Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of level shifter for low power applications

The paper demonstrates a new configuration of level shifters for low power application which is a 45nm CMOS technology and simulated in cadence tool. Conventional level shifter is being designed utilizing the six transistors, eight transistors and ten transistors. The parameters being calculated for the circuit designed are average power dissipation, average noise and leakage voltage. This type of level shifter requires two different voltage supplies: the input logic signal voltage supply (VDDL) and output logic signal voltage supply (VDDH). The voltage used for the working of the designed circuits has been from 0.2 volt to 0.7 volt. Level shifter circuits are popularly used as an interface to different voltage domain in System on chip (SoC) and modern ICs. It has been known that conventional level shifter has the ability will achieve low power operation and convert high voltage digital input signals to high voltage digital output signals. A conventional level shifter capable of handling extremely low voltage input. It is used to shift any voltage level to a desired level. This level shifter fulfills those needs of lower power system and also will a useful for ICs and system on chip (SoC).
Orthogonal Experimental Design method used in Particle Swarm Optimization for multimodal problems

Orthogonal Experimental Design (OED) method is usually used to study the effect of several factors simultaneously and the best combination of factor levels can be found in several tests. The Particle Swarm Optimization (PSO) can utilize OED to improve the searching ability. However, the main effect of OED holds only when no or weak interaction of factors exists. This limitation of OED makes PSO search effective on unimodal or simple problems but very vulnerable on complex multimodal problems. This paper presents an effective method utilizing OED on multimodal problems. A new vector is formed through learning particle's previous and neighborhood's best vector. Instead of treating the new vector as exemplar for others to follow, this new vector is treated as base vector which needs to be explored further. Experimental studies on a set of test functions show that OED method used in this way has better robustness and converges closer to the global optimum than several other peer algorithms.
Exploring opportunistic access techniques using stochastic models: Dynamic spectrum access without sensing

In this paper we explore the use of stochastic approaches for opportunistic access based on models of primary user activity. We focus in particular on a scenario where the secondary user does not perform traditional sensing at all, but relies only on statistical information of primary transmissions. Using techniques from the theory of alternating renewal processes we show that, for different primary user constraints, simple statistical transmission schemes exist based on selecting sufficiently low transmission probability for the secondary user. We describe the general framework for deriving the required results, and give a detailed case study assuming that the primary transmitter activity patterns follow exponential distributions. The results for the exponential case show that limited but stable capacity can be achieved by the secondary user for any primary activity level. We also discuss the possible usage scenarios for such statistical approaches, focusing on situations in which traditional sensing is challenging due to high level of dynamics in the environment.
Exclusive Key Based Group Rekeying Protocols

Abstract—In this paper, we first clarify the meaning of research on 1-resilient group rekeying protocols by showing that they are actually building blocks for constructing hybrid group rekeying protocols with tunable collusion-bandwidth tradeoffs. We then construct secure and efficient 1-resilient group rekeying protocols based on the idea of exclusive key. Given a group of users, an exclusive key for a user i is a key shared by all users in this group except i, and thus can be used to exclude i from this group effectively. We first present three personal key assignment algorithms based on this idea. The first is based on independent exclusive keys, and thus has a great storage requirement. The other two are based on functionally-dependent exclusive keys, and thus greatly reduce the storage requirement. Employing each personal key assignment algorithm, we propose both a stateful group rekeying protocol and a stateless one. We prove that all six protocols are secure against single-user attacks (i.e., 1-resilient) in a symbolic security model. Performance comparisons between our protocols and related ones show that either of the proposed Protocol III and Protocol III' is the best in its own class.
Scale Invariant Feature Transform Flow trajectory approach with applications to human action recognition

In this paper, we apply Scale Invariant Feature Transform (SIFT) Flow, a recently developed method of video representation to human action recognition. SIFT Flow provides a convenient way to express the displacement between keypoints, points which are invariant to scale changes spatially, in two adjacent frames of a video, and it furnishes a compact way to describe the behaviour at keypoints and their neighborhoods as they move in time. A dense trajectory approach using keypoints is developed, and its shape descriptor can be obtained. Local appearance descriptor like histogram of oriented gradients (HOG) evaluated at keypoints, local motion descriptors, like histogram of oriented flows (HOF) and motion boundary histogram (MBH) can be evaluated using SIFT flows. The HOG, HOF MBH, evaluated using SIFT flow, and the keypoint trajectory shape descriptor, together can be used as a feature vector to represent the video. We compare the performance of a number of classifiers to classify the feature vectors, including a bag-of-words approach, support vector machines, linear and nonlinear. It is shown that the proposed novel approach based on keypoints, and SIFT flows produces competitive results when compared with other state-of-the-art results.
A New Efficient IP Address Lookup Based on Binary Search

