Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust H∞ autopilot design for agile missile with time-varying parameters

This paper considers two autopilot designs using H∞ loop shaping for an agile missile that experiences high angle of attack, highly nonlinear and rapidly changing dynamics, and aerodynamic variation after launch. The main autopilot design is started with two H∞ control designs intended to cover the low-speed and high-speed regions of the flight envelope. Then, the two control designs are combined (via Mach variation) to construct a global controller that covers the entire flight envelope. The proposed autopilots have a simple structure and require no time-consuming gain scheduling for many flight conditions, while providing satisfactory tracking and robustness over the entire flight envelope. The performance of the designed autopilots is checked via a comparison study and a challenging intercept scenario. These performance test results clearly demonstrate the merit of the proposed designs.
Classification of taiwanese tones based on pitch and energy movements

This paper addresses the difficulties associated with automatically distinguishing the seven Taiwanese tones. The tone recogniser is an essential component of any automatic speech recognition system customised for tone languages such as Taiwanese. We show that it is difficult to distinguish between the Taiwanese tones simply employing the fundamental frequency contours and that the task is simplified by employing energy contour features besides the fundamental frequency features. To allow energy to be accommodated into the classification model an energy-contour feature extraction approach is presented. The proposed approach is inspired by the ADSR model employed in musical instrument synthesis where the envelopes of complex sounds are modeled employing only a few parameters. Our experiments demonstrate that the inclusion of energy into the recognition model allows the seven Taiwanese tones to be discriminated successfully. The paper also presents acoustical measurements of the fundamental frequency and energy features described.
A framework for efficient cache resizing

We present a novel framework to dynamically reconfigure on-chip memory resources according to the changing behavior of the running applications. Our framework enables smooth scaling (i.e., resizing) of the on-chip caches targeting both performance and power efficiency. In contrast to previous approaches, the resizing decisions in our framework are not tainted by transient events (e.g., misses) that are due to downsizing avoiding at the same time swinging the cache size due to trial-and-error resizing decisions. This minimizes both execution time penalty induced by resizing as well as the effective cache size. Furthermore, an inherent property of our approach is that the actual invalidation of the cache blocks and the corresponding write-backs of the dirty blocks are asynchronous to resizing decisions, ensuring a smooth transition from one size to another. This makes it possible to apply our framework even on write-back caches. The proposed mechanism is simple to implement requiring minimal additional hardware. Using cycle-accurate simulations, we evaluate our proposal against previously proposed techniques. In all cases, our experimental results show significant benefits in both power and performance.
Exact gradient updates in time independent of output size for the spherical loss family

An important class of problems involves training deep neural networks with sparse prediction targets of very high dimension D. These occur naturally in e.g. neural language models or the learning of word-embeddings, often posed as predicting the probability of next words among a vocabulary of size D (e.g. 200,000). Computing the equally large, but typically non-sparse D-dimensional output vector from a last hidden layer of reasonable dimension d (e.g. 500) incurs a prohibitive O(Dd) computational cost for each example, as does updating the $D \times d$ output weight matrix and computing the gradient needed for backpropagation to previous layers. While efficient handling of large sparse network inputs is trivial, the case of large sparse targets is not, and has thus so far been sidestepped with approximate alternatives such as hierarchical softmax or sampling-based approximations during training. In this work we develop an original algorithmic approach which, for a family of loss functions that includes squared error and spherical softmax, can compute the exact loss, gradient update for the output weights, and gradient for backpropagation, all in $O(d^{2})$ per example instead of $O(Dd)$, remarkably without ever computing the D-dimensional output. The proposed algorithm yields a speedup of up to $D/4d$ i.e. two orders of magnitude for typical sizes, for that critical part of the computations that often dominates the training time in this kind of network architecture.
PSLDoc: Protein subcellular localization prediction based on gapped‐dipeptides and probabilistic latent semantic analysis

