Miracle-dz/UniFAR-FTUs huggingface dataset:
Instrumenting self-modifying code

Adding small code snippets at key points to existing code fragments is called instrumentation. It is an established technique to debug certain otherwise hard to solve faults, such as memory management issues and data races. Dynamic instrumentation can already be used to analyse code which is loaded or even generated at run time. With the advent of environments such as the Java Virtual Machine with optimizing Just-In-Time compilers, a new obstacle arises: self-modifying code. In order to instrument this kind of code correctly, one must be able to detect modifications and adapt the instrumentation code accordingly, preferably without incurring a high penalty speedwise. In this paper we propose an innovative technique that uses the hardware page protection mechanism of modern processors to detect such modifications. We also show how an instrumentor can adapt the instrumented version depending on the kind of modificiations as well as an experimental evaluation of said techniques.
Anomaly Detection In Labeled Data

Noisy points in training data maybe due to incorrect class labels or erroneous recording of attribute values. These points greatly influence the orientation of the classification boundary. In this paper, we formalize two notions of noisy points: intrusive outliers and hard-to-classify points. We adapt two well-known distance-based notions of outliers in unlabeled data to formalize intrusive outliers and adapt the corresponding algorithms to detect them in labeled data. We propose a boosting-based algorithm to identify hard-toclassify points in labeled data. We empirically compare these two notions of noisy points and their influence on classification accuracy. Finally we experimentally prove that removal of noisy points improves the robustness of the classification boundary against future noisy data.
Parallel Preconditioned Conjugate-Gradients Methods on Transputer Networks

We show how highly efficient parallel implementations of basic linear algebra routines may be used as building blocks to implement efficient higher level algorithms. We discuss the solution of systems of linear equations using a preconditioned Conjugate-Gradients iterative method on a network of transputers. Results are presented for the solution of both dense and sparse systems; the latter being derived from the finite-difference approximation of partial differential equations.
X-ray microtomographic imaging and analysis for basic research

For research facilities with access to synchrotron X-ray sources, X-ray absorption microtomography (XMT) has evolved from an experimental imaging method to a specialized, if not yet routine, microscopy for imaging the three-dimensional (3D) distribution of linear attenuation coefficients and, in some cases, elemental concentration with micron spatial resolution. Recent advances in source and detector design have produced conventional X-ray source instruments with comparable spatial resolution but with lower throughput and without element specific imaging. Both classes of instrument produce 3D images for analysis. We discuss an integrated approach for the implementation of analytical XMT to support basic research into the structure-property relationships of a variety of materials. The essential components include instrumentation for collecting quantitative 3D images, a 3D image processing environment to address questions as to the quantity, composition, geometry, and relationships among the features in one or more images, and visualization to provide insight and communicate results. We give examples of image analysis of resolved and unresolved pore spaces of sandstones.
Deciding on Micro-CHP; A Multi-Level Decision-Making Approach

Micro combined heat and power (micro-CHP) is a promising, more fuel efficient, technology that could change the energy infrastructure as a whole. This paper describes the possible decision-making that results from micro-CHP introduction. The focus lies on the supplier-household interaction. Decisions made by supplier (price of electricity to/from households) influence decisions of households (1. micro-CHP power level and 2. amount of discharged heat) and determine the supplier's operational costs. When the supplier takes into account the cost optimization of households (that is based on the supplier's decision) in making his price-setting decisions, the problem can be described as a multi-level decision-making (MLDM) problem. We describe how the problem can be modelled and present a solution strategy which considers a combination of two objective functions that are subject to a set of constraints. Results of supplier price-setting are presented as well. Solving the problem via the MLDM approach is expected to lead to improved decision-making and a better performance of the supplier. Applying MLDM to the decision problem presented here is novel and can contribute to dealing with decision-making complexity in the energy infrastructure in general
Beyond Parallel Data: Joint Word Alignment and Decipherment Improves Machine Translation

Inspired by previous work, where decipherment is used to improve machine translation, we propose a new idea to combine word alignment and decipherment into a single learning process. We use EM to estimate the model parameters, not only to maximize the probability of parallel corpus, but also the monolingual corpus. We apply our approach to improve Malagasy-English machine translation, where only a small amount of parallel data is available. In our experiments, we observe gains of 0.9 to 2.1 Bleu over a strong baseline.
Monitoring time-dependent degradation of phospholipids in sectioned tissues by MALDI imaging mass spectrometry.