IP address lookup is an important function in a router to settle overall performance of the router. Binary search on range is a useful algorithm based on binary search for IP address lookup. However, this approach requires a lot of memories for adapting binary search to variable-length prefixes. We propose the improved algorithm to reduce memory requirements than binary search on range dramatically by a different view of a range. It reduces the number of entries and the memory requirements for pointers by about 21 % and 52% on average, respectively.
The Level of Circulating Endothelial Progenitor Cell Is Associated with Cerebral Vasoreactivity: A Pilot Study

Endothelial progenitor cell is known to be able to repair injured vessels. We assessed the hypothesis that endothelial progenitor cell also modulates cerebral endothelial function in healthy status. We used transcranial color-coded sonography to measure middle cerebral arterial vasoreactivity to CO2 (breath-holding index) in healthy subjects and observed its relationship with the number of circulating CD34CD133+ cells. To detect significant correlations between each characteristic and breath-holding index of middle cerebral artery, we used univariate and multivariate regression analyses. 22 young healthy subjects were included in the present study (6 men, 16 women; mean age: 28.45 ± 3.98 years, range: 22–34 years). The mean breath-holding index and CD45lowCD34+CD133+ cells number were 0.95 ± 0.48% and 0.52 ± 0.26, respectively. The level of CD34CD133+ cells was independently associated with middle cerebral artery's vasoreactivity (r = 0.439, P = 0.04). Our results suggest that endothelial progenitor cell also modulates healthy cerebral vessels' endothelial function. This ability of endothelial progenitor cell could be potentially applied therapeutically and for prevention in conditions with cerebral endothelial dysfunction and cerebral ischemia.
Ultrafast relaxation dynamics of hot optical phonons in graphene

Using ultrafast optical pump-probe spectroscopy, we study the relaxation dynamics of hot optical phonons in few-layer and multilayer graphene films grown by epitaxy on silicon carbide substrates and by chemical vapor deposition on nickel substrates. In the first few hundred femtoseconds after photoexcitation, the hot carriers lose most of their energy to the generation of hot optical phonons which then present the main bottleneck to subsequent cooling. Optical phonon cooling on short time scales is found to be independent of the graphene growth technique, the number of layers, and the type of the substrate. We find average phonon lifetimes in the 2.5–2.55 ps range. We model the relaxation dynamics of the coupled carrier-phonon system with rate equations and find a good agreement between the experimental data and the theory. The extracted optical phonon lifetimes agree very well with the theory based on anharmonic phonon interactions.
Stochastic event capture using mobile sensors under linear case

The quality of coverage achieved by mobile sensors depends on the velocity, mobility pattern, number of mobile sensors deployed and the dynamics of the phenomenon being sensed. The gains attained by mobile sensors over static sensors and the optimal motion strategies for mobile sensors are not well understood. In this paper we consider the following event capture problem: The events of interest arrive at certain points in the sensor field and disappear according to known arrival and departure time distributions. An event is said to be captured if it is sensed by one of the mobile sensors before it fades away. We analyze how the quality of coverage scales with velocity and number of mobile sensors. We present algorithm for the motion planning problem: for sensors with fixed speed, what is the minimum number of sensors required to satisfy a bound on the event loss probability? For the minimum sensor problem, when the robots are restricted to move along a line our algorithm return the number of sensors used is within a factor of two of the optimal solution. Simulation experiments show that the Superiority of the proposed algorithm.
Contributions

S ommaire . — Introduction, p. 64. — Chap. I : Révision des données existantes sur les oeufs, larves et hôtes des Leucostomatina, p. 66. -— Chap. II : Observations nouvelles et données biologiques sur Leucos­ toma analis (Mg.), s. str. , p. 68. —· Chap. Ill : Taxonomie du genre Leu­ costoma et description de Leucostoma analis (Mg.), s. str. , p. 74.
A system for road sign detection, recognition and tracking based on multi-cues hybrid

This paper presents a road signs detection, recognition and tracking system based on multi-cues hybrid. In detection stage, the color and gradient cues are used to segment the interesting regions, and the corner and geometrical cues are used to detect the signs. A pseudo RGB-HSI conversion method without the need of nonlinear transformation is presented for color extraction. In recognition stage, a coarse classification is performed using the corresponding relationship of color and shape, then the Support Vector Machines with Binary Tree Architecture is built to recognize each category of road sign. Furthermore, we present a finite-state machine to decide whether a road sign is really recognized by fusion multi-frame recognition results or not. In order to reduce recognition errors, Lucas-Kanade feature tracker is introduced for road sign tracking. Experimental results in different conditions, including sunny, cloudy, and rainy weather demonstrates that most road signs can be correctly detected and recognized with a high accuracy and a frame rate of approximately 15 frames per second on a standard PC.
Summary of Biosurveillance-relevant statistical and data mining technologies

