Miracle-dz/UniFAR-FTUs huggingface dataset:
ECR: A Powerful and Low-Complexity Error Cancellation Rewiring Scheme

Rewiring is known to be a class of logic restructuring technique that is at least equally powerful in flexibility compared to other logic transformation techniques. Especially it is wiring sensitive and is particularly useful for interconnect-based circuit synthesis processes. One of the most well-studied rewiring techniques is the ATPG-based Redundancy Addition and Removal (RAR) technique which adds a redundant alternative wire to make an originally irredundant target wire become redundant and thus removable. In this article, we propose a new Error-Cancellation-based Rewiring scheme (ECR) which can also identify non-RAR-based rewiring operations with high efficiency. In ECR scheme, it is not necessary for alternative wires to be redundant. Based on the notion of error cancellation, we analyze and reformulate the rewiring problem, and a more generalized rewiring scheme is developed to detect more rewiring cases which are not obtainable by existing schemes while it still maintains a low runtime complexity. Comparing with the most recent non-RAR rewiring tool IRRA, the total number of alternative wires found by our approach is about doubled (202%) while the CPU time used is just slightly more (8%) upon benchmarks preoptimized by ABC’s rewriting. Our experimental results also suggest that the ECR engine is more powerful than IRRA in FPGA technology mapping.
Priming With 1-Hz Repetitive Transcranial Magnetic Stimulation Over Contralesional Leg Motor Cortex Does Not Increase the Rate of Regaining Ambulation Within 3 Months of Stroke: A Randomized Controlled Trial

Background The potential benefits of repetitive transcranial magnetic stimulation (rTMS), applied either alone or as a combination treatment, on recovery of lower limbs after stroke have been insufficiently studied. Objective The aim of the study was to evaluate the effect of priming with 1-Hz repetitive transcranial magnetic stimulation over contralesional leg motor area with a double-cone coil before physical therapy on regaining ambulation. Methods Thirty-eight subacute stroke patients with significant leg disabilities were randomly assigned into the experimental group or control group to receive a 15-min real or sham 1-Hz repetitive transcranial magnetic stimulation, respectively, over the contralesional motor cortex representing the quadriceps muscle followed by 45-min physical therapy for 15 sessions for 3 wks. Functional measures, motor evoked potentials, and quality of life were assessed. Results There was no significant difference between experimental group and control group regarding the recovery in ambulation, balance, motor functions, and activity of daily living. No significant difference was found in other functional measures and the quality of life. Only the control group displayed significantly increased cortical excitability of the contralesional hemisphere after the intervention. Conclusions The present study found that insufficient evidence that contralesional priming with 1-Hz repetitive transcranial magnetic stimulation improves ambulatory and other motor functions among patients with a severe leg dysfunction in subacute stroke.
Lexical Class Semantic Analysis

Semantic analysis of lexical classes is a funda- mental step of semantic analysis based on stochastic semantic parsing. The lexical class is a single word or a word group with specific semantic information such as dates, times, cities, etc. Having obtained a set of lexical classes, a semantic parse tree can be built upon it. This tree describes the relation between lexical classes and their appropriate superior concepts. This paper describes an implementation of a lexical class identification based on context-free grammars and parsing methods. The semantic analysis of lexical classes is based on grammars enriched with semantic tags. The main algorithm is described together with the experimental results. I. INTRODUCTION The human-computer communication is one of the most challenging topics of Artificial Intelligence (AI). Since speech is the most natural and the most common way of human communication, there is a need for using it as a communication medium between human and computer. The process of extracting the meaning from an utterance by a computer and storing the meaning in a computer model is called Natural Language Understanding (NLU). Semantic analysis is the first step of the NLU process. The goal of semantic analysis is to represent what the subject intended to say. Recent trends in the area of NLU are heading towards making all the processes stochastic. In a stochastic method of semantic parsing, semantic knowledge is usually represented by some kind of a tree.
Expression Profiling of Lymphocyte Plasma Membrane Proteins*S

