Miracle-dz/UniFAR-FTUs huggingface dataset:
Multivariate classification of pain-evoked brain activity in temporomandibular disorder

Abstract Introduction: Central nervous system factors are now understood to be important in the etiology of temporomandibular disorders (TMD), but knowledge concerning objective markers of central pathophysiology in TMD is lacking. Multivariate analysis techniques like support vector machines (SVMs) could generate important discoveries regarding the expression of pain centralization in TMD. Support vector machines can recognize patterns in “training” data and subsequently classify or predict new “test” data. Objectives: We set out to detect the presence and location of experimental pressure pain and determine clinical status by applying SVMs to pain-evoked brain activity. Methods: Functional magnetic resonance imaging was used to record brain activity evoked by subjectively equated noxious temporalis pressures in patients with TMD and controls. First, we trained an SVM to recognize when the evoked pain stimulus was on or off based on each individual's pain-evoked blood–oxygen–level–dependent (BOLD) signals. Next, an SVM was trained to distinguish between the BOLD response to temporalis-evoked pain vs thumb-evoked pain. Finally, an SVM attempted to determine clinical status based on temporalis-evoked BOLD. Results: The on-versus-off accuracy in controls and patients was 83.3% and 85.1%, respectively, both significantly better than chance (ie, 50%). Accurate determination of experimental pain location was possible in patients with TMD (75%), but not in healthy subjects (55%). The determination of clinical status with temporalis-evoked BOLD (60%) failed to reach statistical significance. Conclusion: The SVM accurately detected the presence of noxious temporalis pressure in patients with TMD despite the stimulus being colocalized with their ongoing clinical pain. The SVM's ability to determine the location of noxious pressure only in patients with TMD reveals somatotopic-dependent differences in central pain processing that could reflect regional variations in pain valuation.
A formal likelihood function for parameter and predictive inference of hydrologic models with correlated, heteroscedastic, and non‐Gaussian errors

Estimation of parameter and predictive uncertainty of hydrologic models has traditionally relied on several simplifying assumptions. Residual errors are often assumed to be independent and to be adequately described by a Gaussian probability distribution with a mean of zero and a constant variance. Here we investigate to what extent estimates of parameter and predictive uncertainty are affected when these assumptions are relaxed. A formal generalized likelihood function is presented, which extends the applicability of previously used likelihood functions to situations where residual errors are correlated, heteroscedastic, and non‐Gaussian with varying degrees of kurtosis and skewness. The approach focuses on a correct statistical description of the data and the total model residuals, without separating out various error sources. Application to Bayesian uncertainty analysis of a conceptual rainfall‐runoff model simultaneously identifies the hydrologic model parameters and the appropriate statistical distribution of the residual errors. When applied to daily rainfall‐runoff data from a humid basin we find that (1) residual errors are much better described by a heteroscedastic, first‐order, auto‐correlated error model with a Laplacian distribution function characterized by heavier tails than a Gaussian distribution; and (2) compared to a standard least‐squares approach, proper representation of the statistical distribution of residual errors yields tighter predictive uncertainty bands and different parameter uncertainty estimates that are less sensitive to the particular time period used for inference. Application to daily rainfall‐runoff data from a semiarid basin with more significant residual errors and systematic underprediction of peak flows shows that (1) multiplicative bias factors can be used to compensate for some of the largest errors and (2) a skewed error distribution yields improved estimates of predictive uncertainty in this semiarid basin with near‐zero flows. We conclude that the presented methodology provides improved estimates of parameter and total prediction uncertainty and should be useful for handling complex residual errors in other hydrologic regression models as well.
Self-Adaptive Automata

Self-adaptive systems aim to efficiently respond to a wide range of changes in their operational environment by dynamically altering their behaviour. Such systems are typically comprised of a base system, implementing core functionality, and an adaptation decision process, which determines how the base system must change at different points in its execution. These two components coordinate through a set of adaptation events: a set of execution points of the former where the latter is invoked. The pattern of these events is crucial for the overall system to achieve (a) correctness against specific requirements, and (b) efficiency of system resources. Existing techniques for modelling self-adaptive systems usually hardcode adaptation events within the base system or the adaptation decision process. This limits system designers in discovering correct and optimal patterns of adaptation events, as changing those involves signi cant changes in the model. In this work we present Self-Adaptive Automata, an abstract modelling framework which decouples adaptation event patterns from the descriptions of base systems and adaptation decision processes. In our framework, base systems expose execution points where adaptation may happen — in the most general case this can include all system states — and adaptation decision processes are parametric to these points. A distinct automaton then pinpoints when in the system adaptation must happen. Using this framework system designers can experiment with different adaptation event patterns, without modifications to the base system or the adaptation decision process, and discover correct and efficient patterns. We show that our framework is compatible with traditional verification tools by providing an adequate translation from Self-Adaptive Automata into FDR, in which correctness against requirements can be verified. We also prove that, although our automata framework includes dynamic self-modifying features, it corresponds to standard models of computation. We illustrate the use of our framework through a use case of a self-adaptive system of autonomous search-and-rescue rovers.
Synthesis and Characterization of Silica-Encapsulated Iron Oxide Nanoparticles