This is directly applicable to a scalar signal (such as “number of respiratory cases today”. This method, more commonly used in computational finance, simply compares the count during the current time period with the weighted average of the counts of recent days. Exponential weighting is typically used, where the half-life is known as the “time window” parameter. This time-window parameter is typically chosen by hand. Hanning window, a low-pass filter, is another commonly used method for computing the trend of a time series. We prefer the Serfling and Univariate HMM methods described below.
Time-Perception Network and Default Mode Network Are Associated with Temporal Prediction in a Periodic Motion Task

The updating of prospective internal models is necessary to accurately predict future observations. Uncertainty-driven internal model updating has been studied using a variety of perceptual paradigms, and have revealed engagement of frontal and parietal areas. In a distinct literature, studies on temporal expectations have also characterized a time-perception network, which relies on temporal orienting of attention. However, the updating of prospective internal models is highly dependent on temporal attention, since temporal attention must be reoriented according to the current environmental demands. In this study, we used functional magnetic resonance imaging (fMRI) to evaluate to what extend the continuous manipulation of temporal prediction would recruit update-related areas and the time-perception network areas. We developed an exogenous temporal task that combines rhythm cueing and time-to-contact principles to generate implicit temporal expectation. Two patterns of motion were created: periodic (simple harmonic oscillation) and non-periodic (harmonic oscillation with variable acceleration). We found that non-periodic motion engaged the exogenous temporal orienting network, which includes the ventral premotor and inferior parietal cortices, and the cerebellum, as well as the presupplementary motor area, which has previously been implicated in internal model updating, and the motion-sensitive area MT+. Interestingly, we found a right-hemisphere preponderance suggesting the engagement of explicit timing mechanisms. We also show that the periodic motion condition, when compared to the non-periodic motion, activated a particular subset of the default-mode network (DMN) midline areas, including the left dorsomedial prefrontal cortex (DMPFC), anterior cingulate cortex (ACC), and bilateral posterior cingulate cortex/precuneus (PCC/PC). It suggests that the DMN plays a role in processing contextually expected information and supports recent evidence that the DMN may reflect the validation of prospective internal models and predictive control. Taken together, our findings suggest that continuous manipulation of temporal predictions engages representations of temporal prediction as well as task-independent updating of internal models.
Using Symbolic Learning to Improve Knowledge-Based Neural Networks

The previously-described KBANN system integrates existing knowledge into neural networks by defining the network topology and setting initial link weights. Standard neural learning techniques can then be used to train such networks, thereby refining the information upon which the network is based. However, standard neural learning techniques are reputed to have difficulty training networks with multiple layers of hidden units; KBANN commonly creates such networks. In addition, standard neural learning techniques ignore some of the information contained in the networks created by KBANN. This paper describes a symbolic inductive learning algorithm for training such networks that uses this previously-ignored information and which helps to address the problems of training "deep" networks. Empirical evidence shows that this method improves not only learning speed, but also the ability of networks to generalize correctly to testing examples.
Der Zusammenhang der Theorie des geplanten Verhaltens mit der selbstberichteten Individualisierungspraxis von Lehrpersonen

Einstellungen stellen als Teil der professionellen Handlungskompetenz von Lehrpersonen eine wichtige handlungsleitende Determinante des Unterrichtsgeschehens dar. Hierzu wird auf Basis der Theorie des geplanten Verhaltens ein Erwartungs-mal-Wert-theoretisches Einstellungsinstrument faktoriell validiert und der Zusammenhang der inklusiven Einstellung, Normvorstellung und Lehrerselbstwirksamkeitsuberzeugung mit der selbstberichteten Individualisierungspraxis von Lehrpersonen betrachtet. Die Ergebnisse einer exploratorischen Faktorenanalyse zeigen in Studie I drei Einstellungsfaktoren, die in Studie II konfirmatorisch bestatigt wurden. Studie III zeigt, dass die selbstberichtete Individualisierungspraxis durch die Normvorstellung und die Intention, sich den Herausforderungen eines inklusiven Unterrichtes anzunehmen, vorhergesagt werden kann. Die Intention mediiert dabei den Zusammenhang der selbstberichteten Individualisierungspraxis mit der Einstellung vollstandig und mit der Normvorstellung partiell. Die Lehrerselbstwirksamkeitsuberzeugung sagt demgegenuber die selbstberichtete Individualisierungspraxis weder direkt noch indirekt vorher. (DIPF/Orig.) As part of the professional competence the attitudes of general classroom teachers play a key role in regard to their instructional quality. On the basis of the expectation – value theory of planned behavior the factorial structure of an inclusive attitude instrument is validated and the relation between inclusive attitudes, subjective inclusive norms, teachers’ self-efficacy and teachers’ self-reported individualized teaching is investigated. The results of an exploratory factor analysis in study I suggested three dimensions of attitude, which were confirmed by a confirmatory factor analysis in study II. The results of study III indicate that teachers’ self-reported individualized teaching can be predicted by subjective inclusive norms and the intention to meet the challenge to make adaption in the general education classroom setting. The intention thereby completely mediates the relation between teachers’ self-reported individualized teaching and inclusive attitudes and it mediates partially the relation between teachers’ self-reported individualized teaching and subjective inclusive norms. In contrast to that, teachers’ self-efficacy does not predict the level of teachers’ self-reported individualized teaching neither directly nor indirectly. (DIPF/Orig.)
Representing and solving local and global ambiguities as multimodal and hyperedge constraints in a generalized graph SLAM framework

