Miracle-dz/UniFAR-FTUs huggingface dataset:
Artificial intelligence approach to test vector reordering for dynamic power reduction during VLSI testing

As the feature size is scaled down with process technology advancement, power minimization has become a serious problem for the designers as well as the test engineers. Test vector reordering for dynamic power minimization during combinational circuit testing is a sub-problem of the general goal of low power testing. In this paper we have proposed an AI-based approach to order the test vectors in an optimal manner to minimize switching activity during testing. Empirically, the proposed algorithm yields on an average of about 22% reduction in switching activity over that given by a standard ATPG tool Synopsis TetraMax, which is also more than the reduction after applying existing Chained Lin-Kernighan heuristic.
How Large a Vocabulary Is Needed for Reading and Listening?

This article has two goals: to report on the trialling of fourteen 1,000 word-family lists made from the British National Corpus, and to use these lists to see what vocabulary size is needed for unassisted comprehension of written and spoken English. The trialling showed that the lists were properly sequenced and there were no glaring omissions from the lists. If 98% coverage of a text is needed for unassisted comprehension, then a 8,000 to 9,000 word-family vocabulary is needed for comprehension of written text and a vocabulary of 6,000 to 7,000 for spoken text. L'article a pour objectif de parler des essais menés sur quatorze listes de 1 000 familles de mots tirées du British National Corpus et de l'emploi de ces listes pour évaluer la taille du vocabulaire nécessaire afin de comprendre sans aide l'anglais oral et écrit. Les essais ont révélé que les listes sont adéquatement triées et ne contiennent aucune omission manifeste. Si on doit connaître 98 % des mots d'un texte pour le comprendre sans aide, il faut un vocabulaire de 8 000 à 9 000 familles de mots pour comprendre un texte écrit et un vocabulaire de 6 000 à 7 000 mots pour un texte oral.
IMPACT MONITORING AND FAULT DETECTION USING PIEZOELECTRIC TRANSDUCERS AND WAVELET ANALYSIS

Abstract: In principle, dynamic events (e.g. foreign-object impact and damage-induced acoustic emission) in a structure can be monitored by using piezoelectric transducers to receive the induced wave propagation signals in the structure. However, whether the dynamic events can be correctly identified from the signals depends strongly on signal analysis and interpretation techniques. Conventional techniques use sensor signals in the time domain or frequency domains. They may miss certain important temporal and spectral patterns in a signal that constitute the signatures of the dynamic events and are the key to their effective and reliable interpretation. Impact monitoring and fault detection methods using piezoelectric transducers and wavelet analysis techniques are being developed in the Department of Mechanical Engineering at the University of South Carolina. In these methods, wave propagation signals are collected using arrays of piezoelectric transducers placed on, or embedded in a structure. The collected signals are analyzed using appropriate wavelet transforms. The final interpretation of the sensor signals is based on signal patterns uncovered by the wavelet transforms in correlation with elastic-wave propagation theory. A number of impact tests have been performed on specimens instrumented with piezoelectric transducers. They include simple steel beams and actual composite aircraft panels. The impact tests are used to illustrate the wavelet analysis technique and show how it permits the interpretation of sensor signals into quantitative descriptions of the dynamic events and/or evaluation of the damage and health status of the host structure. Implications of this research to machinery health monitoring and fault detection will be discussed.
Extended prediction rule to optimise early detection of heart failure in older persons with non-acute shortness of breath: a cross-sectional study

Objectives There is a need for a practical tool to aid general practitioners in early detection of heart failure in the elderly with shortness of breath. In this study, such a screening rule was developed based on an existing rule for detecting heart failure in older persons with a diagnosis of chronic obstructive pulmonary disease. The original rule included a history of ischaemic heart disease, body mass index, laterally displaced apex beat, heart rate, elevated N-terminal pro B-type natriuretic peptide and an abnormal ECG. Design Cross-sectional data were used to validate, update and extend the original prediction rule according to a standardised state-of-the-art stepwise approach. Setting Primary care with 30 participating general practices. Participants Community-dwelling people aged ≥65 years with shortness of breath on exertion. Methods and results Validation of the existing screening rule in our population showed satisfying discrimination with a concordance statistic of 0.84 (range 0.80–0.85), but poor calibration. Performance measures were most improved by adding the predictors age >75 years, peripheral oedema and systolic murmur, resulting in a concordance statistic of 0.88 (range 0.85–0.90) and a net reclassification improvement of 31%. A risk score was computed, which showed high accuracy with a negative predictive value of 87% and a positive predictive value of 73%. Evaluating the improved rule in the derivation set and an independent set of patients with type 2 diabetes aged 60 years or older showed satisfying generalisability of the rule. Conclusions Our rule resulted in excellent prediction of heart failure in the large domain of the elderly with shortness of breath, and would help general practitioners to select those needing echocardiography. Trial registration number NCT01202006.
A Hypothesis for Examining Skeletal Muscle Biopsy-Derived Sarcolemmal nNOSμ as Surrogate for Enteric nNOSα Function