The properties of magnetic core-shell nanoparticles greatly depend on their core sizes and shell materials. Silica shell can prevent the magnetic nanoparticles from corrosion and agglomeration. In addition, the hydrolyzed silica can provide silanol groups to facilitate surface biofunctionalization. In this paper, superparamagnetic Fe3O4 nanoparticles coated with SiO2 shell were prepared by a one-pot water-in-oil microemulsion method. Transmission electron microscopy (TEM), scanning electron microscopy (SEM), and vibrating sample magnetometry (VSM) were utilized to characterize the morphology and magnetic properties of the synthesized nanoparticles. The results indicated that by tuning the water/surfactant molar ratio (Wo) of the microemulsion system, core size of the resulting Fe3O4 nanoparticles can be altered. The size-controllable silica-encapsulated Fe3O4 superparamagnetic nanoparticles have great potential to be applied as multifunctional tracer materials for magnetic particle imaging (MPI).
Introduction to Real-time Control using LabVIEW TM with an Application to Distance Learning*

This paper presents the approach taken to give engineering students the necessary competencies and facilities to implement real-time control solutions. This goal is achieved first by way of an introduction to the basic principles underlying real-time control. Then, by motivating the use of personal computers as a versatile alternative to more traditional implementation equipment. Finally, by combining LabVIEW and DAQ boards to form an integrated framework for fast prototyping of real-time control solutions. Control algorithms written in G (the graphical programming language of LabVIEW), in C or as S-functions (MATLAB scripts describing SIMULINK dynamical models) can be validated on laboratory-scale processes. The possible real- time control and monitoring of ongoing operations, either locally on the host computer or remotely via the Internet, is a key feature from an educational point of view. In fact, the chosen paradigm truly enable the `learning by doing' approach. Moreover, this practical activity can be carried out at any time from anywhere to efficiently support automatic control study.
Survey on Relevance Calculation Methods

Relevance is one of the most important concepts in information retrieval. This paper discusses various approaches for relevance calculation available in literature. In Information Retrieval, relevance is how well a retrieved document or set of documents meets the information need. The paper summarizes, what relevance is and a survey of different methods of calculating relevance. Our goal is to study existing methods and to identify usefulness of existing methods for multilingual data and to understand the prerequisites required for these methods while using mime types likes webpages, urls, xml apart from plain text. General Terms Information Retrieval, Pattern Recognition, Algorithms
Robust Efficient Robot Planning through Varying Model Fidelity

Long-term robot autonomy requires reliable execution success. Execution success improves by modeling the real world accurately during planning time. Unfortunately, computing a full plan in highly accurate models is often intractable since accurate world models increase the dimensionality of the planning space. Additionally, planning in the highest model is unnecessary for simple uncluttered parts of the environment. Current planners achieve tractable planning times by using approximate models. However, these approximations reduce accuracy resulting in decreased execution success. We introduce an approach that balances planning time efficiency while maintaining execution success. This is accomplished by leveraging a varying fidelity model hierarchy. The approach identifies infeasible execution locations and uses a model selection process to locally re-plan in the minimum fidelity model necessary to circumvent the infeasibility. This effectively generates a single, mixed-fidelity plan. We evaluate the approach on a simulated differential drive robot that navigates a constrained environment. The robot maintains its rate of successful task completion while conserving computation resources by switching from lower fidelity models only when needed.
A Novel Approach for On-road Vehicle Detection and Tracking

On the basis of a necessary development of the road safety, vision-based vehicle detection techniques have gained an important amount of attention. This work presents a novel vehicle detection and tracking approach, and structured based on a vehicle detection process starting from, images or video data acquired from sensors installed on board of the vehicle, to vehicle detection and tracking. The features of the vehicle are extracted by the proposed GIST image processing algorithm, and recognized by the state-of-art Support Vectors Machine classifier. The tracking process was performed based on edge features matching approach. The Kalman filter was used to correct the measurements. Extensive experiments were carried out on real image data validate that it is promising to employ the proposed approach for on road vehicle detection and tracking.
Business Cycle in a Neoclassical Growth Model : How important are technology shocks as a propagation mechanism ?