Graph-based Simultaneous Localization and Mapping (SLAM) has experienced a recent surge towards robust methods. These methods take the combinatorial aspect of data association into account by allowing decisions of the graph topology to be made during optimization. In this paper, the Generalized Graph SLAM framework for SLAM under ambiguous data association is presented, and a formal description of using hyperedges to encode uncertain loop closures is given for the first time. The framework combines both hyperedges and multimodal Mixture of Gaussian constraints to cover all sources of ambiguity in SLAM. An extension of the authors' multimodal Prefilter method is developed to find good initial conditions in such a generalized multimodal hypergraph. Experiments on a real world 3D dataset show that the novel multimodal hypergraph Prefilter method is both significantly more robust and faster than other robust state-of-the-art methods.
The Effect of Fluoxetine on Progression in Progressive Multiple Sclerosis: A Double-Blind, Randomized, Placebo-Controlled Trial

Preclinical studies suggest that fluoxetine may have neuroprotective properties. In this pilot study forty-two patients with secondary or primary progressive MS were randomized to receive fluoxetine 20 mg twice daily or placebo for 2 years. Every 3 months the Expanded Disability Status Scale (EDSS), 9-hole peg test (9-HPT) and ambulation index (AI) were assessed. Brain MRI scans, Multiple Sclerosis Functional Composite, Fatigue Impact Scale, Guy's neurological disability Scale and SF-36 were performed at baseline, year 1 and year 2. Seven out of 20 (35%) patients in the fluoxetine group and 7 out of 22 (32%) patients in the placebo group had sustained progression on the EDSS, 9-HPT, or AI at 2 years. No differences were identified between the 2 treatment groups with respect to secondary clinical outcomes and T2 lesion load, grey matter volume and white matter volume. An unanticipated low rate of disability progression in the placebo group decreased the statistical power. At least 200 patients would have been needed to detect a 50% treatment effect. This trial shows that fluoxetine was generally well tolerated, but no assumptions can be made about a possible treatment effect. An adequately powered controlled trial of fluoxetine in progressive MS is still warranted. This trial is registered with Current Controlled Trials ISRCTN38456328.
Point rendering of non-manifold surfaces with features

We are concerned with producing high quality images of implicit surfaces, in particular those with non-manifold features. In this work we present a point based technique that improves the rendering of non-manifold implicit surfaces by using point and gradient information to prune plotting nodes resulting from using octree spatial subdivision based on the natural interval extension of the surface's function. The use of intervals guarantees that no parts of the surfaces are missed in the view volume and the combination of point and gradient sampling preserves this feature while greatly enhacing the quality of point based rendering of implicit surfaces. We also sucessfully render non-manifold features of implicit surfaces such as rays and thin sections. We illustrate the technique with a number of example surfaces.
The local galaxy density and the arm class of spiral galaxies

We have examined the effect of the environmental density on the arm classification of an extensive sample of spiral galaxies included in the Nearby Galaxy Catalog (Tully, 1988a). We have also explored the dependence of the arm class of a galaxy on other factors, such as its blue absolute magnitude and its disk-to-total mass ratio, inferred in the literature either from the gradient of a good galaxy rotation curve or from a photometric mass decomposition method. We have found that the arm class is strongly related to the absolute magnitude in the mid-type spirals (in the sense that grand design galaxies are, on average, more luminous than flocculent objects), whilst this relation is considerably weaker in the early and late types. In general the influence of the local density on the arm structure appears to be much weaker than that of the absolute magnitude. The local density acts essentially in strengthening the arm class--absolute magnitude relation for the mid types, whereas no environmental density effects are observed in the early and late types. Using the most recent estimates of the disk-to-total mass ratio, we do not confirm this ratio to be a significant factor which affects the arm class; nevertheless, owing to poor statistics and large uncertanties, the issue remains open. Neither a local density effect nor an unambiguous bar effect on the disk-to-total mass ratio is detectable; the latter finding may challenge some theoretical viewpoints on the formation of bar structures.