Miracle-dz/UniFAR-FTUs huggingface dataset:
PRACTICAL, STEREOSELECTIVE SYNTHESIS OF PALINAVIR, A POTENT HIV PROTEASE INHIBITOR

Palinavir is a potent peptidomimetic-based HIV protease inhibitor. We have developed a highly convergent and stereoselective synthesis which is amenable to the preparation of multikilogram quantities of this compound. The synthetic sequence proceeds in 24 distinct chemical steps (with several integrated, multistep operations) from commercially available starting materials. No chromatographies are required throughout the process, and the final product is purified by crystallization of its dihydrochloride salt to >99% homogeneity.
Blind decoder‐assisted interference reduction for coded DS‐CDMA systems

We propose an iterative blind interference reduction strategy for short‐burst coded DS‐CDMA systems. The blind strategy works by creating a set of ‘training sequences’ in the receiver that are used as input to an interference reduction algorithm whose task is to produce a corresponding set of equalizers that attempt to recover the desired signal. To maintain a reasonable complexity level we develop a semi‐blind interference reduction algorithm that is capable of equalizing the received signal with a relatively small training sequence length (thus maintaining a small training sequence set). The objective then becomes to determine which equalizer from the generated set gives the best performance (smallest bit error). It is demonstrated that the success of this scheme depends greatly on the ability to find an appropriate criterion for picking the best equalizer. Of the tested criteria, one based on feedback from the decoder (essentially using trellis information) is shown to achieve nearly optimal performance. Copyright © 2005 John Wiley & Sons, Ltd.
WORDS AND MODELS (Systems Approach to Linguistics)

What is the unit of semantic analysis? A word by itself does not mean a great deal. It means more when associated with other words, i.e. when used in a specific context. Together they can be regarded as constituting a model. When building a model one must choose the appropriate variables for them. Words suitable for one context or model are not necessarily appropriate for another. Thus the words used in ordinary language are not always appropriate for the more objective models built by scientists; they were developed for a more subjective model that has come down to us as part of our cultural heritage. The fact that contexts are often implicit rather than explicit makes it possible to use words in such a way as to convey to them their meaning when used in a totally different context. This is the basis of many ambiguities and verbal tricks. It is facilitated by our tendency towards “nominal realism”, i.e. towards regarding words as real things as opposed to units of a model designed to represent real situations for specific purposes. It is only by regarding them in the latter way that such ambiguities can be avoided.
Evaluating the motivational impact of CALL systems: current practices and future directions

A major aim of computer-assisted language learning (CALL) is to create computer environments that facilitate students’ second language (L2) acquisition. To achieve this aim, CALL employs technological innovations to create novel types of language practice. Evaluations of the new practice types serve the important role of distinguishing effective practice environments from less effective environments, while simultaneously informing educational practices and second language acquisition (SLA) theory. Accordingly, evaluations of CALL systems necessarily deal with multiple criteria. Most researchers would probably agree that motivation is an important criterion in CALL evaluations: a system can provide sufficient L2 input and opportunities for L2 output, yet fail to be pedagogically effective if learners are unwilling to participate. Furthermore, knowledge of the motivational impact of practice can provide valuable context linking individual language learners, practice effort and learning outcomes. From the perspective of recent theoretical developments in L2 motivation theory, this paper surveys a representative sample of CALL system evaluations that include motivational impact. Our analysis suggests not only that CALL needs to do more to align its treatment of motivation with recent L2 motivation theories, but also that it is well positioned to do so. We find that (1) few CALL studies treat motivation as it relates to practice as a dynamic variable, (2) behavioural practice logs are underexploited and (3) very few evaluations take into account learners’ individual interests and goals. Drawing on these and other findings, we suggest four new directions for developing the motivation dimension in CALL evaluations.
A photoacoustic study of thermal conductivity annealing curves for a silicon substrate implanted with Si ions