This paper quantitatively investigates the role of technology shocks in propagating business cycles in a neoclassical growth model. I use the new technique of business cycle accounting (BCA) which enables me to maintain the basic framework of a standard growth model, but allows multiple propagation channels (referred to as wedges), technology fluctuations being one of them. My test case is Japan during the period 1980 to 2000. I find that though technology shocks play an important role in propagating market frictions, they are by no means enough to account for the observed economic fluctuations during this period. Shocks that propagate themselves as investment wedges play a major role. A standard RBC model fails to recognize this channel due to the paucity of propagation channels that it allows and consequently tends to overemphasize the role of technology shocks, often at the expense of other channels thus leading to erroneous conclusions.
Conformational Variations of Both Phosphodiesterase-5 and Inhibitors Provide the Structural Basis for the Physiological Effects of Vardenafil and Sildenafil

Vardenafil has higher affinity to phosphodiesterase-5 (PDE5) than sildenafil and lower administered dosage for the treatment of erectile dysfunction. However, the molecular basis for these differences is puzzling because two drugs have similar chemical structures. Reported here is a crystal structure of the fully active and nonmutated PDE5A1 catalytic domain in complex with vardenafil. The structure shows that the conformation of the H-loop in the PDE5A1-vardenafil complex is different from those of any known structures of the unliganded PDE5 and its complexes with the inhibitors. In addition, the molecular configuration of vardenafil differs from that of sildenafil when bound to PDE5. It is noteworthy that the binding of vardenafil causes loss of the divalent metal ions that have been observed in all the previously published PDE structures. The conformational variation of both PDE5 and the inhibitors provides structural insight into the different potencies of the drugs.
A Novel Low-Bias Charge Concept for HBT/BJT Models Including Heterobandgap and Temperature Effects—Part II: Implementation, Parameter Extraction and Verification

A new compact model approach was suggested in Part I for the low-bias base charge of homo- and heterojunction bipolar transistors. Conventional capacitance-charge formulas with dc extracted parameters were shown to provide accurate description for the Moll-Ross-Gummel charge components. New parameters were introduced to account for BGN effects and Ge doping on the temperature behavior of SiGe transistors. In this part, the implementation of the concept will be detailed for an unnormalized and a normalized heterojunction-bipolar-transistor/bipolar-junction-transistor model. Extraction methodology will be described for the new parameters, and the results will be demonstrated on advanced transistors.
BIOSYNTHESIS OF METAL NANOPARTICLES USING FUNGI AND ACTINOMYCETE

There is little doubt that nanomaterials will play a key role in many technologies of the future. One key aspect of nanotechnology concerns the development of reliable experimental protocols for the synthesis of nanomaterials over a range of chemical compositions, sizes and high monodispersity. In the context of the current drive to develop green technologies in materials synthesis, this aspect of nanotechnology assumes considerable importance. An attractive possibility is to use micro-organisms in the synthesis of nanoparticles. In this article, we provide a brief overview of the research efforts worldwide on the use of micro-organisms in the biosynthesis of inorganic nanoparticles, with particular emphasis on the recent and exciting results obtained at the National Chemical Laboratory, Pune on the biosynthesis of noble-metal nanoparticles using fungi and actinomycete. Some of the challenges in this emerging approach are highlighted.
APPENDIX VI

Background: Mutations in APP, PSEN1 and PSEN2 lead to early-onset Alzheimer disease (EOAD). These mutations account for ~11% of EOAD overall, leaving the majority of genetic risk for the most severe form of Alzheimer disease unexplained. Methods: We performed Whole-Exome Sequencing (WES) in 50 Caucasian EOAD cases previously screened negative for APP,PSEN1, and PSEN2 to search for rare variants contributing to risk for EOAD. Variant filtering for functional, damaging rare variants (MAF<0.1%) was performed. Genes with shared (2+ cases with the same variant), damaging variants were examined for interactions with known EOAD genes (APP, PSEN1, PSEN2, SORL1, GRN, MAPT) and APOE using STRINGdb. Results: 176 genes had rare functional variants shared in two or more cases. 46 of these genes were prioritized for their damaging potential, defined by their shared rare variants having a Combined Annotation Dependent Depletion (CADD) score in the top 10% of all variants. Gene network analysis of these 46 genes with known EOAD genes and APOE identified five top candidate genes: HSPG2 (interacts withGRN, APOE, and APP), CLSTN1 (interacts with PSEN1 and APP), DOCK3 (interacts with PSEN1 and PSEN2), and the APOEinteractors SAR1B and STAT1. 5 cases have a variant in HSPG2, a gene potentially involved in amyloidogenesis and tau aggregation in AD, while 4 cases have a variant in DOCK3, a gene expressed exclusively in the central nervous system and associated with neurofibrillary tangles in AD brains. Conclusions: WES of EOAD cases identified several genes with potential roles in AD pathogenesis.
Cash Holdings , Costly Financing , and Q-Theory at the Firm Level ∗

