Miracle-dz/UniFAR-FTUs huggingface dataset:
Simultaneous continual flow pipeline architecture

Since the introduction of the first industrial out-of-order superscalar processors in the 1990s, instruction buffers and cache sizes have kept increasing with every new generation of out-of-order cores. The motivation behind this continuous evolution has been performance of single-thread applications. Performance gains from larger instruction buffers and caches come at the expense of area, power, and complexity. We show that this is not the most energy efficient way to achieve performance. Instead, sizing the instruction buffers to the minimum size necessary for the common case of L1 data cache hits and using new latency-tolerant microarchitecture to handle loads that miss the L1 data cache, improves execution time and energy consumption on SpecCPU 2000 benchmarks by an average of 10% and 12% respectively, compared to a large superscalar baseline. Our non-blocking architecture outperforms other latency tolerant architectures, such as Continual Flow Pipelines, by up to 15% on the same SpecCPU 2000 benchmarks.
Predictors of supra-threshold speech-in-noise intelligibility by hearing-impaired listeners

The aim was to assess the relative importance of cochlear mechanical dysfunction, temporal processing deficits, and age for hearing-impaired listeners to understand supra-threshold speech in noise backgrounds. 68 hearing-aid candidates took part in the study. Intelligibility was assessed for speech-shaped noise (SSN) and reversed two-talker masker (R2TM) backgrounds. Behavioural estimates of cochlear gain loss and residual compression from a previous study were used as indicators of cochlear mechanical dysfunction. Temporal processing abilities were assessed using frequency modulation detection thresholds. Age, audiometric thresholds, and the difference between audiometric thresholds and cochlear gain loss were also included in the analyses. Stepwise multiple linear regression models of intelligibility were designed to assess the relative importance of the various factors for speech intelligibility. Results showed that (1) cochlear gain loss was unrelated to intelligibility; (2) residual cochlear compression was related to intelligibility in SSN but not in R2TM backgrounds; (3) temporal processing was strongly related to intelligibility in R2TM backgrounds and much less so in SSN backgrounds; (4) age per se hindered intelligibility. We conclude that all factors affect speech intelligibility but their relative importance varies across masker backgrounds.
System Level Benchmarking with Yield-Enhanced Standard Cell Library for Carbon Nanotube VLSI Circuits

The quest for technologies with superior device characteristics has showcased Carbon-Nanotube Field-Effect Transistors (CNFET) into limelight. In this work we present physical design techniques to improve the yield of CNFET circuits in the presence of Carbon Nanotube (CNT) imperfections. Various layout schemes are studied for enhancing the yield of CNFET standard cell library. With the help of existing ASIC design flow, we perform system-level benchmarking of CNFET circuits and compare them to CMOS circuits at various technology nodes. With CNFET technology, we observe maximum performance gains for circuits with gate-dominated delays. Averaged across various benchmarks at 16 nm, we report 8× improvement in Energy-Delay-Product (EDP) with CNFET circuits when compared to CMOS counterpart. We also study the performance of a complete OpenRISC processor, where we see 1.5× improvement in EDP over CMOS at 16 nm technology node. Voltage scaling enabled by CNFETs can be explored in the future for further performance benefits.
OPTIMALITY OF PAYOFFS IN LÉVY MODELS

In this paper, we determine the lowest cost strategy for a given payoff in Levy markets where the pricing is based on the Esscher martingale measure. In particular, we consider Levy models where prices are driven by a normal inverse Gaussian (NIG)- or a variance Gamma (VG)-process. Explicit solutions for cost-efficient strategies are derived for a variety of vanilla options, spreads, and forwards. Applications to real financial market data show that the cost savings associated with these strategies can be quite substantial. The empirical findings are supplemented by a result that relates the magnitude of these savings to the strength of the market trend. Moreover, we consider the problem of hedging efficient claims, derive explicit formulas for the deltas of efficient calls and puts and apply the results to German stock market data. Using the time-varying payoff profile of efficient options, we further develop alternative delta hedging strategies for vanilla calls and puts. We find that the latter can provide a more accurate way of replicating the final payoff compared to their classical counterparts.
Dobutamine “Stress” Test and Latent Cardiac Susceptibility to Inhaled Diesel Exhaust in Normal and Hypertensive Rats

