Miracle-dz/UniFAR-FTUs huggingface dataset:
SCAN: Learning Speaker Identity from Noisy Sensor Data

Sensor data acquired from multiple sensors simultaneously is featuring increasingly in our evermore pervasive world. Buildings can be made smarter and more efficient, spaces more responsive to users. A fundamental building block towards smart spaces is the ability to understand who is present in a certain area. A ubiquitous way of detecting this is to exploit the unique vocal features as people interact with one another. As an example, consider audio features sampled during a meeting, yielding a noisy set of possible voiceprints. With a number of meetings and knowledge of participation (e.g. through a calendar or MAC address), can we learn to associate a specific identity with a particular voiceprint? Obviously enrolling users into a biometric database is time-consuming and not robust to vocal deviations over time. To address this problem, the standard approach is to perform a clustering step (e.g. of audio data) followed by a data association step, when identity-rich sensor data is available. In this paper we show that this approach is not robust to noise in either type of sensor stream; to tackle this issue we propose a novel algorithm that jointly optimises the clustering and association process yielding up to three times higher identification precision than approaches that execute these steps sequentially. We demonstrate the performance benefits of our approach in two case studies, one with acoustic and MAC datasets that we collected from meetings in a non-residential building, and another from an online dataset from recorded radio interviews.
Evaluating the effects of distance metrics on a NGE-based system

The nested generalized exemplar (NGE) model (implemented by EACH algorithm) is an incremental form of inductive learning from examples that generalizes a given training set into hypotheses represented as a set of hyper-rectangles in an n-dimensional Euclidean space. NGE depends heavily on the distance metric used in both processes, learning and classification. This work investigates the impact on the predictive accuracy of the learnt concepts by NGE as a consequence of using three new heterogeneous distance functions namely HVDM, IVDM and WVDM, instead of the Euclidean distance metric originally proposed. The paper presents and analyses the results of experiments in various domains using the Euclidean and the three heterogeneous distance functions.
Learning Structured Classifiers with Dual Coordinate Ascent

Abstract : We present a unified framework for online learning of structured classifiers that handles a wide family of convex loss functions, properly including CRFs, structured SVMs, and the structured perceptron. We introduce a new aggressive online algorithm that optimizes any loss in this family. For the structured hinge loss, this algorithm reduces to 1-best MIRA; in general, it can be regarded as a dual coordinate ascent algorithm. The approximate inference scenario is also addressed. Our experiments on two NLP problems show that the algorithm converges to accurate models at least as fast as stochastic gradient descent, without the need to specify any learning rate parameter.
Fitting of superquadrics for breast modelling by geometric distance minimization

Breast cancer is one of the most mediated malignant diseases, because of its high incidence and prevalence, but principally due to its physical and psychological invasiveness. Surgeons and patients have often many options to consider for undergoing the procedure. The ability to visualise the potential outcomes of the surgery and make decisions on their surgical options is, therefore, very important for patients and surgeons. In this paper we investigate the fitting of a 3d point cloud of the breast to a parametric model usable in surgery planning, obtaining very promising results with real data.
Androgen resistance caused by mutations in the androgen receptor gene

Defects in the human androgen receptor cause a spectrum of defects in male phenotypic sexual development associated with abnormalities in the receptor protein assayed in cultured fibroblasts and in broken cell assays. In some patients these abnormalities are associated with absent ligand binding, in other qualitative or quantitative abnormalities of ligand binding are present, and in some no abnormality of ligand binding is detected. Analysis of the androgen gene structure in such patients has permitted identification of the causative mutation in many families. Although results of these studies often reinforce concepts established by in vitro mutagenesis studies of other steroid receptors, some mutations have provided unusual insight into the structural organization of the androgen receptor molecule. —McPhaul, M. J.; Marcelli, M. ; Tilley, W. D. ; Griffin, J. E. ; Wilson, J. D. Androgen resistance caused by mutations in the androgen receptor gene. FASEB J. 5: 2910‐2915; 1991.
Oleic Acid Metabolism via a Conserved Cytochrome P450 System-Mediated ω-Hydroxylation in the Bark Beetle-Associated Fungus Grosmannia clavigera