I incorporate costly external financing and cash holdings into an investment-based model of stock returns. I estimate the model at the firm level—a task notoriously difficult for q theoretic models. I find that a relatively parsimonious model with cash holdings dramatically outperforms similar models without cash holdings even at the firm level. Surprisingly, even simply accounting for the existence of cash holdings dramatically improves fit even without any additional parameters. Though costly external financing appears to impact the model to some extent, the largest gains by far come from simply accounting for cash. JEL Classifications: G30 G32 G34
PresentationCytosolic Peptide Stability to Antigen N-Terminal Dibasic Peptides: Coupling Cutting Edge: HLA-B27 Acquires Many

Ag presentation by MHC class I is a highly inefficient process because cytosolic peptidases destroy most peptides after proteasomal generation. Various mechanisms shape the MHC class I peptidome. We define a new one: intracellular peptide stability. Peptides with two N-terminal basic amino acids are more stable than other peptides. Such peptides should be overrepresented in the peptidome of MHC class I-associated peptides. HLA-B27 binding peptides use anchor residue R at P2 and, although most amino acids are allowed, particular amino acids are overrepresented at P1, including R and K. We show that such N-terminal dibasic peptides are indeed more efficiently presented by HLA-B27. This suggests that HLA-B27 can present peptides from Ags present in fewer copies than required for successful peptide generation for other MHC class I molecules. The Journal of Immunology, 2006, 176: 2697–2701.
Offline signature identification using the histogram of symbolic representation

This work proposes an off-line handwritten signature identification system using the Histogram of Symbolic Representation (HSR). The HSR is considered as one-class classifier which has the ability to generate a model for each writer using only its own reference signatures. This method allows also modeling the writing style of each writer by taking into account the variability of signatures. To evaluate the robustness of the proposed identification system, two well-known standard offline handwritten signature datasets are used, namely CEDAR-55 and GPDS-300. The experimental results achieve an accuracy of 98.63% and 97.84%, respectively. These obtained results are broadly better than the state-of-art when using only 5 reference signatures.
MMPI-2 CHARACTERISTICS OF MALE SERIAL MURDERERS

The current research reports 61 male serial homicide offenders’ Minnesota Multiphasic Personality Inventory – 2 (MMPI-2) results. Offenders had an average MMPI-2 profile code of 4-6-8. They also scored high on the Harris-Lingoes scales of Authority Problems, Persecutory Ideas, and Emotional Alienation. Megargee classifications were fairly divided, but Delta was the largest grouping. A hierarchical cluster analysis of MMPI-2 profiles revealed two distinct profiles, one disturbed and one non-disturbed. The disturbed cluster showed numerous elevations on the clinical scales, while the non-disturbed cluster only had an elevation on Psychopathic Deviance. There were several differences in content and supplementary scales measurements for the two clusters. The MMPI-2 basic scales were limited in the prediction of offender’s murderous behavior. This project shows the average serial murderer has a typical MMPI-2 pattern and one indicative of emotional disorders. However, the murderers remain diverse in their individual psychopathologies. Implications and limitations of the research are discussed.
leader-following formation control with an adaptive linear and terminal sliding mode combined controller using auto-structuring fuzzy neural network

—This paper proposes an intelligent formation control method of the leader and follower agents with nonlinear dynamics. In the proposed method, agents can exchange only information of positions of available agents and the follower agents follow the leader, taking a predefined formation. In the real world, the method that doesn’t need a lot of information to make cooperative behaviors is very useful for the case of environments existing communication delay and weak communication. In addition, each agent will be controlled by the linear and terminal combined sliding mode control method. Furthermore, to adapt the change of the environment, the Auto-Structuring Fuzzy Neural Control System (ASFNCS) is introduced to provide appropriate control inputs while coping well with disturbance and nonlinear dynamics. In the simulation, it is verified that the proposed method is useful in the point of performance of the leader-following formation control.
Using Proteins in a Bioinorganic Laboratory Experiment: Iron Loading and Removal from Transferrin

With the increasing availability of metalloproteins it is now possible to incorporate them into bioinorganic laboratory experiments. Thus, we have developed a laboratory experiment where students use UV–vis spectroscopy to determine the rate of iron removal from transferrin by a well-known, commercially-available iron chelating drug, Deferiprone. Students gain experience in both chemical and biological laboratory techniques, while assessing the efficacy of a drug that is physiologically relevant to humans.
NBTI-aware technique for transistor sizing of high-performance CMOS gates

NBTI imposes a challenge for the design of circuits in DSM technologies. NBTI causes increase of Vt of the PMOS transistors, thus leading to timing degradation of CMOS circuits over time. This manuscript presents a NBTI-aware transistor sizing technique for high-performance CMOS gates, which improves the cell reliability with minimum area penalty. The delay of an inverter designed on a 32nm technology using our methodology presents 6% delay reduction at year 3 compared to a traditional sizing methodology, both using the same area. For a NAND gate we achieved a delay improvement up to 11%. The methodology here proposed can be extended to other CMOS logic gates, including complex gates.