The physicochemical properties of plasma membrane proteins of mammalian cells render them refractory to systematic analysis by two-dimensional electrophoresis. We have therefore used in vivo cell surface labeling with a water-soluble biotinylation reagent, followed by cell lysis and membrane purification, prior to affinity capture of biotinylated proteins. Purified membrane proteins were then separated by solution-phase isoelectric focusing and SDS-PAGE and identified by high-pressure liquid chromatography electrospray/tandem mass spectrometry. Using this approach, we identified 42 plasma membrane proteins from a murine T cell hybridoma and 46 from unfractionated primary murine splenocytes. These included three unexpected proteins; nicastrin, osteoclast inhibitory lectin, and a transmembrane domain-containing hypothetical protein of 11.4 kDa. Following stimulation of murine splenocytes with phorbol ester and calcium ionophore, we observed differences in expression of CD69, major histocompatibility complex class II molecules, the glucocorticoid-induced TNF receptor family-related gene product, and surface immunoglobulin M and D that were subsequently confirmed by Western blot or flow cytometric analysis. This approach offers a generic and powerful strategy for investigating differential expression of surface proteins in many cell types under varying environmental and pathophysiological conditions.
A low-voltage CMOS current squarer circuit

This paper presents a low-voltage current-mode CMOS squarer circuit. The low voltage operation can be achieved by the simple circuit design of the simple CMOS current mirrors and voltage shifting circuit. The realization method uses the MOS square-law technique in saturation region and require only V/sub Th/+2V/sub DS/. Simulation results show that the proposed circuit works well at supply voltage of 1V for a 0.5 /spl mu/m technology.
Adaptive grasping by multi fingered hand with tactile sensor based on robust force and position control

In this paper we propose a new robust force and position control method for property-unknown objects grasping. The proposed control method is capable of selecting the force control or position control, and smooth and quick switching according to the amount of the external force. The proposed method was applied to adaptive grasping by three-fingered hand which has 12 DOF, and the experimental results revealed that the smooth collision process and the stable grasping is realized even if the precise surface position, the mass and the stiffness are unknown. In addition a new algorithm determines the grasp force according to the "slip" measured with the tactile sensor and the viscoelastic media on the fingertip. This algorithm works at starting and stationary state, so the friction and mass unknown object grasping is realized by the effectual force.
Finnish and Greek early childhood teachers’ perspectives and practices in supporting children’s autonomy

ABSTRACT Kindergarten teachers from different cultural backgrounds attribute various meanings to children’s autonomy. There seems to be cultural differences in early childhood education curricula with regard to how a child’s autonomy is described and how it is supported. This qualitative study asks: how do teachers narrate their perspective and pedagogical support of children’s autonomy, and what kinds of similarities and differences in the pedagogy and practices can be found in Finnish and Greek early childhood education (ECEC) contexts? The data of this qualitative study consist of a semi-structured questionnaire of 14 kindergarten teachers and observations of their pedagogical practices in the day care groups of 4- to 5-year-old children. The results suggest that teachers’ overall conception of autonomy was identical, but the different cultural contexts and curriculums affected the way the teachers emphasized and valued different dimensions of autonomy.
Identification of the e allele at the Extension locus (MC1R) in Brazilian Creole sheep and its role in wool color variation.

The melanocortin 1 receptor (MC1R) gene has been described as responsible for the black color in some breeds of sheep, but little is known about its function in many colored breeds, particularly those with a wide range of pigmentation phenotypes. The Brazilian Creole is a local breed of sheep from southern Brazil that has a wide variety of wool colors. We examined the MC1R gene (Extension locus) to search for the e allele and determine its role in controlling wool color variation in this breed. One hundred and twenty-five animals, covering the most common Creole sheep phenotypes (black, brown, dark gray, light gray, and white), were sequenced to detect the mutations p.M73K and p.D121N. Besides these two mutations, three other synonymous sites (429, 600, and 725) were found. The dominant allele (E(D): p.73K, and p.121N) was found only in colored animals, whereas the recessive allele (E⁺: p.73M, and p.121D) was homozygous only in white individuals. We concluded that MC1R is involved in the control of wool color in Brazilian Creole sheep, particularly the dark phenotypes, although a second gene may be involved in the expression of the white phenotype in this breed.
Visual language modeling for image classification