Background: Exercise “stress” testing is a screening tool used to determine the amount of stress for which the heart can compensate before developing abnormal rhythm or ischemia, particularly in susceptible persons. Although this approach has been used to assess risk in humans exposed to air pollution, it has never been applied to rodent studies. Objective: We hypothesized that a single exposure to diesel exhaust (DE) would increase the risk of adverse cardiac events such as arrhythmia and myocardial ischemia in rats undergoing a dobutamine challenge test, which can be used to mimic exercise-like stress. Methods: Wistar-Kyoto normotensive (WKY) and spontaneously hypertensive (SH) rats implanted with radiotelemeters and a chronic intravenous catheter were whole-body exposed to 150 μg/m3 DE for 4 hr. Increasing doses of dobutamine, a β1-adrenergic agonist, were administered to conscious unrestrained rats 24 hr later to elicit the cardiac response observed during exercise while heart rate (HR) and electrocardiogram (ECG) were monitored. Results: A single exposure to DE potentiated the HR response of WKY and SH rats during dobutamine challenge and prevented HR recovery at rest. During peak challenge, DE-exposed SH rats had lower overall HR variability when compared with controls, in addition to transient ST depression. All DE-exposed animals also had increased arrhythmias. Conclusions: These results are the first evidence that rats exhibit stress-induced cardiac dysrhythmia and ischemia sensitivity comparable to humans after a single exposure to a toxic air pollutant, particularly when in the presence of underlying cardiovascular disease. Thus, exposure to low concentrations of air pollution can impair the heart’s ability to respond to stress and increase the risk of subsequent triggered dysfunction.
WIDEBAND BANDPASS FILTERS USING PARALLEL-COUPLED SIRS WITH WIDE SPURIOUS SUPPRRESSION

This paper proposes parallel-coupled stepped impedance resonators (SIRs) used for wideband bandpass fllters with reduced size and improved spurious response suppression in out-of-band. The fllter design concept is demonstrated by both two- and four-parallel coupled line resonator structures with the same fundamental frequency (f0) of 6.5GHz. The flrst bandpass fllter has been designed using two resonators with ‚/2 slot and embedded slot feeds for spurious response suppression at 2f0 and 3f0, respectively. The measured insertion loss is around 0.3dB and the operation band is from 4.4 to 9.2GHz (FBW = 73.8%). For the second bandpass fllter, the embedded slot feeds and additional loaded open-stubs have been utilized to improve the wide spurious response suppression and sharp skirt characteristic in the passband. The measured passband of frequency response is from 4.2 to 8.6GHz (FBW = 67.7%). The insertion loss of passband is lesser than 0.25dB and the return loss is better than 15dB. The fllter designs are described in details. The simulated and experimental results are demonstrated and discussed.
A novel method for vision tracking with line of sight control, using a fuzzy logic controller and Euler angle orientation in the feedforward loop

This paper presents a novel method for vision tracking with line of sight control. The line of sight control for the vision tracking is an important function for various robot applications. However, when a robot is moving fast, it is difficult to keep the camera's sight towards a specific target, using only a vision feedback controller, due to the limitation in vision processing capabilities. In the proposed method, a feedforward controller based on robot position information is implemented to improve the tracking performance. The translational motion of the robot is estimated using a fuzzy logic controller and a slip detector. In addition, an Euler angle expression is used to calculate the robot orientation in 3-D space. Then, a vision-based feedback controller is added to compensate for the feedforward targeting error. To evaluate the performances, the developed vision tracking system is implemented on a mobile robot, and various experiments are performed. Experimental results show excellent tracking and recognition performances.
Efficient Multi-depth Querying on Provenance of Relational Queries Using Graph Database

Data Provenance is the history associated with that data. It constitutes the origin, creation, processing, and archiving of data. In today's Internet era, it has gained significant importance for database analytics. Most of the provenance models store provenance information in relational databases for further querying and analysis. Although, querying of provenance in Relational Databases is very efficient for small data sets, it becomes inefficient as the provenance data grows and traversal depth of provenance query increases. This is mainly due to increase in number of join operations to search the entire provenance data. Graph Databases provide an alternative to RDBMSs for storing and analyzing provenance data as it can scale to billions of nodes and at the same time traverse thousands of relationships efficiently. In this paper, we propose efficient multi-depth querying of provenance data using graph databases. The proposed solution allows efficient querying of provenance of current as well as historical queries. A comparison between relational and graph databases is presented for varying provenance data size and traversal depths. Graph databases are found to scale well with increasing depth of provenance queries, whereas in relational databases the querying time increases exponentially.
Structured Signal Recovery From Quadratic Measurements: Breaking Sample Complexity Barriers via Nonconvex Optimization