The pathophysiology of gastrointestinal motility disorders is controversial and largely unresolved. This provokes empiric approaches to patient management of these so-called functional gastrointestinal disorders. Preliminary evidence demonstrates that defects in neuronal nitric oxide synthase (nNOS) expression and function, the enzyme that synthesizes nitric oxide (NO), the key inhibitory neurotransmitter mediating mechano-electrical smooth muscle relaxation, is the major pathophysiological basis for sluggishness of oro-aboral transit of luminal contents. This opinion is an ansatz of the potential of skeletal muscle biopsy and examining sarcolemmal nNOSμ to provide complementary insights regarding nNOSα expression, localization, and function within enteric nerve terminals, the site of stimulated de novo NO synthesis. The main basis of this thesis is twofold: (a) the molecular similarity of the structures of nNOS α and μ, similar mechanisms of localizations to “active zones” of nitrergic synthesis, and same mechanisms of electron transfers during NO synthesis and (b) pragmatic difficulty to routinely obtain full-thickness biopsies of gastrointestinal tract, even in patients presenting with the most recalcitrant manifestations of stasis and delayed transit of luminal contents. This opinion attempts to provoke dialog whether this approach is feasible as a surrogate to predict catalytic potential of nNOSα and defects in nitrergic neurotransmission. This discussion makes an assumption that similar molecular mechanisms of nNOS defects shall be operant in both the enteric nerve terminals and the skeletal muscles. These overlaps of skeletal and gastrointestinal dysfunction are largely unknown, thus meriting that the thesis be validated in future by proof-of-principle experiments.
An Intelligent Data Fusion Algorithm with Fusion Benefit Pattern Tree for Wireless Sensor Networks

The small size of the wireless sensor networks makes it easy to apply in various applications. However, the small transmission capability limits the applications to different domains. How to fuse the transmitted data to increase the effective transmission of wireless sensor networks is an important issue. This work designs an intelligent data fusion algorithm with fusion benefit pattern tree for wireless sensor networks. the algorithm computes the fusion benefit from past transmission paths. Then, it discovers the association among past transmission patterns according to the fusion benefits to construct an appropriate fusion benefit pattern tree. the topology of fusion benefit pattern tree is taken as a data aggregation tree for data fusion. the experiential results show that the topology can provide higher transmission dataflow.
Interactive 3-D Shape Retrieval System Using the Attributed Relational Graph

One of the issues in 3-D shape retrieval arises from the fact that users suffer inherent difficulty in generating a 3-D query. Therefore, the demands on the user-friendly querying interface such as query by sketch and query by editing are increasing. In this paper, we propose a new shape retrieval system using the attributed relational graph (ARG) composed of nodes with unary attributes and edges with binary relations for interactive searching and comparing 3-D objects. As similar to other systems, the ARG-based retrieval system consists of two components: the ARG generation via feature extraction and its similarity matching. For the ARG generation, we adopt a morphological shape decomposition scheme as a part-based representation. More specifically, the part-based representation can be generated from the decomposition result and encoded into the ARG. In the case of similarity matching, we consider various ARG matching algorithms such as earth mover's distance, spectral analysis, relaxation and modified nested earth mover's distance. Finally, we have conducted the experiments for the ARG with various matching algorithms and the Shape3D, i.e.an international standard for description and browsing of 3-D object database in MPEG-7, using the database of 480 3-D objects with 33 categories in terms of the bull's eye performance, the average normalized modified retrieval rate, and the precision/recall curve.
Matrix models for D-particle dynamics and the string/black hole transition

For a generic two-dimensional 0A string background, we map the Dirac–Born–Infeld action to a matrix model. This is achieved using a canonical transformation. The action describes D0-branes in this background, while the matrix model has a potential which encodes all the information of the background geometry. We apply this formalism to specific backgrounds: for Rindler space, we obtain a matrix model with an upside-down quadratic potential, while for AdS2 space, the potential is linear. Furthermore we analyse the black hole geometry with RR flux. In particular, we show that at the Hagedorn temperature, the resulting matrix model coincides with the one for the linear dilaton background. We interpret this result as a realization of the string/black hole transition.
Moving Object Recognition and Classification based on Recursive Shape Parameter Estimation