Although it has been studied for many years, image classification is still a challenging problem. In this paper, we propose a visual language modeling method for content-based image classification. It transforms each image into a matrix of visual words, and assumes that each visual word is conditionally dependent on its neighbors. For each image category, a visual language model is constructed using a set of training images, which captures both the co-occurrence and proximity information of visual words. According to how many neighbors are taken in consideration, three kinds of language models can be trained, including unigram, bigram and trigram, each of which corresponds to a different level of model complexity. Given a test image, its category is determined by estimating how likely it is generated under a specific category. Compared with traditional methods that are based on bag-of-words models, the proposed method can utilize the spatial correlation of visual words effectively in image classification. In addition, we propose to use the absent words, which refer to those appearing frequently in a category but not in the target image, to help image classification. Experimental results show that our method can achieve comparable accuracy while performing classification much more quickly.
Hopping and rolling locomotion with spherical tensegrity robots

This work presents a 10 kg tensegrity ball probe that can quickly and precisely deliver a 1 kg payload over a 1 km distance on the Moon by combining cable-driven rolling and thruster-based hopping. Previous research has shown that cable-driven rolling is effective for precise positioning, even in rough terrain. However, traveling large distances using thruster-based hopping, which is made feasible by the lightweight and compliant nature of the tensegrity structure, has not been explored. To evaluate the feasibility of a thruster-based tensegrity robot, a centrally-positioned cold gas thruster with nitrogen propellant was selected, and the system was simulated using the NASA Tensegrity Robotics Toolkit (NTRT) for four hopping profiles on hilly terrains. Optimizing energy efficiency and mechanical capabilities of the tensegrity robot, hopping profiles with a long flight distance per hop, followed by the higher accuracy rolling, are recommended. Simulations also show that thrust regulation can improve energy efficiency. Regulation of thrust magnitude can be achieved using a pressure regulator, but regulation of thrust orientation calls for additional control effort. In this paper, it is demonstrated that gimbal systems as well as shape-shifting control of the tensegrity structure have the potential to regulate thrust orientation. Finally, algorithms for localization and path planning that combine hopping and rolling for energy-efficient navigation are presented.
Self-Regulatory Depletion Effects, Physical Endurance and Muscle Activity: An Examination of Depletion Effects and Trait Self Control as an Effect Modifier

The limited strength model of self-regulation describes self-regulation as a limited, consumable, and renewable internal resource that is depleted when people attempt to control their emotions, thoughts or behaviours (Baumeister & colleagues 1994; 1996). Evidence indicates a consistent relationship across emotional, mental, and physical domains that task performance in all of these areas draws on the same limited resource and is governed by processes occurring within the central nervous system (Galliot, et al., 2007). The main purpose of this study was to examine the effects of selfregulation depletion on muscle activity (EMG) and physical stamina via an isometric task (ankle dorsiflexion). A secondary objective was to investigate trait self-control as an effect modifier of cognitive self-regulation depletion effects on physical stamina. It was hypothesized that individuals would show a greater decline in isometric endurance performance after undergoing a self-regulatory depletion manipulation compared to when they were exposed to· a non-depletion task of similar duration~ It was also expected that participants would exhibit greater increases in EMG amplitude after being depleted compared to when not depleted. Additionally, it was hypothesized that people who scored lower on a measure of trait self-control would demonstrate greater depletion effects (i.e., greater pre-to-post performance differences) than those who scored higher on trait self-control. The study was a within-subjects cross-over design involving 31 informed and consenting sedentary university students (Mage= 21.72 ± 2.57 years). Participants were stratified by gender and randomized to experience either cognitive depletion (modified Stroop task) or non-depletion (colour word reading task) for their
Insulator speckles associated with long-distance chromatin contacts