Imaging mass spectrometry (IMS) is useful for visualizing the localization of phospholipids on biological tissue surfaces creating great opportunities for IMS in lipidomic investigations. With advancements in IMS of lipids, there is a demand for large-scale tissue studies necessitating stable, efficient and well-defined sample handling procedures. Our work within this article shows the effects of different storage conditions on the phospholipid composition of sectioned tissues from mouse organs. We have taken serial sections from mouse brain, kidney and liver thaw mounted unto ITO-coated glass slides and stored them under various conditions later analyzing them at fixed time points. A global decrease in phospholipid signal intensity is shown to occur and to be a function of time and temperature. Contrary to the global decrease, oxidized phospholipid and lysophospholipid species are found to increase within 2 h and 24 h, respectively, when mounted sections are kept at ambient room conditions. Imaging experiments reveal that degradation products increase globally across the tissue. Degradation is shown to be inhibited by cold temperatures, with sample integrity maintained up to a week after storage in -80 °C freezer under N2 atmosphere. Overall, the results demonstrate a timeline of the effects of lipid degradation specific to sectioned tissues and provide several lipid species which can serve as markers of degradation. Importantly, the timeline demonstrates oxidative sample degradation begins appearing within the normal timescale of IMS sample preparation of lipids (i.e. 1-2 h) and that long-term degradation is global. Taken together, these results strengthen the notion that standardized procedures are required for phospholipid IMS of large sample sets, or in studies where many serial sections are prepared together but analyzed over time such as in 3-D IMS reconstruction experiments.
Learning, Generalization, and Obstacle Avoidance with Dynamic Movement Primitives and Dynamic Potential Fields

In order to offer simple and convenient assistance for the elderly and disabled to take care of themselves, we propose a general learning and generalization approach for a service robot to accomplish specified tasks autonomously in an unstructured home environment. This approach firstly learns the required tasks by learning from demonstration (LfD) and represents the learned tasks with dynamic motion primitives (DMPs), so as to easily generalize them to a new environment only with little modification. Furthermore, we integrate dynamic potential field (DPF) with the above DMPs model to realize the autonomous obstacle avoidance function of a service robot. This approach is validated on the wheelchair mounted robotic arm (WMRA) by performing serial experiments of placing a cup on the table with an obstacle or without obstacle on its motion path.
Coherent Zooming of Illustrations with 3D-Graphics and Text

In this paper we develop a method for zooming in complex information spaces consisting of geometric models and associated textual explanations. In particular we introduce a technique to extend fisheye views to the exploration of three-dimensional models. The result of a zooming process is a modified geometric model which is rendered and presented to the user with appropriately selected and well-placed textual labels. The user interacts with such coherent presentations to explore the information space. Our methods have been implemented in a system called the ZOOM ILLUSTRATOR. The system maintains a close relationship between images and associated text, with interaction on the textual part influencing the appearance of the graphical part and vice versa. We demonstrate our system on medical illustrations, in particular from anatomy.
Subthalamic Nucleus High-Frequency Stimulation Restores Altered Electrophysiological Properties of Cortical Neurons in Parkinsonian Rat