Prediction of protein subcellular localization (PSL) is important for genome annotation, protein function prediction, and drug discovery. Many computational approaches for PSL prediction based on protein sequences have been proposed in recent years for Gram‐negative bacteria. We present PSLDoc, a method based on gapped‐dipeptides and probabilistic latent semantic analysis (PLSA) to solve this problem. A protein is considered as a term string composed by gapped‐dipeptides, which are defined as any two residues separated by one or more positions. The weighting scheme of gapped‐dipeptides is calculated according to a position specific score matrix, which includes sequence evolutionary information. Then, PLSA is applied for feature reduction, and reduced vectors are input to five one‐versus‐rest support vector machine classifiers. The localization site with the highest probability is assigned as the final prediction. It has been reported that there is a strong correlation between sequence homology and subcellular localization (Nair and Rost, Protein Sci 2002;11:2836–2847; Yu et al., Proteins 2006;64:643–651). To properly evaluate the performance of PSLDoc, a target protein can be classified into low‐ or high‐homology data sets. PSLDoc's overall accuracy of low‐ and high‐homology data sets reaches 86.84% and 98.21%, respectively, and it compares favorably with that of CELLO II (Yu et al., Proteins 2006;64:643–651). In addition, we set a confidence threshold to achieve a high precision at specified levels of recall rates. When the confidence threshold is set at 0.7, PSLDoc achieves 97.89% in precision which is considerably better than that of PSORTb v.2. 0 (Gardy et al., Bioinformatics 2005;21:617–623). Our approach demonstrates that the specific feature representation for proteins can be successfully applied to the prediction of protein subcellular localization and improves prediction accuracy. Besides, because of the generality of the representation, our method can be extended to eukaryotic proteomes in the future. The web server of PSLDoc is publicly available at http://bio‐cluster.iis. sinica.edu. tw/∼bioapp/PSLDoc/. Proteins 2008. © 2008 Wiley‐Liss, Inc.
Modeling of Microvascular Shape Memory Composites

This work investigates the adaptive and morphing properties of SMCs based on a shape memory polymer (SMP) and a microvascular arrangement of shape memory alloys (SMAs). Here, the microvascular SMA phase has been subjected to a two-way shape memory effect (SME) process, in order to fully control the actuation properties of the SMC. It has been observed that the two-way trained SMA successfully induces a morphing performance on the SMC during a fluid heating-cooling cycle. The initial results suggest that the actuation behavior of the SMC strongly depends on the microvascular fluid heating rate as well as on the temperature difference between the glass transition temperature of the SMP and the activation temperature of the SMA. Analytical and Finite Element Method (FEM) analysis on the microvascular SMC has also been performed. The results suggest that the FEA analysis offers a better prediction of the thermo-mechanical behavior of the SMC. It has been observed that whilst the FEA successfully predicts the thermal profile of the SMC, the mechanical modeling seems to require a degree of amendment. Here, the FEA has predicted a deflection 20% higher than those experimentally recorded. Although a refinement is needed on the mechanical modeling of the FEA analysis, the current FEA work certainly provides the elementary design parameters for future optimizations of morphing structures based on
Simulation of self-assembly and mechanics of transiently crosslinked, semiflexible biopolymer networks

Finite element simulations of biopolymer networks can reveal key properties of biological cells. Having developed a network model of polymer fibers and crosslinkers, accounting for their Brownian dynamics and interaction, complex networks and their mechanics have been investigated. The results on network self-assembly help elucidate the cell's control of its mechanics, which strongly depends on network architecture, and have led to simulations of composite bundle networks revealing new, universal rheological properties. Their significance transcends the biological setting.
Nanocrystalline Silicon for Optoelectronic Applications

Light emission in silicon has been intensively investigated since the 1950s when crystalline silicon (c-Si) was recognized as the dominant material in microelectronics. Silicon is an indirect-bandgap semiconductor and momentum conservation requires phonon assistance in radiative electron-hole recombination (Figure 1a, top left). Because phonons carry a momentum and an energy, the typical signature of phonon-assisted recombination is several peaks in the photoluminescence (PL) spectra at low temperature. These PL peaks are called “phonon replicas. ” High-purity c-Si PL is caused by free-exciton self-annihilation with the exciton binding energy of ~11 meV. The TO-phonon contribution in conservation processes is most significant, and the main PL peak (~1.1 eV) is shifted from the bandgap value (~1.17 eV) by ~70 meV—that is, the exciton binding energy plus TO-phonon energy (Figure 1a).
A bayesian recurrent neural network for unsupervised pattern recognition in large incomplete data sets

A recurrent neural network, modified to handle highly incomplete training data is described. Unsupervised pattern recognition is demonstrated in the WHO database of adverse drug reactions. Comparison is made to a well established method, AutoClass, and the performances of both methods is investigated on simulated data. The neural network method performs comparably to AutoClass in simulated data, and better than AutoClass in real world data. With its better scaling properties, the neural network is a promising tool for unsupervised pattern recognition in huge databases of incomplete observations.
Xylan removal by xylanase for the production of dissolving pulp from bamboo

II Preface. III List of Tables . VII List of Figures . VIII List of Glossary and Nomenclature . XI Acknowledgements . XIII Chapter 1: Introduction . 1 Chapter 2: Literature review . 5 2.1 Molecular structure of xylan in bamboo pulp . 5 2.2 Molecular structure of xylanase . 7 2.3 Mechanism of xylanase action on xylan . 8 2.4 Mechanism of xylanase action on xylan in bamboo pulp . 10 2.5 Approaches to improve xylanase treatment efficiency. 12 2.6 Scope, hypothesises and objectives . 15 2.7 Thesis outline . 16 Chapter 3 Selection of xylanase and optimizing the treatment conditions . 18
Determinants of sugar‐sweetened beverage consumption in young children: a systematic review