We present an approach for recognizing and classifying moving vehicles in monocular images sequences of traac scenes recorded by a stationary camera. A generic vehicle model, represented by a 3D polyhedral model described by 12 length parameters, is used to cover the diierent shapes of road vehicles. The object recognition process is initialized by formulating a model hypothesis using a reference model and initial values provided by a motion segmentation step from a model-based tracking system described previously. This model hypothesis is veriied and the shape as well as the pose and motion parameters of the object are estimated simultaneously. A recursive estimator updates the state description of the shape and motion parameters. In this way all relevant data from the image sequence evaluated so far are accumulated and used for the shape parameter estimation and classiication of a moving vehicle. A classiication is based on the assumption that diierences between class members can be considered as deformations of the shape of a stored prototype. Results on real world traac scenes are presented and some open problems are outlined.
Object Recognition Using Tactile Measurements: Kernel Sparse Coding Methods

Dexterous robots have emerged in the last decade in response to the need for fine-motor-control assistance in applications as diverse as surgery, undersea welding, and mechanical manipulation in space. Crucial to the fine operation and contact environmental perception are tactile sensors that are fixed on the robotic fingertips. These can be used to distinguish material texture, roughness, spatial features, compliance, and friction. In this paper, we regard the investigated tactile data as time sequences, of which dissimilarity can be evaluated by the popular dynamic time warping method. A kernel sparse coding method is therefore developed to address the tactile data representation and classification problem. However, the naive use of sparse coding neglects the intrinsic relation between individual fingers, which simultaneously contact the object. To tackle this problem, we develop a joint kernel sparse coding model to solve the multifinger tactile sequence classification problem. In this model, the intrinsic relations between fingers are explicitly taken into account using the joint sparse coding, which encourages all of the coding vectors to share the same sparsity support pattern. The experimental results show that the joint sparse coding achieves better performance than conventional sparse coding.
Fusion of depth and vision information for human detection

In this paper, we present a novel approach to detect human base on the fusion of depth and vision information. Instead of conventional human detection method which is mostly done in color image, our approach not only detects human and annotates their position but also extracts whole body pixels from depth image. Our approach has huge advantage over traditional vision image based method, since depth feature is robust against illumination changes, discriminative to background clutter and stable to human pose variation. This system contains a model based human head detector, which detects head through fusion of depth and color haar feature based detector. Then we propose a graph based algorithm to extract whole human body from depth image based head pixels. The experimental results on a large real-world dataset demonstrate that our method has an outstanding performance.
A Comparison of Real and Virtual 3D Construction Tools with Novice Users

In this paper, we present the results of a comparative evaluation of a "virtual Lego" system against real Lego bricks with novice users. The virtual Lego system was designed to emulate the behavior of real Lego bricks as close as possible, while still allowing for efficient operation. We implemented two different user interfaces for the virtual Lego system, namely controlling it with a 2D mouse, based on an efficient mapping from 2D to 3D, as well as with a 3D haptics device, which provides haptic feedback to the user. The results of our study show that real Lego is still significantly faster for first-time users (i.e. users with minimal training). A more surprising result is that the 2D mouse condition and the 3D haptics condition did not differ significantly even though the 3D haptics condition provides much richer feedback. We discuss the results and speculate about the
Beware: Recruitment of Muscle Activity by the EEG-Neurofeedback Trainings of High Frequencies

EEG-neurofeedback (NFB) became a very popular method aimed at improving cognitive and behavioral performance. However, the EMG frequency spectrum overlies the higher EEG oscillations and the NFB trainings focusing on these frequencies is hindered by the problem of EMG load in the information fed back to the subjects. In such a complex signal, it is highly probable that the most controllable component will form the basis for operant conditioning. This might cause different effects in the case of various training protocols and therefore needs to be carefully assessed before designing training protocols and algorithms. In the current experiment a group of healthy adults (n = 14) was trained by professional trainers to up-regulate their beta1 (15–22 Hz) band for eight sessions. The control group (n = 18) underwent the same training regime but without rewards for increasing beta. In half of the participants trained to up-regulate beta1 band (n = 7) a systematic increase in tonic EMG activity was identified offline, implying that muscle activity became a foundation for reinforcement in the trainings. The remaining participants did not present any specific increase of the trained beta1 band amplitude. The training was perceived effective by both trainers and the trainees in all groups. These results indicate the necessity of proper control of muscle activity as a requirement for the genuine EEG-NFB training, especially in protocols that do not aim at the participants’ relaxation. The specificity of the information fed back to the participants should be of highest interest to all therapists and researchers, as it might irreversibly alter the results of the training.
When Change Matters: An Analysis of Survey Interaction in Dependent Interviewing on the British Household Panel Study