Electrophysiological recordings performed in parkinsonian patients and animal models have confirmed the occurrence of alterations in firing rate and pattern of basal ganglia neurons, but the outcome of these changes in thalamo-cortical networks remains unclear. Using rats rendered parkinsonian, we investigated, at a cellular level in vivo, the electrophysiological changes induced in the pyramidal cells of the motor cortex by the dopaminergic transmission interruption and further characterized the impact of high-frequency electrical stimulation of the subthalamic nucleus, a procedure alleviating parkinsonian symptoms. We provided evidence that a lesion restricted to the substantia nigra pars compacta resulted in a marked increase in the mean firing rate and bursting pattern of pyramidal neurons of the motor cortex. These alterations were underlain by changes of the electrical membranes properties of pyramidal cells including depolarized resting membrane potential and increased input resistance. The modifications induced by the dopaminergic loss were more pronounced in cortico-striatal than in cortico-subthalamic neurons. Furthermore, subthalamic nucleus high-frequency stimulation applied at parameters alleviating parkinsonian signs regularized the firing pattern of pyramidal cells and restored their electrical membrane properties.
Fast convergent Green's function in a rectangular enclosure

Ewald summation technique has been proven to be a highly efficient method for rapid calculation of the potential Green's functions in rectangular cavities. It is known that in addition to potential Green's functions, curl of the vector potential Green's function is also needed when applying the mixed potential integral equation method to dielectric objects. In this paper the Ewald sum technique is applied for rapid calculation of these components of the dyadic Green's functions. Several numerical implementation issues including the extraction of singularity when using the Ewald summation method ate also addressed for the first time, leading to further enhancement in computational speed accuracy, and numerical stability.
Contextual Itemset Mining in DBpedia

In this paper we show the potential of contextual itemset mining in the context of Linked Open Data. Contextual itemset mining extracts frequent associations among items considering background information. In the case of Linked Open Data, the background information is represented by an Ontology defined over the data. Each resulting itemset is specific to a particular context and contexts can be related each others following the ontological structure. We use contextual mining on DBpedia data and show how the use of contextual information can refine the itemsets obtained by the knowledge discovery process.
Exact BER for M-QAM with MRC and Imperfect Channel Estimation in Rician Fading Channels

In this paper, we study the effect of imperfect channel estimation (ICE) on the performance of M-level quadrature amplitude modulation (M-QAM) with maximum ratio combining (MRC) and pilot-symbol assisted modulation (PSAM) in generalized Rician fading channels. By expressing the bit error rate (BER) of MRC diversity M-QAM in terms of the distribution of new decision variables, we derive novel, exact, and easy-to-evaluate BER expressions for diversity M-QAM with channel estimation errors. Our results include versatile system and fading channel parameters (e.g., arbitrary spatial and temporal correlation patterns among the diversity branches), and are valid for arbitrary linear channel estimators and square and rectangular M -QAM with different constellation sizes. In addition, we evaluate the performance of minimum mean-squared error (MMSE)- and sinc-interpolator-based channel estimators with PSAM, and provide some new insights into the performance of M-QAM with PSAM in generalized fading channels
Social recommender systems for web 2.0 folksonomies

The rapidly increasing popularity of Web 2.0 knowledge and content sharing systems and growing amount of shared data make discovering relevant content and finding contacts a difficult enterprize. Typically, folksonomies provide a rich set of structures and social relationships that can be mined for a variety of recommendation purposes. In this paper we propose a formal model to characterize users, items, and annotations in Web 2.0 environments. Our objective is to construct social recommender systems that predict the utility of items, users, or groups based on the multi-dimensional social environment of a given user. Based on this model we introduce recommendation mechanisms for content sharing frameworks. Our comprehensive evaluation shows the viability of our approach and emphasizes the key role of social meta knowledge for constructing effective recommendations in Web 2.0 applications.
A Load Cell for Grain-Propelled Ballistic Rocket Thrust Measurement