This paper concerns the problem of recovering an unknown but structured signal <inline-formula> <tex-math notation="LaTeX">${x}\in \mathbb {R} ^{n}$ </tex-math></inline-formula> from <inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula>-quadratic measurements of the form <inline-formula> <tex-math notation="LaTeX">${y}_{r}= \left |{\langle {a}_{r}, {x}\rangle }\right |^{{2}}$ </tex-math></inline-formula> for <inline-formula> <tex-math notation="LaTeX">${r}={1},{2},\ldots, {m}$ </tex-math></inline-formula>. We focus on the under-determined setting where the number of measurements is significantly smaller than the dimension of the signal (<inline-formula> <tex-math notation="LaTeX">${m} < < {n}$ </tex-math></inline-formula>). We formulate the recovery problem as a nonconvex optimization problem where prior structural information about the signal is enforced through constrains on the optimization variables. We prove the projected gradient descent, when initialized in a neighborhood of the desired signal, converges to the unknown signal at a linear rate. These results hold for <italic>any</italic> closed constraint set (convex or non-convex) providing convergence guarantees to the global optimum even when the objective function and constraint set are nonconvex. Furthermore, these results hold with a number of measurements that are only a constant factor away from the minimal number of measurements required to uniquely identify the unknown signal. Our results provide the first provably tractable algorithm for this data-poor regime, breaking local sample complexity barriers that have emerged in this paper. In this paper, we utilize and further develop powerful tools for uniform convergence of empirical processes that may have broader implications for rigorous understanding of constrained nonconvex optimization heuristics.
Characteristic improvement of inkjet printed Ag interconnects using tape on-off and mirror-reaction processes

In the paper, we develop and implement the processes of tape on-off and Ag mirror-reaction to the previously developed size scalable inkjet printing process technique, i.e. CPLoP (Combined Process of Lift-off and Printing) for continuing its future interconnect applications in flexible microelectronics. The newly developed processes not only reduce coffee ring effect but also improve better electrical conductivity of the printed Ag interconnects at low process temperature. A 10μm wide Ag interconnect with flat surface morphology and low electrical resistivity, i.e. 7.7 μΩ · cm, has been realized.
A survey of technical requirements and consumer application standards for IP-based smart grid AMI network

The smart grid is a new advanced electrical network promoted by the government to address environmental sustainability, energy generation, distribution and consumption efficiency issues. As one of the enabling technologies, Advanced Metering Infrastructure refers to systems which measure, collect and analyze energy usage, and interact with advanced devices such as smart meters through various communication media either on request or on pre-defined schedules. This paper focuses on proposing the technical requirements imposed on the communications network for AMI. Then we examine each of the proposed AMI consumer application standards found in open literature based on these requirements. We will discuss the system engineering approach taken by NIST/EPRI to develop standards for smart grid and AMI and highlight outstanding security and interoperability issues concerned for deploying smart grid AMI. We compare the performances of consumer application standards in addressing these open issues. Finally, we conclude and propose our future work.
A new SIFT keypoint descriptor for copy detection

In this paper, we propose a novel keypoint descriptor coined Spatial Coherent Feature (SCF) based on Scale Invariant Features Transform (SIFT) and the spatial coherent information. Like SIFT, our descriptors encode the salient aspects of image gradient in the keypoint's neighborhood. However, instead of only using 4*4 sample regions to computer orientation histogram, we get descriptor based on eight consecutive neighborhood regions, and each region has different size but approximate numbers of pixel. That is to say, the new descriptor is computed on spatial consecutive regions, and it includes neighborhood information around keypoint. The performance of SCF descriptor is tested for copy detection. Experimental results demonstrated that the novel keypoint descriptor can be robust for some kinds of attacks such as scale, rotation and reduce the error matching because of introducing spatial coherency, compared to SIFT descriptor.
Institutional and communicative processes constituting specific technology paths and environmental perceptions

Building on knowledge management discussions, the paper discusses how environmental knowledge are created and communicated in enterprises and sectors, taking communication on environmental properties of chemical in industrial production as a case. Knowledge is conceived as situated enacted practice making the task of environmental communication a problem of transfer and translation of knowledge between different practice communities and knowledge systems, each having specific bounded cognition. Taking this theoretical approach, the paper, as an example, analyses specific problems related to the development and implementation of a science based information system to assess and document environmental properties of chemicals (The “KEMI-centre project”). The paper discusses how knowledge systems and practice communities condition environmental communication on chemicals and environmental performance within and between enterprises/organisations.
A 995Mpixels/s 0.2nJ/pixel fractional motion estimation architecture in HEVC for Ultra-HD