ABSTRACT Nuclear foci of chromatin binding factors are, in many cases, discussed as sites of long-range chromatin interaction in the three-dimensional nuclear space. Insulator binding proteins have been shown to aggregate into insulator bodies, which are large structures not involved in insulation; however, the more diffusely distributed insulator speckles have not been analysed in this respect. Furthermore, insulator binding proteins have been shown to drive binding sites for Polycomb group proteins into Polycomb bodies. Here we find that insulator speckles, marked by the insulator binding protein dCTCF, and Polycomb bodies show differential association with the insulator protein CP190. They differ in number and three-dimensional location with only 26% of the Polycomb bodies overlapping with CP190. By using fluorescence in situ hybridization (FISH) probes to identify long-range interaction (kissing) of the Hox gene clusters Antennapedia complex (ANT-C) and Bithorax complex (BX-C), we found the frequency of interaction to be very low. However, these rare kissing events were associated with insulator speckles at a significantly shorter distance and an increased speckle number. This suggests that insulator speckles are associated with long-distance interaction. Summary: Insulator speckles have been discussed to be either storage places for insulator proteins or to represent sites of long-range chromatin interaction. Here we demonstrate their association with interacting chromatin.
A Min-Max Optimization Framework For Online Graph Classification

Traditional online learning for graph node classification adapts graph regularization into ridge regression, which may not be suitable when data is adversarially generated. To solve this issue, we propose a more general min-max optimization framework for online graph node classification. The derived online algorithm can achieve a min-max regret compared with the optimal linear model found offline. However, this algorithm assumes that the label is provided for every node, while label is scare and labeling is usually either too time-consuming or expensive in real-world applications. To save labeling effort, we propose a novel confidence-based query approach to prioritize the informative labels. Our theoretical result shows that an online algorithm learning on these selected labels can achieve comparable mistake bound with the fully-supervised online counterpart. To take full advantage of these labels, we propose an aggressive algorithm, which can update the model even if no error occurs. Theoretical analysis shows that the mistake bound of the proposed method, thanks to the aggressive update trials, is better than conservative competitor in expectation. We finally empirically evaluate it on several real-world graph databases. Encouraging experimental results further demonstrate the effectiveness of our method.
Synthesis of 2-D state-space fixed-point digital-filter structures with minimum roundoff noise

Based on a roundoff-noise analysis, a general synthesis procedure is developed which leads to an optimal local state-space 2-D digital-filter realization that minimizes the output-noise power due to roundoff subject to a scaling condition on the state variables. The outputnoise power and the signal scaling condition are closely related to two positive-definite matrices W and K . These matrices provide two sets of invariants, called the 2-D second-order modes of the filter, which play a crucial role in the minimization of the output-noise power. With the availability of matrices W and K , the 2-D similarity transformation that yields an optimal state-space realization can be obtained by solving separately two 1-D optimization problems so that the well-developed techniques for minimizing roundoff noise in 1-D state-space digital filters can also be used for minimizing roundoff noise in 2-D state-space digital filters.
The Relationship between the Transforming Growth Factor β1 T29C Gene Polymorphism and Left Ventricular Geometry and Function in Hypertensive Subjects