A 1000-kgf resistive strain-gauge load cell has been developed for quality testing of rocket propellant grain. A 7075-T6 aluminum alloy has been used for the elastic column, in which 8 uniaxial, 120-Omega strain gauges have been bonded and connected to form a full Wheatstone bridge to detect the strain. The chosen geometry makes the transducer insensitive to moments and, also, to the temperature. Experimental tests using a universal testing machine to imposed compression force to the load cell have demonstrated that its behavior is linear, with sensitivity of 2.90 muV/kgf plusmn0.34%, and negligible hysteresis. The designed force transducer response to a dynamic test has been comparable to that of a commercial load cell
BRIDGING THE GAPS: HYBRID TRACKING FOR ADAPTIVE MOBILE AUGMENTED REALITY

Tracking accuracy in a location-aware mobile system can change dynamically as a function of the user's location and other variables specific to the tracking technologies used. This is especially problematic for mobile augmented reality systems, which ideally require extremely precise position tracking for the user's head, but which may not always be able to achieve that level of accuracy. While it is possible to ignore variable positional accuracy in an augmented reality user interface, this can make for a confusing system; for example, when accuracy is low, virtual objects that are nominally registered with real ones may be too far off to be of use. To address this problem, we describe an experimental mobile augmented reality system that: (1) employs multiple position-tracking technologies, including ones that apply heuristics based on environmental knowledge; (2) coordinates these concurrently monitored tracking systems; and (3) automatically adapts the user interface to varying degrees of confidence in tracking accuracy. We share our experiences with managing these multiple tracking technologies, employing various techniques to facilitate smooth and reasonable “hand-offs” between the cooperating systems. We present these results in the context of an intelligent navigational guidance system that helps users to orient themselves in an unfamiliar environment, using path planning to guide them toward destinations they choose, and sometimes towards ones the system infers as equally relevant.
Super-gaussianity of Speech Spectral Coefficients as a Potential Biomarker for Dysarthric Speech Detection

Parkinson’s disease (PD) and Amyotrophic Lateral Sclerosis (ALS) are progressive neurodegenerative diseases which, among other symptoms, cause dysarthria of speech. To assist the clinical diagnosis and treatment of neurological diseases, several studies have addressed the characterization and classification of healthy and dysarthric speech. However, most contributions deal with PD speech, with significantly fewer results presented for ALS speech. The objective of this paper is to show that ALS speech has a similar statistical distribution as PD speech, with the complex spectral coefficients being significantly less super-Gaussian than healthy speech spectral coefficients. In addition, a method to exploit the super-Gaussianity of speech signals as a feature to classify healthy and dysarthric speech is presented and evaluated. The proposed approach is evaluated on a French database of healthy and dysarthric (PD and ALS) speech. Experimental results show that the use of the super-Gaussianity of speech signals yields a significantly higher classification accuracy than state-of-the-art features such as fundamental frequency, jitter, shimmer, harmonics-to-noise ratio, or Mel frequency cepstral coefficients.
Humanoid robot which can lift a 30kg box by whole body contact and tactile feedback

We present realization of a humanoid which can lift a heavy object by whole body contact. Most humanoid motions are limited to the posture of the end-effectors only landing. In principle these humanoids can not do natural motion. If a humanoid robot is allowed arbitrary contact with the surrounding objects, it can improve the performance and operate a heavier object. We propose a "whole body contact motion" of a humanoid robot. It is defined as a control of contact state of a humanoid robot which has the distributed tactile sensors. We develop conformable and scalable tactile skin and an adult-size humanoid with a smooth surfaces for arbitrary contact. We install the skin on the entire surfaces of the humanoid. Finally we describe the humanoid lifting a 30kg box by tactile feedback.
Multilevel Cross‐Dependent Binary Longitudinal Data

We provide insights into new methodology for the analysis of multilevel binary data observed longitudinally, when the repeated longitudinal measurements are correlated. The proposed model is logistic functional regression conditioned on three latent processes describing the within‐ and between‐variability, and describing the cross‐dependence of the repeated longitudinal measurements. We estimate the model components without employing mixed‐effects modeling but assuming an approximation to the logistic link function. The primary objectives of this article are to highlight the challenges in the estimation of the model components, to compare two approximations to the logistic regression function, linear and exponential, and to discuss their advantages and limitations. The linear approximation is computationally efficient whereas the exponential approximation applies for rare events functional data. Our methods are inspired by and applied to a scientific experiment on spectral backscatter from long range infrared light detection and ranging (LIDAR) data. The models are general and relevant to many new binary functional data sets, with or without dependence between repeated functional measurements.
Large-scale Dictionary Learning For Local Coordinate Coding