The bark beetle-associated fungus Grosmannia clavigera participates in the large-scale destruction of pine forests. In the tree, it must tolerate saturating levels of toxic conifer defense chemicals (e.g. monoterpenes). The fungus can metabolize some of these compounds through the ß-oxidation pathway and use them as a source of carbon. It also uses carbon from pine triglycerides, where oleic acid is the most common fatty acid. High levels of free fatty acids, however, are toxic and can cause additional stress during host colonization. Fatty acids induce expression of neighboring genes encoding a cytochrome P450 (CYP630B18) and its redox partner, cytochrome P450 reductase (CPR2). The aim of this work was to study the function of this novel P450 system. Using LC/MS, we biochemically characterized CYP630 as a highly specific oleic acid ω-hydroxylase. We explain oleic acid specificity using protein interaction modeling. Our results underscore the importance of ω-oxidation when the main ß-oxidation pathway may be overwhelmed by other substrates such as host terpenoid compounds. Because this CYP-CPR gene cluster is evolutionarily conserved, our work has implications for metabolism studies in other fungi.
Eye-Movement Strategies in Developmental Prosopagnosia and “Super” Face Recognition

Developmental prosopagnosia (DP) is a cognitive condition characterized by a severe deficit in face recognition. Few investigations have examined whether impairments at the early stages of processing may underpin the condition, and it is also unknown whether DP is simply the “bottom end” of the typical face-processing spectrum. To address these issues, we monitored the eye-movements of DPs, typical perceivers, and “super recognizers” (SRs) while they viewed a set of static images displaying people engaged in naturalistic social scenarios. Three key findings emerged: (a) Individuals with more severe prosopagnosia spent less time examining the internal facial region, (b) as observed in acquired prosopagnosia, some DPs spent less time examining the eyes and more time examining the mouth than controls, and (c) SRs spent more time examining the nose—a measure that also correlated with face recognition ability in controls. These findings support previous suggestions that DP is a heterogeneous condition, but suggest that at least the most severe cases represent a group of individuals that qualitatively differ from the typical population. While SRs seem to merely be those at the “top end” of normal, this work identifies the nose as a critical region for successful face recognition.
Structure development in thermoset recyclate‐filled polypropylene composites

Polypropylene containing comminuted fiber reinforced thermoset recyclate has been shown to exhibit enhanced mechanical properties relative to particulate-filled materials. Optimum mechanical performance in these recyclate-filled materials is achieved in compositions made from rubber-modified polypropylene containing maleic anhydride-modified PP in conjunction with silane coupling agent. Although matrix crosslinking was found to enhance properties in both filled and unfilled systems, composite properties are dominated by the development of strong interfacial bonding between polypropylene and recyclate reinforcement. A mechanism for the formation of interfacial bonding is proposed involving reaction between maleic anhydride functionalized PP, formation of trisilanol groups and their subsequent condensation with hydroxyl groups on the recyclate surfaces, together with molecular entanglement and co-crystallization of the grafted and ungrafted polypropylene molecules. Furthermore, in the absence of treatment there is evidence that the elastomer particles encapsulate the filler particles. However, this effect is strongly hindered when functionalized-PP is added, either in isolation or in combination with the silane co-treatment. The crystalline nucleation of PP by thermoset recyclate and treatment is also considered. The treatment system investigated was found to promote interfacial bonding to both the polyester (DMC) and woven glass-reinforced phenolic recyclates investigated, suggesting it may be suitable for treating mixed composite scrap.
Design of Intrusion Detection and Prevention System (IDPS) using DGSOTFC in collaborative protection networks

Intrusion Detection System (IDS) is the process of monitoring the events that occur in a system or network and process them for possible intrusions where as Intrusion Prevention System (IPS) has the capability to attempt to stop such possible intrusions. Combining the two systems will result in IDPS which not only detects the attacks but also prevent such attacks to occur in the networks. Distributed Denial of Service (DDOS) attacks are the major concern for security in the collaborative networks. Although non DDOS attacks are also make the network performances poor, the effect of DDOS attacks is severe. In DDOS attacks, flooding of the particular node as victim and jam it with massive traffic happens and the complete network performance is affected. In this paper, a novel Intrusion Detection and Prevention System is designed which detects the flooding DDOS attacks based on Firecol and prevents the attacks based on Dynamic Growing Self Organizing Tree (DGSOT) for collaborative networks. Simulation results show that DGSOT with Firecol (DGSOTFC) produces better intrusion detection and prevention system. Performance metrics based on the parameters delay and energy conservation are better in DGSOT-FC than the traditional IDPS systems.
Simulation Study on Optimization of Injection Molding Process for Thin-shell Plastic Parts via the Taguchi Method and Grey Relational Analysis

