standing, sit down and lie down. These three basic positions can indicate whether a man in a room doing an activity or not. Kinect camera can recognize gestures of the human body, and it can classify parts of the human body into several points. This study was used the three machine learning with the classification method, SVM, MLP and Naive Bayes. SVM and MLP excellent in activity recognition. This study uses 10 body skeleton joint obtained from Kinect camera. SVM and MLP showed a good performance, respectively 99,23% and 98,8%. Naive Bayes quite well with 73,43% Naive Bayes about speed ahead with 0,02 seconds followed by SVM and MLP with 0,52 seconds and 16,75 seconds. Future expected the research applied to Smart home and Smart care by adding a type of activity.
Telephone Channel Compensation in Speaker Verification Using a Polynomial Approximation in the Log-Filter-Bank Energy Domain

This correspondence presents a novel feature-space channel compensation technique that models the convolutional distortion in the log-energy mel-filter domain by means of a polynomial approximation. The proposed parametric distortion model generates appropriate constraints in the spectral domain that help to improve the channel cancelling estimation with limited data. In a text-dependent speaker verification task, the polynomial-based channel estimation scheme can lead to reductions in equal error rate (EER) as great as 22% and 8% when compared with the baseline system and with the standard cepstral bias removal approach, respectively, with no significant increase in computational load.
Minimizing Memory Access By Improving Register Usage Through High-level Transformations

Multimedia signal processing software typically have to process large amounts of data. The algorithms often involve the handling of data arrays in the form of nested loops. Experiments show that for this kind of applications data transfer (memory access) operations consume much more power than data-path operations. Our objective is to reduce memory access related power consumption by reducing the number of data transfers between processor and memory, or between a higher (closer to processor) level of memory and a memory at a lower level using source program transformation. In this paper, we propose a source-to-source transformation method to improve register usage for multi-dimensional arrays in nested loops. Significant improvement is demonstrated through some benchmark programs.
Compiling Quantum Programs Using Genetic Algorithms

On many of these technologies, two-qubit gates (or, if you prefer, two-operand instructions) can only have neighboring qubits as operands. When two operands that are not next to each are scheduled to be arguments to an instruction, they must be brought together by swapping qubit values (or variables) with their neighbors until the arguments are next to each other, and the algorithmically specified gate can be performed. The swap instructions are pure overhead, to be optimized away whenever possible.
Mannitol metabolism during pathogenic fungal–host interactions under stressed conditions

Numerous plants and fungi produce mannitol, which may serve as an osmolyte or metabolic store; furthermore, mannitol also acts as a powerful quencher of reactive oxygen species (ROS). Some phytopathogenic fungi use mannitol to stifle ROS-mediated plant resistance. Mannitol is essential in pathogenesis to balance cell reinforcements produced by both plants and animals. Mannitol likewise serves as a source of reducing power, managing coenzymes, and controlling cytoplasmic pH by going about as a sink or hotspot for protons. The metabolic pathways for mannitol biosynthesis and catabolism have been characterized in filamentous fungi by direct diminishment of fructose-6-phosphate into mannitol-1-phosphate including a mannitol-1-phosphate phosphatase catalyst. In plants mannitol is integrated from mannose-6-phosphate to mannitol-1-phosphate, which then dephosphorylates to mannitol. The enzyme mannitol dehydrogenase plays a key role in host–pathogen interactions and must be co-localized with pathogen-secreted mannitol to resist the infection.
An improved DV-Hop localization algorithm for wireless sensor networks

Wireless sensor networks have attracted great interest in the last decade, and have provided solutions in a growing number of applications. The location of sensors is a major issue in these networks. Many localization algorithms have been proposed and can be categorized into two families: range-based and range-free algorithms. In this paper, the localization algorithm DV-Hop is improved in order to minimize the estimation error and thus obtain better localization accuracy of sensor nodes. Simulations have been conducted and results are reported to show the efficiency of the enhanced version of DV-Hop.
S3MKL: scalable semi-supervised multiple kernel learning for image data mining

