Miracle-dz/UniFAR-FTUs huggingface dataset:
Low Power 4-2 Compressor with Fully Restored Node V oltages

Low power design of CMOS circuits is increasingly becoming important these days due to the vast growth in portable digital equipment. In this regard high-speed multipliers play a dominant role. The majority of multiplier designs today make use of 4-2 compressors to speed up the partial product summation tree. A lot of work has been done in 4-2 compressors for reducing the transistor count in their design. With the same objective we present a novel CMOS 4-2 compressor in this paper which makes use of pass logic design techniques. A new XOR-XNOR combination gate is used to build the circuit while totally eliminating the use of inverters in the design. The relationship between switching activity and the circuit complexity is fully analyzed and illustrated with examples. The overall switching activity of the proposed compressor circuit is found to be much lower when compared to earlier designs. The total power dissipation has also been cut down to a minimum while providing the full output voltage swing at all nodes in the circuit. This permits reliable operation of the circuit when the supply voltage is scaled down. Furthermore, the complete circuit uses only a total of 30 transistors. Key-Words: multiplier, XOR-XNOR gate, compressor, switching activity
A Software-Implemented Fault-Tolerance Approach for Control and Display Systems in Avionics

Engineering interactive systems for safety critical applications such as in avionic digital cockpits (and more generally Graphical User interfaces) is a challenge from a dependability viewpoint. The dependability of the user interface and its related hardware and software components must be consistent with the criticality of the functions to be controlled and their required DAL levels. This paper proposes a stepwise refinement approach going from systematic identification of failure modes of these systems to their detection via formally defined assertions. The last steps of the approach present how the assertions can be included into the monitoring part of self-checking interactive components and how they can be deployed on an architecture compliant with the ARINC 653 specification, ensuring temporal and spatial segregation, thus detecting errors and preventing failures due to both physical and transient software faults. We present how these contributions have been applied to the Flight Control Unit Backup interactive application which is available in A380 interactive cockpits.
Prognostic value of magnetic resonance imaging combined with electromyography in the surgical management of cervical spondylotic myelopathy

The present study aimed to evaluate the value of pre-operative magnetic resonance imaging (MRI) combined with electromyography (EMG) for predicting clinical outcome following surgical management of cervical spondylotic myelopathy (CSM). A total of 94 patients with cervical compressive myelopathy were prospectively enrolled and treated with anterior, posterior and posterior-anterior united decompression between October 2007 and February 2009. Prior to surgery 1.5-T MRI and EMG were performed in all patients. The patients were classified into four types based on the presence (+) or absence (−) of an increased signal intensity (ISI) on the T2-weighted magnetic resonance (MR) images and also based on the positive (+)/negative (−) results of the EMG. The four types were as follows: Type I, MRI/EMG (−/−); Type II, MRI/EMG (+/−); Type III, MRI/EMG (−/+); and Type IV, MRI/EMG (+/+). The clinical outcome was also graded according to a modified Japanese Orthopedic Association (JOA) scoring system. Furthermore, pre- and post-operative clinical data were statistically analyzed to explore the correlation between the factors. There were 36 cases (38%) of Type I, 16 (17%) of Type II, 13 (14%) of Type III and 29 (31%) of Type IV. According to the analysis of the clinical data between the four types, there were significant differences in the disability classifications, pre-operative JOA scores and disease duration (P<0.05), but there were no significant differences in gender, age or cord compression ratios (P>0.05). Until the final follow-up, there was a significant difference in the recovery ratio between the four study groups (Hc=27.46, P<0.05). Further comparison showed that the surgical outcome was best in Type I patients and worst in Type IV patients. In conclusion, there was a distinct correlation between classification and the rate of clinical improvement. Patients who had a negative EMG and those without an ISI on T2-weight images tended to suffer only mild symptoms, a short disease duration and, most significantly, experience a good surgical outcome.
Using machine learning techniques for traffic classification and preliminary surveying of an attacker ’ s profile

The increasing complexity of systems brings up new attack vectors and it is therefore easier to compromise systems. A defender of a system is often forced to quickly assess the situation and develop an appropriate defense strategy. The most common way to protect their own networks are Intrusion Detection Systems (IDS). IDS detect attacks either using predefined, static signatures, or based on the behavior of users. Existing systems are inflexible due to static and often stale signatures and thus can be easily bypassed by attackers. This paper on the one hand presents the theoretical concepts of detection of attackers and extends existing attack mitigation approaches by machine learning mechanisms, which can be used during security exercise/challenges like the UCSB International Capture The Flag (iCTF). By improving and combining static signatures with machine learning approaches, a new technique of attack detection called “Classification Voting” was developed, which reduces the number of false positive alerts in an production environment. Our approach allows the generation of signatures without dedicated domain knowledge by guided manual classification of detected network packets. Based on machine learning, new packets can be further classified using the generated model. At the same time the existing models of other signatures can be improved by adding new packets.
Global Methods for Cross-lingual Semantic Role and Predicate Labelling