Recrystallization by annealing is part of the processing of ion-implanted semiconductor substrates because the ion implantation generates defects within the crystal. Furthermore, it was reported by Zammit's group [1] that the thermal conductivity of such substrates deteriorates remarkably after implantation. In light of these facts, we have analyzed the photoacoustic signal from a thin layer fabricated on a substrate by ion implantation, and have used the data to measure the thermal conductivity of the implanted layer in the depth direction. Our thermal conductivity measurements can be used to monitor the recrystallization of the implanted layer in the course of the annealing. We fabricated the samples by implanting Si ions into a region on top of a Si substrate at room temperature. The ions used in the implantation had an energy of 150 keV. The ion implantation doses were in the range from 1 × 1012 to 1 × 1016ions/cm2. The thickness of the ion-implanted layer was measured by TEM to be 100 nm. By detecting the difference between the phases of photoacoustic signals coming from the ion-implanted region and from the unimplanted region, we could extract the thermal conductivity of the implanted layer from the frequency modulation characteristics of this phase difference. We used this technique to measure annealing curves for the thermal conductivity. Our results indicated that in the course of restoring the crystallinity, the thermal conductivity begins to increase at about 500 °C, and by 750 °C it has recovered to almost the same value as prior to the implantation. Our measured annealing curves for the thermal conductivity correlated strongly with measurement data on the annealing curves of the ESR signal intensity from the same samples. This result makes it clear that measurement of the thermal conductivity by the photoacoustic method is an efficient way to monitor the defect density in implanted layers. © 2003 Wiley Periodicals, Inc. Electron Comm Jpn Pt 2, 86(10): 1–8, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjb.10034
Towards a modular timing analysis of real-time software systems

In this paper we present a timing Petri net extension based on fuzzy set theory to perform timing analysis of software systems. A modular timing analysis is defined by using a structure tool named G-Net. Finally, we make some considerations about the integration of this timing extension and G-CPN, an object-based Petri net.
Techniques for analyzing distributed computations

Inherent non-determinism in distributed programs and presence of multiple threads of control makes it difficult to write correct distributed software. Not surprisingly, distributed systems are particularly vulnerable to software faults. To build a distributed system capable of tolerating software faults, two important problems need to be addressed: fault detection and fault recovery. The fault detection problem requires finding a (consistent) global state of the computation that satisfies certain predicate (e.g., violation of mutual exclusion). To prevent a fault from causing any serious damage such as corrupting stable storage, it is essential that it be detected in a timely manner. However, we prove that detecting a predicate in 2-CNF, even when no two clauses contain variables from the same process, is an NP-complete problem. We develop a technique, based on computation slicing, to reduce the size of the computation and thus the number of global states to be examined for detecting a predicate. Slicing can be used to throw away the extraneous global states of the computation in an efficient manner, and focus on only those that are currently relevant for our purpose. To detect a fault, therefore, rather than searching the state-space of the computation, it is much more efficient to search the state-space of the slice. We identify several useful classes of predicates for which the slice can be computed efficiently. Our experimental results indicate that slicing can lead to an exponential reduction over existing techniques both in terms of time as well as space for fault detection. To recover from faults, we consider rollback recovery approach, which involves restoring the system to a previous state and then re-executing. We focus on rollback recovery using controlled re-execution , which is useful and effective for tolerating synchronization faults. Unlike other approaches which depend on chance and do not ensure that the re-execution is fault-free, the controlled re-execution method avoids synchronization faults during re-execution in a deterministic fashion. Specifically, it selectively adds synchronization dependencies during re-execution to ensure that the previously detected synchronization faults do not occur again. We provide efficient algorithms to solve the problem for two important classes of synchronization faults.
The application and programming of an image store for the SEM

The programming and use of a purposebuilt digital image store constructed for a SEM is described. The system is based on purpose designed circuitry controlled by a Motorola MC68000 and is capable of noise-averaging using multiple frames, colour look-up tables, high-resolution photographic recording, image processing using matrix functions, some image quantification, and x-ray spectrum acquisition. The way in which the FORTH language provided an efficient means of controlling the SEM is described and examples of each type of operation are presented.
Developing a values scorecard

Purpose – The purpose of this paper is to define a framework and categorization of the types of evidence required to prove the value and impact of libraries. It questions the limitations of current measurement for library value, and hence for contributing to academic and research library planning and advocacy. The paper describes and draws on some of the recent progress in value and impact measurement over the last five years. Scenario planning exercises conducted by both ARL and SCONUL are used to analyse the likely future value proposition emerging for libraries. A values‐based value scorecard is proposed, which would demonstrate the transcendent value of academic and research libraries now and in the future.Design/methodology/approach – The paper examines research literature from library and general management texts to propose a framework for measuring the value and impact of libraries.Findings – The paper concludes that a value scorecard can be used alongside the balanced scorecard to add a more value.
Learning algorithm for selection of an autoregressive model for multi-step ahead forecast