For large scale image data mining, a challenging problem is to design a method that could work efficiently under the situation of little ground-truth annotation and a mass of unlabeled or noisy data. As one of the major solutions, semi-supervised learning (SSL) has been deeply investigated and widely used in image classification, ranking and retrieval. However, most SSL approaches are not able to incorporate multiple information sources. Furthermore, no sample selection is done on unlabeled data, leading to the unpredictable risk brought by uncontrolled unlabeled data and heavy computational burden that is not suitable for learning on real world dataset. In this paper, we propose a scalable semi-supervised multiple kernel learning method (S3MKL) to deal with the first problem. Our method imposes group LASSO regularization on the kernel coefficients to avoid over-fitting and conditional expectation consensus for regularizing the behaviors of different kernel on the unlabeled data. To reduce the risk of using unlabeled data, we also design a hashing system where multiple kernel locality sensitive hashing (MKLSH) are constructed with respect to different kernels to identify a set of "informative" and "compact" unlabeled training subset from a large unlabeled data corpus. Combining S3MKL with MKLSH, the method is suitable for real world image classification and personalized web image re-ranking with very little user interaction. Comprehensive experiments are conducted to test the performance of our method, and the results show that our method provides promising powers for large scale real world image classification and retrieval.
THE ROLE OF DRY MERGERS FOR THE FORMATION AND EVOLUTION OF BRIGHTEST CLUSTER GALAXIES

Using a resimulation technique, we perform high-resolution cosmological simulations of dry mergers in a massive (1015 M☉) galaxy cluster identified in the Millennium Run. Our initial conditions include well resolved compound galaxy models consisting of dark matter halos and stellar bulges that are used to replace the most massive cluster progenitor halos at redshift z = 3, allowing us to follow the subsequent dry merger processes that build up the cluster galaxies in a self-consistent cosmological setting. By construction, our galaxy models obey the stellar mass–size relation initially. Also, we study both galaxy models with adiabatically contracted and uncompressed halos. We demonstrate that the brightest cluster galaxy (BCG) evolves away from the Kormendy relation as defined by the smaller mass galaxies (i.e., the relation bends). This is accompanied by a significantly faster dark matter mass growth within the half-light radius of the BCG compared to the increase in the stellar mass inside the same radius. As a result of the comparatively large number of mergers the BCG experiences, its total mass-to-light ratio becomes significantly higher than in typical elliptical galaxies. We also show that the mixing processes between dark matter and stars lead to a small but numerically robust tilt in the fundamental plane and that the BCG lies on the tilted plane. Our model is consistent with the observed steepening of the logarithmic mass-to-light gradient as a function of the stellar mass. As we have not included effects from gas dynamics or star formation, these trends are exclusively due to N-body and stellar dynamical effects. Surprisingly, we find only tentative weak distortion in the Faber–Jackson relation that depends on the aperture size, unlike expected based on studies of isolated merger simulations. This may be due to differences in the distribution of galaxy orbits, which is given in our approach directly by the cosmological context while it has to be assumed in isolated merger simulations, and the fact that the BCG is located deep in the cluster potential well. Another uncertainty in both approaches lies in the definition of the spatial extent of the BCG.
Simultaneous position and stiffness control for an inflatable soft robot

Soft robot research has led to the development of platforms that should allow for better performance when working in uncertain or dynamic environments. The potential improvement in performance of these platforms ranges from mechanical robustness to high forces, to applying lower incidental contact forces in uncertain situations. However, the promise of these platforms is limited by the difficulty of controlling them. In this paper, we present preliminary results on simultaneously controlling stiffness and position for a pneumatically actuated soft robot. Improving on our prior work, we show that by including the pressure in our soft robot actuation chambers as state variables we can improve our average rise time by up to 137%, settling time by 119%, and overshoot by 853%. In addition to these improvements, we can now control both joint position and stiffness simultaneously. This performance improvement comes from using Model Predictive Control running at 300 Hz with improved dynamic models of the soft robot. High performance control of soft robot joints, such as the joint presented in this paper, will enable a wide range of robot applications that were previously difficult or impossible due to the rigid nature of traditional robot linkages and actuation schemes.