We address the problem of transferring semantic annotations to new languages using parallel corpora. Previous work has transferred these annotations on a token-to-token basis, an approach that is sensitive to alignment errors and translation shifts. We present a global approach to transfer that aggregates information across the whole parallel corpus and leads to more robust labellers. We build two global models, one for predicate labelling and one for role labelling, each tailored to the task at hand. We show that the combination of direct and global methods outperforms previous results.
The Rise in Household Saving

This article investigates household saving behaviour in Australia and the drivers behind the sharp rise in saving that occurred in the late 2000s after an extended period of decline. Saving behaviour is important as, among other things, it influences household consumption, which accounts for a little over half of GDP. The rise in household saving appears to have been underpinned by precautionary motives, a reduction in expected future income gains for some types of households and an effort to rebuild wealth after the global financial crisis. Also, the long transition to higher levels of indebtedness may have run its course over this period, including perhaps because of a change in attitudes to debt. The ageing of the population does not appear to have played a significant role in recent changes in the saving ratio, although it may place downward pressure on saving over the years ahead.
Financial Conservatism: Evidence on Capital Structure from Low Leverage Firms

A persistent and puzzling empirical regularity is the fact that many firms adopt conservative financial policies. These "under-leveraged" firms carry substantially less debt than predicted by dominant theories of capital structure (Graham (2000) and Myers (1984)). This paper examines the phenomenon of financial conservatism by studying firms that adopt a persistent policy of low leverage. Our major findings are as follows. 1) Conservative firms follow a pecking order style financial policy. A high flow of funds and substantial cash balances allow them to fund the bulk of discretionary expenditures internally. 2) Financial conservatism is largely transitory. Seventy percent of low leverage firms drop their conservative financial policy; almost 50% do so within five years. 3) Conservative firms stockpile financial slack or debt capacity. Their "stockpiles" are utilized later to finance discretionary expenditures, particularly acquisitions and capital expenditures. 4) Financial conservatism is not an industry-based phenomenon. Conservative firms do, however, have relatively high market-to-book and operate relatively frequently in industries thought to be sensitive to financial distress. 5) Conservative firms do not have low tax rates, high non-debt tax shields or face severe information asymmetries.
AN EXTENDED AND MULTIFEATURES BACKGROUND SUBTRACTION WITH SVM

We propose an improved version of the Extended SVM that utilizes the computational power of Graphics Processing Units (GPUs) to achieve real-time performance. The frame rate is greater than 50 frames per second (fps) for most of the tests, even on HD video formats. Although trivial background subtraction (BGS) algorithms (e.g. frame differencing, running average.) can perform quite fast, they are not robust enough to be used in various computer vision problems. Some complex algorithms usually give better results, but are too slow to be applied to real-time systems. Our approach is much faster than existing methods on typical video formats.
Parameter estimation for synthetic TEC surfaces by using Particle Swarm Optimization

In this study, parameter estimation is made for global ionospheric Total Electron Content (TEC) on both noiseless and noisy synthetic surfaces by using modified Particle Swarm Optimization (PSO). In addition, the improvements made in the PSO algorithm to obtain better results are presented. Trend functions that best regionally and globally represent the quiet and distorted ionosphere are given. For noisy trend surfaces, additive white Gaussian noise is added on trend surfaces according to latitude. International GPS System stations (IGS) are used for regional sampling whereas TNPGN-Active stations are used for both regional and global sampling. A brief discussion of PSO and its improvements for modified PSO is provided. Performance and error criterias are determined for the results of noisy and noiseless dual-core Gaussian trend surfaces.
HYPERBOLIC GROUPS AND FREE CONSTRUCTIONS

We investigate which free constructions (amalgamated products and HNN-extensions) over word hyperbolic groups produce groups that are again word hyperbolic. A complete answer is obtained for the case when the amalgamated subgroups are virtually cyclic. The results are applied, in particular, to show that a ${\bf Q}$-completion of a torsion-free hyperbolic group has solvable word problem and conjugacy problem.
Chapter 4 Grasp Control 4.1 Background