This paper presents a fractional motion estimation (FME) design in high efficiency video coding (HEVC) for ultrahigh definition video (Ultra-HD). To reduce complexity and achieve high throughput, the design is co-optimized in algorithm and hardware architecture. Bilinear quarter pixel approximation, together with a 5T12S search pattern is proposed to reduce the complexity of the interpolation and search process. Furthermore, we introduce an exhaustive size-hadamard transform (ES-HAD), to improve coding quality, and determine the best transform size rather than using complex transform coding. Besides, a data reuse method of ES-HAD is applied to reduce the hardware overhead. This design is implemented in 65nm CMOS chip and verified by FPGA based evaluation system. It achieves 995Mpixels/s for 7680×4320 30fps encoding, at least 4.7 times faster than previous designs. Its power dissipation is 198.6mW at 188MHz, with 0.2nJ/pixel power efficiency. Despite high complexity in HEVC, the chip achieves 56% improvement on power efficiency than previous works in H.264.
Fault-tolerant Distributed Systems in Hardware

Very large-scale integrated (VLSI) hardware designs can be seen as distributed systems at several levels of abstraction: from the cores in a multicore architecture down to the Boolean gates in its circuit implementation, hardware designs comprise of interacting computing nodes with non-negligible communication delays. The resulting similarities to classic large-scale distributed systems become even more accented in mission critical hardware designs that are required to operate correctly in the presence of component failures. We advocate to act on this observation and treat fault-tolerant hardware design as the task of devising suitable distributed algorithms. By means of problems related to clock generation and distribution, we show that (i) design and analysis techniques from distributed computing can provide new and provably correct mission critical hardware solutions and (ii) studying such systems reveals many interesting and challenging open problems for distributed computing.
MISTO: A Multi-Resolution Deformable Model for Segmentation of Soft-Tissue Organs

We propose a multi-resolution integrated model for the segmentation of soft-tissue organs called MISTO. The model is constructed hierarchically to represent the most significant deformations from the training set as well as to generate representative deformation modes of the organ shapes. The clutter surrounding of the surface points are formulated in terms of an external functional which is also learnt automatically from the training samples. By combining a set of powerful shape models and context constraints, the segmentation process can be carried out very effectively. To avoid the local minimum during model optimization, the deformation strategies are designed such that the portions of the surface for which we have more reliable prior knowledge on their possible deformations are deformed first, followed by deformation on the less informed portions. The experimental and validation results verify that our proposed approaches can be robustly applied to highly deformable anatomies such as soft-tissue organs.
Performance Measurement of Decision Tree Excluding Insignificant Leaf Nodes

Too much information exist in ubiquitous environment, and therefore it is not easy to obtain the appropriately classified information from the available data set. Decision tree algorithm is useful in the field of data mining or machine learning system, as it is fast and deduces good result on the problem of classification. Sometimes, however, a decision tree may have leaf nodes which consist of only a few or noise data. The decisions made by those weak leaves will not be effective and therefore should be excluded in the decision process. This paper proposes a method using a classifier, UChoo, for solving a classification problem, and suggests an effective method of decision process involving only the important leaves and thereby excluding the noisy leaves. The experiment shows that this method is effective and reduces the erroneous decisions and can be applied when only important decisions should be made.
Stellar Kinematics for the Central Spheroid in the Polar Disk Galaxy NGC 4650A

We have obtained high angular resolution, high signal-to-noise ratio spectra of the calcium triplet absorption lines on the photometric axes of the stellar spheroid in the polar disk galaxy NGC 4650A. Along the major axis, the observed rotation and velocity dispersion measurements show the presence of a kinematically decoupled nucleus and a flat velocity dispersion profile. The minor-axis kinematics is determined for the first time: along this direction some rotation is measured, and the velocity dispersion is nearly constant and slightly increases at larger distances from the center. The new high-resolution kinematic data suggest that the stellar component in NGC 4650A resembles a nearly exponential oblate spheroid supported by rotation. The main implications of these results on the previous mass models for NGC 4650A are discussed. Moreover, the new kinematic data set constraints on current models for the formation scenarios of polar ring galaxies, supporting a slow accretion rather than a secondary strong dissipative event.
CloudCmp: Shopping for a Cloud Made Easy

Cloud computing has gained much popularity recently, and many companies now offer a variety of public cloud computing services, such as Google Ap-pEngine, Amazon AWS, and Microsoft Azure. These services differ in service models and pricing schemes, making it challenging for customers to choose the best suited cloud provider for their applications. This paper proposes a framework called CloudCmp to help a customer select a cloud provider. We outline the design of CloudCmp and highlight the main technical challenges. CloudCmp includes a set of benchmarking tools that compare the common services offered by cloud providers, and uses the benchmarking results to predict the performance and costs of a customer's application when deployed on a cloud provider. We present preliminary benchmarking results on three representative cloud providers. These results show that the performance and costs of various cloud providers differ significantly, suggesting that CloudCmp, if implemented, will have practical relevance.