The paper addresses the problem of learning an order of an autoregressive (AR) model with multi step ahead prediction properties and describes a computational learning algorithm based on a new selection criterion ("pattern residuals' interdependence measure estimator"-PRIME). The PRIME criterion for the selection of an AR model with sufficient predictive power measures interdependence among residuals obtained from a given training set (an observed time series), a time series modelled by an autoregressive moving average (ARMA) process, and a corresponding deterministic pattern extracted from the original data by a smoothing filter. As a measure of the residuals' interdependence, a mean expected log likelihood function of a correlation coefficient between the residuals is defined. The paper presents the results of Monte Carlo simulation which generate an empirical distribution of the proposed estimator and provide evidence of appropriate identification of data. It illustrates also the results obtained by multi step ahead forecast for actual data. The comparison between models selected by the proposed criterion and well known criteria provides favourable evidence for the strength of the PRIME criterion.
Testing observer's ability to detect if an image was compressed shows large observer variability

In medical imaging, many methods of irreversible compression have been studied. However, none of them has been adopted as a standard, probably because of the lack of agreement regarding what is meant by 'acceptable' for an irreversible compression scheme. Among the numerous evaluation studies which have been performed, few only involved a psychovisual approach, and it is unsafe to draw definite conclusion from the reported result. The purpose of this work was to determine the importance of intra and inter observer variability in the psychovisual evaluation of irreversible compression methods for a very specific evaluation protocol involving digitized images of breast parenchyma and imags simulating breast parenchyma using a fractal model. the compression methods to be evaluated were JPEG and Embedded Zerotree Wavelet. Six radiologists had to determine whether each image had been compressed and decompressed. ROC analysis was performed to characterize the performance in compression detection. The large intra and inter observer variability observed in the detection of compression underlines the difficulty of determining an 'acceptable' scheme of irreversible compression in medical imaging in general, and bring the approval of irreversible compression schemes into question.
Key derivation for network management applications

This article describes a key derivation method that greatly simplifies key distribution issues for network management (NM) applications and may in some cases relieve NM stations from the need to store any keys at all. This method is easily generalized for network protocols and applications where several "masters" need to talk to a large number of "slaves". It shows a way to cut down on the amount of keys stored with little impact on security. This is especially important when human users are involved.
Simulating DNA Chip Design Using All-Electronic Graphene-Based Substrates

In this paper, we present a theoretical investigation of an all-electronic biochip based on graphene to detect DNA including a full dynamical treatment for the environment. Our proposed device design is based on the changes in the electronic transport properties of graphene interacting with DNA strands under the effect of the solvent. To investigate these systems, we applied a hybrid methodology, combining quantum and classical mechanics (QM/MM) coupled to non-equilibrium Green’s functions, allowing for the calculations of electronic transport. Our results show that the proposed device has high sensitivity towards the presence of DNA, and, combined with the presence of a specific DNA probe in the form of a single-strand, it presents good selectivity towards specific nucleotide sequences.
Analysis of Output Capacitor Voltage Ripple in Multi-Phase Transformer-Linked Boost Chopper Circuit

The multi-phase method is effective in downsizing the power converter system. Because of the multi-phase boost chopper circuit, the output capacitor and the cooling system can be downsized in the case of the boost chopper circuit. Furthermore, the volume of the inductor can be reduced using the transformer-linked method. However, there is little comparative data available on the output capacitor in the boost chopper of the conventional multi-phase method and the transformer-linked method. The purpose of my work was to examine the characteristics of the output capacitor in both the multi-phase method and the transformer-linked method. To evaluate the performance of the downsized capacitor, a voltage ripple analysis has been successfully employed as the index of the volume of the output capacitor. From the experimental results, it was found that there is little difference between the multi-phase method and the transformer-linked method in regard to the voltage ripple of the output capacitor. The experimental results indicate that the performance of the downsized capacitor is the same for the multi-phase method and the transformer-linked method.
Low-coherence interferometry with polynomial interpolation on Compute Unified Device Architecture-enabled graphics processing units

Abstract. An algorithm for interpolation of central fringe position in low-coherence interferometry measurements is presented. The algorithm is based on a polynomial curve fitting. Fast calculation of interpolation is possible due to the use of an NVIDIA Compute Unified Device Architecture (CUDA) technology, which allows independent analysis of different points of a high-resolution detector matrix on separate cores of a graphics processing unit (GPU). The dependency of the method’s accuracy on the spectral width of the light source is checked. The computation times on a GPU are compared with those achieved with a multicore central processing unit, showing nearly 30 times faster calculations when using CUDA technology. The algorithm accuracy is tested by measuring a flat glass surface with two different cameras—an ordinary CCD camera and a cooled EMCCD camera. Finally, the algorithm is applied to measurements of a populated optical fiber connector array prototyped using deep proton writing technology.
The quality and reputation of open, distance and e-learning: what are the challenges?