The authors examine how questionnaire structure affects survey interaction in the context of dependent interviewing (DI). DI is widely used in panel surveys to reduce observed spurious change in respondent circumstances. Although a growing literature generally finds beneficial measurement properties, little is known about how DI functions in interviews. The authors systematically observed survey interaction using behavior coding and analyzed an application of DI to obtain respondent employment characteristics. The authors found respondents indicated change in circumstances through a number of verbal machinations, including mismatch answers and explanations. Assessing whether these behaviors influenced subsequent question administration, the authors found qualitative evidence that the information disclosed when negating a DI question leads to subsequent interviewing errors. Quantitative analyses supported this evidence, suggesting that standardized interviewing deteriorates as respondents struggle to identify change in their circumstances. This analysis suggests that the reliability of detail about changed circumstances may not be improved using DI.
Online make-to-order joint replenishment model: primal dual competitive algorithms

In this paper, we study an online make-to-order variant of the classical joint replenishment problem (JRP) that has been studied extensively over the years and plays a fundamental role in broader planning issues, such as the management of supply chains. In contrast to the traditional approaches of the stochastic inventory theory, we study the problem using competitive analysis against a worst-case adversary. Our main result is a 3-competitive deterministic algorithm for the online version of the JRP. We also prove a lower bound of approximately 2.64 on the competitiveness of any deterministic online algorithm for the problem. Our algorithm is based on a novel primal-dual approach using a new linear programming relaxation of the offline JRP model. The primal-dual approach that we propose departs from previous primal-dual and online algorithms in rather significant ways. We believe that this approach can extend the range of problems to which online and primal-dual algorithms can be applied and analyzed.
Forms of address in post-revolutionary Iranian Persian: A sociolinguistic analysis

ABSTRACT The sudden shift from power to solidarity in Iran in the face of the sociopolitical upheaval in the country has yielded some interesting changes in the forms of address in Persian. In general, since the Islamic Revolution of 1979, forms of address in Persian have undergone a sociolinguistic simplification. In post-revolutionary Iran plain speech and forms of address marking solidarity have gained popularity, whereas asymmetrical forms reflecting the complex social class structure of pre-revolutionary Iran have gradually declined. This article gives a sociolinguistic account of the forms of address in present-day Iranian Persian and documents the impact of the revolution on this aspect of the Persian language. (Sociopolitical change, language change, forms of address, politeness systems, Iranian Persian)
Induction of acute graft‐versus‐host disease by T cells that do not respond to in vitro alloantigen stimulation

The mixed lymphocyte reaction (MLR) has been used extensively to measure alloreactive T cells. In clinical practice, a negative MLR of recipient T cells responding to donor cells does not necessarily mean that a donor‐specific tolerance has been established. This discrepancy indicates that the presently used methods fail to demonstrate the full repertoire of alloreactive T cells. This could be the result of the fact that some alloreactive T cells do not respond in vitro but will mount a response towards alloantigens in vivo, or that some alloreactive T cells do not respond during the MLR but will respond later if the alloantigen stimulation remains. We therefore examined the non‐proliferating T‐cell population in a mouse primary alloreactive response. Spleen and lymph node cells derived from C57BL/6J (H‐2b) mice were stained with carboxy‐fluorescein diacetate succinimidyl ester and injected intravenously into C.B‐17 severe combined immunodeficient (SCID) mice (H‐2d). The donor cells were recovered 5 d after the injection. The non‐proliferating T cells were sorted and were non‐reactive to alloantigen stimulation in vitro. Nevertheless, these non‐proliferating T cells could proliferate and cause acute graft‐versus‐host disease after being adoptively transferred to secondary recipients of SCID mice. These results suggest that there exists an alloreactive T‐cell population that does not respond to in vitro alloantigen stimulation but can mount a delayed alloresponse in vivo.
Academic Achievement in Large Lectures: Analyzing the Effects of Attendance Rates and Class Motivation on Economics Exam Grades

Research on the impact of class size on student motivation and performance can be important for university instructors. This paper investigates the influence of class size on exam performance and attendance rates. Empirical analyses show that students in the larger section of microeconomic principles taught by the same instructor did not perform significantly worse on exams with the exception of the cumulative final. This is attributed to a significant drop in attendance at the end of the semester. Blinder-Oaxaca style decompositions of the residual effects reveal that the negative effects of large lectures can be greater for lower achieving students.
NTUplace3: An Analytical Placer for Large-Scale Mixed-Size Designs With Preplaced Blocks and Density Constraints

In addition to wirelength, modern placers need to consider various constraints such as preplaced blocks and density. We propose a high-quality analytical placement algorithm considering wirelength, preplaced blocks, and density based on the log-sum-exp wirelength model proposed by Naylor and the multilevel framework. To handle preplaced blocks, we use a two-stage smoothing technique, i.e., Gaussian smoothing followed by level smoothing, to facilitate block spreading during global placement (GP). The density is controlled by white-space reallocation using partitioning and cut-line shifting during GP and cell sliding during detailed placement. We further use the conjugate gradient method with dynamic step-size control to speed up the GP and macro shifting to find better macro positions. Experimental results show that our placer obtains very high-quality results.