The distribution of the T29C TGFβ1 gene polymorphism was analyzed in 198 hypertensives with left ventricular hypertrophy (LVH) and in 235 hypertensives without LVH. Circulating TGFβ1 levels, procollagen type III levels, microalbuminuria, and left ventricular geometry and function were evaluated in all the hypertensives with LVH subgrouped according to T29C TGFβ1 gene polymorphism. Circulating TGFβ1 was evaluated by ELISA technique, procollagen type III by a specific radioimmunoassay, microalbuminuria by radioimmunoassay, and left ventricular geometry and function by echocardiography. All groups were comparable for gender, age, and sex. Regarding T29C TGFβ1 gene polymorphism, prevalence of TC or CC genotypes was significantly (P < .05) higher in hypertensives with LVH than hypertensives without LVH TC and CC LVH hypertensives were characterized by a higher prevalence of subjects with microalbuminuria (P < .05 TC and CC versus TT), by increased levels of TGFβ1, procollagen type III, urinary albumin excretion, LVM, LVM/h2.7, and lower values of left ventricular ejection fraction (P < .05 TC and CC versus TT). Our data suggest that T29C TGFβ1 gene polymorphism was associated with clinical characteristics adequate to recognize a subset of LVH hypertensives with a higher severity of hypertension.
An Extended-Bandwidth Three-Way Doherty Power Amplifier

This paper expounds a three-way Doherty power amplifier (3W-DPA) as a solution to the need for high-efficiency wideband power amplifiers when driven by multi-standard signals. The paper begins with a theoretical analysis of 3W-DPA architecture from which the governing equations are derived. This analysis enables the identification of circuit parameters for maximizing bandwidth. A comprehensive methodology was devised to address the practical design challenges resulting from the transistor nonidealities: nonlinear input capacitance, transistor package, and output capacitance. Based on this methodology, a fully analog 30-W 3W-DPA was designed and implemented using GaN packaged transistors. The 3W-DPA prototype maintained an average drain efficiency of 55% at an output back-off of up to 9 dB, over the frequency range of 0.73-0.98 GHz. The 3W-DPA was successfully linearized when driven with 20-MHz four-carrier wideband code division multiple access (WCMDA) signals. 830- and 900-MHz power-added efficiencies of 47% and 53% was achieved at 32- and 35-dBm average output power, corresponding to a peak-to-average power ratio of 11.7 and 7.14 dB, respectively.
Real-time analysis for wormhole NoC: Revisited and revised

The network delay upper-bound analysis problem is of fundamental importance to real-time applications in Network-on-Chip (NoC). In the paper, we revisit a state-of-the-art analysis model for real-time communication in wormhole NoC with priority-based preemptive arbitration and show that the model may provide pessimistic or even incorrect network delay upper-bound. We then propose a revised analysis model to correct the flaws in the previous model by further classifying indirect interference as upstream and downstream indirect interferences according to the relative positions of traffic flows and taking buffer influence into consideration. Simulated evaluations show that our model provides tighter and correct network delay upper-bound compared with the state-of-the-art model.
Relationships between Robot's Self-Disclosures and Human's Anxiety toward Robots

The research aimed at investigating how self-disclosure of robots affects humans' anxiety and behaviors toward the robots. A psychological experiment (N = 39), comparing between the conditions of no-self-disclosure, positive self-disclosure, and negative self-disclosure from a small-sized humanoid robot, found that the subjects' anxiety toward communication capacity of robots was stable before/after positive self-disclosure from the robot although this anxiety increased under the other conditions. On the other hand, self-disclosure from the subjects was independent to the conditions of the robot's self-disclosure, and the subjects originally having hither anxiety toward discourse with robots before the interaction performed negative self-disclosure toward the robot.
Registration Based Non‐uniform Motion Deblurring

This paper proposes an algorithm which uses image registration to estimate a non‐uniform motion blur point spread function (PSF) caused by camera shake. Our study is based on a motion blur model which models blur effects of camera shakes using a set of planar perspective projections (i.e., homographies). This representation can fully describe motions of camera shakes in 3D which cause non‐uniform motion blurs. We transform the non‐uniform PSF estimation problem into a set of image registration problems which estimate homographies of the motion blur model one‐by‐one through the Lucas‐Kanade algorithm. We demonstrate the performance of our algorithm using both synthetic and real world examples. We also discuss the effectiveness and limitations of our algorithm for non‐uniform deblurring.