Distance education institutions, students and staff have often had to overcome negative perceptions about the overall quality of their programmes and qualifications. In this paper, we identify four of the major challenges cited as undermining the credibility and effectiveness of open, distance and e-learning (ODeL): the quality of teaching, learning and quality assurance processes; outcomes; access; and the perceptions of students, staff and employers. We conclude with reflections on current and future developments in ODeL, including the impact of massive open online courses (MOOCs): how far do they have the potential to address the challenges identified? We argue that some of these have been, or can be, resolved in many contexts, and are now the same as those faced by all teaching and learning programmes. We should shift the main focus of distance teaching and learning programmes from inputs such as media adopted, to outcomes, in terms of students successfully achieving their intended goals in education, employment and future livelihoods. This will impact on employers’ and others’ perceptions of ODeL. Examples are drawn from all sectors and are, therefore, necessarily selective, and, unless specified, are relevant to all modes of ODeL.
Electrically Programmable Terahertz Diatomic Metamolecules for Chiral Optical Control

Optical chirality is central to many industrial photonic technologies including enantiomer identification, ellipsometry-based tomography, and spin multiplexing in optical communications. However, a substantial chiral response requires a three-dimensional constituent, thereby making the morphology highly complex to realize structural reconfiguration. Moreover, an active reconfiguration demands intense dosage of external stimuli that pose a major limitation for on-chip integration. Here, we report a low bias, electrically programmable synthetic chiral paradigm with a remarkable reconfiguration among levorotatory, dextrorotatory, achiral, and racemic conformations. The switchable optical activity induced by the chiral conformations enables a transmission-type duplex spatial light modulator for terahertz single pixel imaging. The prototype delivers a new strategy towards reconfigurable stereoselective photonic applications and opens up avenues for on-chip programmable chiral devices with tremendous applications in biology, medicine, chemistry, and photonics.
Fine gradings of complex simple Lie algebras and Finite Root Systems

A $G$-grading on a complex semisimple Lie algebra $L$, where $G$ is a finite abelian group, is called quasi-good if each homogeneous component is 1-dimensional and 0 is not in the support of the grading. Analogous to classical root systems, we define a finite root system $R$ to be some subset of a finite symplectic abelian group satisfying certain axioms. There always corresponds to $R$ a semisimple Lie algebra $L(R)$ together with a quasi-good grading on it. Thus one can construct nice basis of $L(R)$ by means of finite root systems. We classify finite maximal abelian subgroups $T$ in $\Aut(L)$ for complex simple Lie algebras $L$ such that the grading induced by the action of $T$ on $L$ is quasi-good, and show that the set of roots of $T$ in $L$ is always a finite root system. There are five series of such finite maximal abelian subgroups, which occur only if $L$ is a classical simple Lie algebra.
Laser radar in robotics

In this paper we describe the basic operating principles of laser radar sensors and the typical algorithms used to process laser radar imagery for robotic applications. We review 12 laser radar sensors to illustrate the variety of systems that have been applied to robotic applications wherein information extracted from the laser radar data is used to automatically control a mechanism or process. Next we describe selected robotic applications in seven areas: autonomous vehicle navigation, walking machine foot placement, automated service vehicles, manufacturing and inspection, automotive, military and agriculture. We conclude with a discussion of the status of laser radar technology and suggest trends we see in the application of laser radar sensors to robotics. Many new applications are expected as the maturity level progresses and system costs are reduced.
Exemplar-based human interaction recognition: features and key pose sequence model

Due to intra-class variation, camera jitter, background clutter, etc, human activity recognition is a challenging task in computer vision. We propose an exemplar-based key pose sequence model for human interaction recognition. In our model, an activity is modelled with a sequence of key poses, important atomic-level actions performed by the actors. We employ a strict temporal ordering of the key poses for each actor, an exemplar representation is used to model the variability in the instantiation of key poses. To utilize interaction information, spatial arrangements between the actors are included in the model. Quantitative results that form a new state-of-the-art on the benchmark UT-Interaction dataset are presented. Results on a subset of the TRECVID dataset are also promising.