Sugar‐sweetened beverage (SSB) consumption is associated with adverse health outcomes. Improved understanding of the determinants will inform effective interventions to reduce SSB consumption. A total of 46,876 papers were identified through searching eight electronic databases. Evidence from intervention (n = 13), prospective (n = 6) and cross‐sectional (n = 25) studies on correlates/determinants of SSB consumption was quality assessed and synthesized. Twelve correlates/determinants were associated with higher SSB consumption (child's preference for SSBs, TV viewing/screen time and snack consumption; parents' lower socioeconomic status, lower age, SSB consumption, formula milk feeding, early introduction of solids, using food as rewards, parental‐perceived barriers, attending out‐of‐home care and living near a fast food/convenience store). Five correlates/determinants were associated with lower SSB consumption (parental positive modelling, parents' married/co‐habiting, school nutrition policy, staff skills and supermarket nearby). There was equivocal evidence for child's age and knowledge, parental knowledge, skills, rules/restrictions and home SSB availability. Eight intervention studies targeted multi‐level (child, parents, childcare/preschool setting) determinants; four were effective. Four intervention studies targeted parental determinants; two were effective. One (effective) intervention targeted the preschool environment. There is consistent evidence to support potentially modifiable correlates/determinants of SSB consumption in young children acting at parental (modelling), child (TV viewing) and environmental (school policy) levels.
Selective Distribution of Retinal Input to Mouse SCN Revealed in Analysis of Sagittal Sections

The suprachiasmatic nucleus (SCN) is the locus of the master circadian clock, setting the daily rhythms in physiology and behavior and synchronizing these responses to the local environment. The most important of these phase-setting cues derive from the light-dark cycle and reach the SCN directly via the retinohypothalamic tract (RHT). The SCN contains anatomically and functionally heterogeneous populations of cells. Understanding how these neurons access information about the photic environment so as to set the phase of daily oscillation requires knowledge of SCN innervation by the RHT. While retinal innervation of the SCN has long been a topic of interest, the information is incomplete. In some instances, studies have focused on the caudal aspect of the nucleus, which contains the core region. In other instances, subregions of the nucleus have been delineated based on projections of where specific peptidergic cell types lie, rather than based on double or triple immunochemical staining of distinct populations of cells. Here, we examine the full extent of the mouse SCN using cholera toxin β (CTβ) as a tracer to analyze RHT innervation in triple-labeled sagittal sections. Using specific peptidergic markers to identify clusters of SCN cells, we find 3 distinct patterns. First is an area of dense RHT innervation to the core region, delineated by gastrin-releasing peptide (GRP) and vasoactive intestinal peptide (VIP) immunoreactive cells. Second is an area of moderate RHT fiber clusters, bearing arginine-vasopressin (AVP)–positive cells that lie close to the core. Finally, the outermost, shell, and rostral AVP-containing regions of the SCN have few to no detectable retinal fibers. These results point to a diversity of inputs to individual SCN cell populations and suggest variation in the responses that underlie photic phase resetting.
The role of aldosterone in renal physiology.

Aldosterone is a highly important mineralocorticoid produced in the zona glomerulosa of the adrenal cortex in response to angiotensin II, potassium ions, and adrenocorticotropic hormone. Its most important physiological functions concern maintenance of sodium balance, potassium homeostasis, and excretion of hydrogen ions. Although the distal tubule of the kidney appears to be its principal site of action, its effects on other membranes and glandular tissues may induce secondary effects on other portions of the nephron. Its precise mechanism of action is the result of its reaction with highly specific protein receptors located only in responsive tissues. These proteins, in complex with aldosterone, stimulate formation of DNA-dependent RNA that in turn leads to synthesis of new proteins. The latter, by an unknown mechanism, are responsible for the definitive action of aldosterone.
The ‘magic square’: A roadmap towards emotional business intelligence

Emotions are known to be an important driver in human behaviour and decision-making. In the business world, there is a growing belief that emotions are not an obstacle but rather an enabler for a successful business. Business intelligence (by providing analytical processing and convenient presentation of a business data) traditionally supports rational decision-making. However, opposite to former opinion that all decisions should be ‘cleansed’ of emotions, there are more and more indicators of the need for solutions supporting also emotional decision-making. The domain of emotional business intelligence, suggested in this paper, concerns emotional and emotion-aware decisions, intuition, innovation and creativity. We present the new domain as an integration of popular, emerging and evolving domains of emotional business, emotional intelligence and business intelligence. We argue the objectives of emotional business intelligence and discuss the needed technological basis, models and methods for its roadmapping.
Behavioural evidence for vestibular stimulation as a treatment for central post-stroke pain