Creating force domain behavior requires control processes that optimize manip-ulator contact configuration based on the forces that can be applied. In the case of grasping, contact configuration must be optimized for grasp quality measures. If robust controllers can be defined that converge to good grasp configurations, even over limited domains of attraction, then these controllers can be sequenced or combined to create robust behavior over larger domains. This chapter focuses on defining controllers that control grasp quality. Starting with Coelho's force and moment residual control primitives, a composite grasp controller is defined that executes both of these control primitives concurrently [60]. Next, a hybrid position and force controller is defined that acts in concert with the grasp controller to slide the contacts to good grasp configurations [61]. Finally, the set of potential grasp controllers is expanded by allowing controllers to be parameterized by virtual contacts that correspond to contact groups [57]. Experimental results are presented that demonstrate these controllers to be a practical and effective way of synthesizing grasps in poorly modeled domains. The control-based approach to solving force-domain problems taken in this thesis rests heavily upon Coelho's force and moment residual controllers. These controllers minimize grasp error functions by displacing contacts on the surface of an object in response to local tactile feedback at the contacts. This section describes different approaches to tactile sensing and a method for displacing contacts on the surface of an object that acquires tactile feedback, called probing. Next, Coelho's force residual and moment residual controllers that displace contacts into quality grasp configurations based on local tactile feedback are described. Grasp controllers assume that it is possible to sense local surface geometry in the neighborhood of each contact. This may be accomplished in a number of ways. For example, it may be possible to use computer vision to extract the relationship between object surface and grasp contact [1]. Notice that this vision problem is significantly easier than the general problem of reconstructing full object geometry. Unfortunately, it can be difficult to use vision for the purpose of " tactile sensing " without placing 34
Comprehensive prediction of novel microRNA targets in Arabidopsis thaliana

MicroRNAs (miRNAs) are 20–24 nt long endogenous non-coding RNAs that act as post-transcriptional regulators in metazoa and plants. Plant miRNA targets typically contain a single sequence motif with near-perfect complementarity to the miRNA. Here, we extended and applied the program RNAhybrid to identify novel miRNA targets in the complete annotated Arabidopsis thaliana transcriptome. RNAhybrid predicts the energetically most favorable miRNA:mRNA hybrids that are consistent with user-defined structural constraints. These were: (i) perfect base pairing of the duplex from nucleotide 8 to 12 counting from the 5′-end of the miRNA; (ii) loops with a maximum length of one nucleotide in either strand; (iii) bulges with no more than one nucleotide in size; and (iv) unpaired end overhangs not longer than two nucleotides. G:U base pairs are not treated as mismatches, but contribute less favorable to the overall free energy. The resulting hybrids were filtered according to their minimum free energy, resulting in an overall prediction of more than 600 novel miRNA targets. The specificity and signal-to-noise ratio of the prediction was assessed with either randomized miRNAs or randomized target sequences as negative controls. Our results are in line with recent observations that the majority of miRNA targets are not transcription factors.
Molecular Computation Based on Tile Assembly Model: Modular-Multiplication and Modular-Square over Finite Field GF(2N)

In recent years, there are some questions worth study that how to implement the arithmetic operations based on DNA computing. This paper proposes that how to calculate modular-multiplication and modular-square simultaneously in one DNA computing model. This model approaches modular-multiplication and modular-square in the same way. It is given that the bitwise assembly rules manifested as 7746 types of computation tiles performing six different functions. The assembly time is 3n-3 and the space complexity 2n2-3n+1. Compared to previous works, this model achieves more functionalities and it is easier to encode the seed configuration. It's assembly speed is more faster. Without considering its functionality, this model could be improved continually by adding 32 types of computation tiles and adjusting seed configuration. Time complexity and space complexity could respectively reach to 3n-4 and 2n2-4n+2.
A diversified memory built-in self-repair approach for nanotechnologies

Memory built in self repair (BISR) is gaining importance since several years. Because defect densities are increasing with submicron scaling, more advanced solutions may be required for memories to be produced with the upcoming nanometric CMOS process generations. This problem will be exacerbated with nanotechnologies, where defect densities are predicted to reach levels that are several orders of magnitude higher than in current CMOS technologies. For such defect densities, traditional memory repair is not adequate. This work presents a diversified repair approach merging ECC codes and self-repair, for repairing memories affected by high defect densities. The approach was validated by means of statistical fault injection simulations considering defect densities as high as 3*10/sup -2/% (3% of cells are defective). The obtained results show that the approach provides close to 100% memory yield, by means of reasonable hardware cost, for technologies of very poor quality. Thus, the extreme defect densities that many authors predict for nanotechnologies do not represent a show-stopper, at least as concerning memories.
What do you see when you interact with friends online?: face, hand, or canvas?