Local coordinate coding has recently been introduced to learning visual feature dictionary and achieved top level performance for object recognition. However, the computational complexity scales linearly with the number of samples, so it does not scale up well for large-scale databases. In this paper, we propose an online learning algorithm which, at every iteration round, only processes one or a mini-batch of random samples (e.g., two hundred samples). Our algorithm theoretically ensures the convergence to the expected objective at infinity. Experiments on object recognition demonstrate the advantage over the original local coordinate coding method in terms of efficiency with comparable performance.
Synaptic SAP97 Isoforms Regulate AMPA Receptor Dynamics and Access to Presynaptic Glutamate

The synaptic insertion of GluR1-containing AMPA-type glutamate receptors (AMPARs) is critical for synaptic plasticity. However, mechanisms responsible for GluR1 insertion and retention at the synapse are unclear. The synapse-associated protein SAP97 directly binds GluR1 and participates in its forward trafficking from the Golgi network to the plasma membrane. Whether SAP97 also plays a role in scaffolding GluR1 at the postsynaptic membrane is controversial, attributable to its expression as a collection of alternatively spliced isoforms with ill-defined spatial and temporal distributions. In the present study, we have used live imaging and electrophysiology to demonstrate that two postsynaptic, N-terminal isoforms of SAP97 directly modulate the levels, dynamics, and function of synaptic GluR1-containing AMPARs. Specifically, the unique N-terminal domains confer distinct subsynaptic localizations onto SAP97, targeting the palmitoylated α-isoform to the postsynaptic density (PSD) and the L27 domain-containing β-isoform primarily to non-PSD, perisynaptic regions. Consequently, α- and βSAP97 differentially influence the subsynaptic localization and dynamics of AMPARs by creating binding sites for GluR1-containing receptors within their respective subdomains. These results indicate that N-terminal splicing of SAP97 can control synaptic strength by regulating the distribution of AMPARs and, hence, their responsiveness to presynaptically released glutamate.
INVESTIGATION OF BIOLOGICAL ACTIVITIES AND SECONDARY METABOLITES OF HYDNUM REPANDUM ACETONE EXTRACT

This study aimed to evaluate the biological activities and polyphenolic contents of the acetone extract of H. repandum mushroom. Polyphenolic compounds were evaluated by high-performance liquid chromatography (HPLC). The antioxidant activity was assessed by radical scavenging activity assays and reducing power. The antimicrobial activity was established based on the values of the minimum inhibitory concentration (MIC) using microdilution method. Cytotoxic activity was determined by 3-4,5-dimethylthiazol-2-yl-2,5-diphenyltetrazolium bromide (MTT). The genotoxic and antimutagenic activities were tested using cytokinesis block micronucleus (CBMN) assay on human peripheral blood lymphocytes in vitro. Among the determined polyphenolic compounds, ferulic acid and quercetin were mostly found. The extract showed high free radical scavenging activity, while the reducing power was less emphasized and concentration-dependent. The MIC fluctuated in a range of 0.009-10 mg/mL. The cytotoxic activity (based on IC50) ranged from 116.5 to 158.33 μg/mL, when HeLa cells were the most sensitive. The highest tested concentrations of the extract showed significant genotoxic activity, while against mitomycin C, the extract caused protective activity. The results indicated that H. repandum acetone extract contained secondary metabolites which showed biological activities such as antioxidant, antimicrobial, cytotoxic, genotoxic and protective against chemotherapeutics, indicating that their inclusion in nutrition could be of great importance in the prevention and treatment of various pathological conditions.