Background: Central post-stroke pain (CPSP) is often resistant to treatment. We have previously proposed that caloric vestibular stimulation might alleviate it. Methods: We conducted a single blind placebo controlled investigational study of caloric vestibular stimulation (CVS) in nine patients with CPSP. Participants rated their pain levels before and after the procedure on a 10 point scale. Results: We found a significant immediate treatment effect of the cold water caloric stimulation with an average pain reduction of 2.58 points (SEM 0.52) for the experimental condition compared with 0.54 points (SEM 0.49) for the placebo conditions. Conclusions: Participants who responded best to CVS had suffered strokes that spared and permitted activation of the dominant parieto-insular vestibular cortex (PIVC), which is known to be located in the non-dominant hemisphere. These findings tie in closely with the thermosensory disinhibition hypothesis for central pain, which leads us to propose that vestibular stimulation may alleviate CPSP from cross activation between the PIVC and the thermosensory cortex in the adjacent dorsal posterior insula. Alternatively, if one views vestibular function and thermoregulation as part of a larger interoceptive system that exists to maintain homeostasis, then it is possible they share a common integrative mechanism in the brainstem, which may act to reset the balance in central pain.
The Brassinosteroid Signaling Pathway—New Key Players and Interconnections with Other Signaling Networks Crucial for Plant Development and Stress Tolerance

Brassinosteroids (BRs) are a class of steroid hormones regulating a wide range of physiological processes during the plant life cycle from seed development to the modulation of flowering and senescence. The last decades, and recent years in particular, have witnessed a significant advance in the elucidation of the molecular mechanisms of BR signaling from perception by the transmembrane receptor complex to the regulation of transcription factors influencing expression of the target genes. Application of the new approaches shed light on the molecular functions of the key players regulating the BR signaling cascade and allowed identification of new factors. Recent studies clearly indicated that some of the components of BR signaling pathway act as multifunctional proteins involved in other signaling networks regulating diverse physiological processes, such as photomorphogenesis, cell death control, stomatal development, flowering, plant immunity to pathogens and metabolic responses to stress conditions, including salinity. Regulation of some of these processes is mediated through a crosstalk between BR signalosome and the signaling cascades of other hormones, including auxin, abscisic acid, ethylene and salicylic acid. Unravelling the complicated mechanisms of BR signaling and its interconnections with other molecular networks may be of great importance for future practical applications in agriculture.
Peptides with Dual Antimicrobial and Anticancer Activities

In recent years, the number of people suffering from cancer and multi-resistant infections has increased, such that both diseases are already seen as current and future major causes of death. Moreover, chronic infections are one of the main causes of cancer, due to the instability in the immune system that allows cancer cells to proliferate. Likewise, the physical debility associated with cancer or with anticancer therapy itself often paves the way for opportunistic infections. It is urgent to develop new therapeutic methods, with higher efficiency and lower side effects. Antimicrobial peptides (AMPs) are found in the innate immune system of a wide range of organisms. Identified as the most promising alternative to conventional molecules used nowadays against infections, some of them have been shown to have dual activity, both as antimicrobial and anticancer peptides (ACPs). Highly cationic and amphipathic, they have demonstrated efficacy against both conditions, with the number of nature-driven or synthetically designed peptides increasing year by year. With similar properties, AMPs that can also act as ACPs are viewed as future chemotherapeutic drugs, with the advantage of low propensity to resistance, which started this paradigm in the pharmaceutical market. These peptides have already been described as molecules presenting killing mechanisms at the membrane level, but also acting toward intracellular targets, which increases their success compartively to one-target specific drugs. This review will approach the desirable characteristics of small peptides that demonstrated dual activity against microbial infections and cancer, as well as the peptides engaged in clinical trials.
A proposal for a dynamic h-type index

A time-dependent h-type indicator is proposed. This indicator depends on the size of the h-core, the number of citations received, and recent change in the value of the h-index. As such, it tries to combine in a dynamic way older information about the source (e.g., a scientist or research institute that is evaluated) with recent information. © 2008 Wiley Periodicals, Inc.
Migraine: Association with Personality Characteristics and Psychopathology

The relationship between migraine and psychopathology has been discussed far more often than it has been systematically studied. Twentieth-century investigators have frequently described the obsessional, rigid, angry personality postulated to characterize migraine sufferers. More recent population-based studies have demonstrated associations between migraine and depression and migraine and panic disorder. This article discusses the relationship of migraine and personality and migraine and psycho-pathology.