People use plethora of interactive remote conference tools for various tasks ranging from collaborative works to entertainment needs. The tasks are often distinguishable in terms of their types and users' usage patterns. We present a preliminary user study designed to explore the different usage patterns derived by performing different types of tasks. In this study, 18 people used an interactive remote conference tool for three types of tasks; Collaborative Creation, Cooperative Problem Solving, and Competitive Game Play with different screen configurations. We analyzed usage patterns using an eye-tracker as well as the result from post experimental questionnaire. We found that different tasks resulted in different gaze patterns. We also present an interesting finding on how users mistakenly report the use of the tool by contrasting the result with the questionnaire and eye-tracking log.
Experimental studies of autonomous driving of a vehicle on the road using LiDAR and DGPS

This paper presents the practical method of finding the drivable path on the road for the autonomous vehicle using a multi-channel LiDAR sensor. Determination of the drivable path is quite important for the vehicle to guarantee the safety of the driver. To increase the reliability of determination, many sensors should be used and fused together. The LiDAR sensor is one of potential sensors among many sensors for the accurate estimation of the road condition by extracting curb information on the road. The global map is built on the basis of the point clouds from the sensor with the help of DGPS information and the drivable path is determined. Actual driving experiments on the road are performed with the test vehicle based on the sensor fusion between DGPS and LiDAR.
Scheduling of Linear Algebra Kernels on Multiple Heterogeneous Resources

In this paper, we consider task-based dense linear algebra applications on a single heterogeneous node which contains regular CPU cores and a set of GPU devices. Efficient scheduling strategies are crucial in this context in order to achieve good and portable performance. HeteroPrio, a resource-centric dynamic scheduling strategy has been introduced in a previous work and evaluated for the special case of nodes with exactly two types of resources. However, this restriction can be limiting, for example on nodes with several types of accelerators, but not only this. Indeed, an interesting approach to increase resource usage is to group several CPU cores together, which allows to use intra-task parallelism. We propose a generalization of HeteroPrio to the case with several classes of heterogeneous workers. We provide extensive evaluation of this algorithm with Cholesky factorization, both through simulation and actual execution, compared with HEFT-based scheduling strategy, the state of the art dynamic scheduling strategy for heterogeneous systems. Experimental evaluation shows that our approach is efficient even for highly heterogeneous configurations and significantly outperforms HEFT-based strategy.
Establishing the “ Statistical Accuracy ” of Uniform Crime Reports ( UCR ) in West Virginia

• The methodology introduced in this report offers a valid approach for assessing the statistical accuracy of UCR crime statistics in WV and the nation. • Of the 31,084 reported crimes in the population, 1,297 were estimated to contain a classification error. • An estimated 4.17% of all records reported for the population of 12 law enforcement agencies in this study were misclassified. • The UCR Part I crimes of aggravated assault, burglary, larceny, and robbery contained a statistically significant amount of classification error. • A significant level of error was found for nonindex crimes such as simple assault/intimidation, unfounded offenses, and general incidents. • A great deal of overlap in classification error was found between some individual crimes such as aggravated-simple assaults and larceny-burglary offenses. • Both the Violent Crime and Index totals for the state were significantly undercounted in reported UCR statistics. • The Violent Crime Total for WV was undercounted by 22.49% compared to the Index Total at 2.35%. • The Property Crime Total for the state did not contain a significant amount of classification error at 0.18%. • The differentiation between aggravated and simple assault crimes accounted for a disproportionate amount of classification error in reported UCR statistics in WV. • The inclusion of simple assault into Violent Crime and Index Totals reduced classification error for these categories to nonsignificant levels. The Uniform Crime Reporting (UCR) Program is a national initiative involving more than 17,000 city, county, and state law enforcement agencies which voluntarily report crime data to the FBI (FBI, 2004). 1 The main objective of the UCR Program is to generate a valid set of crime statistics for use in law enforcement administration, operation, and management. Over the years, however, the information gathered and reported through the UCR Program has become a social indicator for the nation. The public looks to the statistics generated from the UCR Program for information on fluctuations in the level of crime. Meanwhile, criminologists, sociologists, legislators, municipal planners, the media, and other students of criminal justice use the statistics for varied research and planning purposes (FBI, 2004). Such widespread use of UCR information has underscored the importance of ensuring its accuracy. While considerable attention has been focused on errors associated with victim reporting and missing data (Hart and Rennison, 2003; Maltz, 1999), few studies have sought to ascertain the magnitude of error resulting from the erroneous classification of crimes. …
Feature space denoising improves word spotting

Some of the sliding window features commonly used in off-line handwritten text recognition are inherently noisy or sensitive to image noise. In this paper, we investigate the effects of several de-noising filters applied in the feature space and not in the image domain. The purpose is to target the intrinsic noise of these features, stemming from the complex shapes of handwritten characters. This noise is present even if the image has been captured without any kind of artefacts or noise. An evaluation, using a public database, is presented showing that the recognition of word-spotting can be improved considerably by using de-noising filters in the feature space.