Abstract This paper deals with the application computer-aided engineering integrated with statistical techniques to reduce warpage variation due to injection molding process parameters during the production of thin-shell plastic components. For this purpose, Moldflow simulation runs are carried out by utilizing the combination of process parameters based on a three-level L18 orthogonal array table. An optimal parameter combination of the injection molding process is obtained via grey relational analysis (GRA). By analyzing the grey relational grade matrix, the degree of influence for each controllable process factor onto warpage can be found. Additionally, the analysis of variance (ANOVA) is also applied to identify the most significant factor; the melt temperature and the packing pressure are found to be the most significant factors in the simulation process for an injection molding process of thin-shell plastic parts.
Diffraction Gratings and Applications

A brief history of spectral analysis fundamental properties of gratings the types of diffraction gratings efficiency behaviour of plane reflection gratings transmission gratings icily gratings concave gratings surfacewaves and grating anomalies waveguide, fibber, an acousto-optic gratings review of electromagnetic theories of grating efficiencies testing of grating performance instrumental systems grating damage and control mechanical ruling of gratings holographic recording of gratings alternative methods of grating manufacture replication of gratings.
Induction of mitochondrial fusion by cysteine‐alkylators ethacrynic acid and N‐ethylmaleimide

Mitochondrial fusion and fission are important aspects of eukaryotic cell function that permit the adoption of varied mitochondrial morphologies depending upon cellular physiology. We previously observed that ethacrynic acid (EA) induced mitochondrial fusion in cultured BSC‐1 and CHO/wt cells. However, the mechanism responsible for it was not clear since EA has a number of known cellular effects including glutathione (GSH) depletion and alkylation of cysteine residues. To gain insight, we have tested the effects of a variety of compounds on EA induced cellular toxicity and mitochondrial fusion. N‐acetyl cysteine (NAC), a GSH precursor, was found to abrogate both the toxic and fusion‐inductive effects, whereas diethylmaleate (dEM), a GSH depletor, potentiated both these effects in a dose‐dependent manner. However, treatment with dEM alone, which depleted GSH to the same degree as EA, did not induce mitochondrial fusion. These results indicate that although detoxification of EA via formation of GSH conjugates is dependant upon GSH levels, the depletion of GSH by EA is not responsible for its effect on mitochondrial fusion. Dihydro‐EA (DH‐EA), a saturated EA analogue, lacked EA's toxicity and effect on fusion, indicating that the α,β‐unsaturated ketone is central to its observed effects. N‐ethylmaleimide (NEM), another well‐known cysteine‐alkylator, also induced mitochondrial fusion at near toxic concentrations. These data suggests that cysteine‐alkylation is the causative factor for fusion and toxicity. In live BSC‐1 cells, EA induced fusion of mitochondria occurred very rapidly (<20 min), which suggests that it is inducing fusion by modifying certain critical cysteine residue(s) in proteins involved in the process. © 2004 Wiley‐Liss, Inc.
Quantifying Audit Expectation Gap: A New approach to Measuring Expectation Gap

Abstract The main objective of the study is at first identifying the expectation gap about audit responsibility and the second quantifying the expectation gap in Iran. In order to collecting data, a questionnaire designed and developed between auditors and investors. Collected data analyzed by employing non-parametric statistics test. The results show that there is expectation gap between auditors and investors in Iran. The current study employed a new approach in the world in order to quantifying the expectation gap. It gives the more strength to other researchers in order to measuring audit expectation gap in the world.
Semi-supervised Statistical Inference for Business Entities Extraction and Business Relations Discovery

The sheer volume of user-contributed data on the Internet has motivated organizations to explore the collective business intelligence (BI) for improving business decisions making. One common problem for BI extraction is to accurately identify the entities being referred to in user-contributed comments. Although named entity recognition (NER) tools are available to identify basic entities in texts, there are still challenging research problems such as co-reference resolution and the identification of abbreviations of organization names. The main contribution of this paper is the illustration of a novel semi-supervised method for the identification of business entities (e.g., companies), and hence to automatically construct business networks. Based on the automatically discovered business networks, financial analysts can then predict the business prestige of companies for better financial investment decision making. Initial experiments show that the proposed NER method for business entity identification is more effective than other baseline methods. Moreover, the proposed semi-supervised business relationship extraction method is more effective than the state-ofthe-art supervised machine learning classifiers when there are not many training examples available. Our research work contributes to advance the computational methods for the extraction of entities and their relationships from texts.
Spin Burst Test of Carbon-Carbon Composite Disk

In order to establish design criteria for a turbine disk made of Carbon-Carbon (C/C) composites, spin burst tests were performed on quasi-isotropically laminated C/C composite disks. Un-notched and notched flat disks were prepared to evaluate the effect of stress concentration on fracture behavior. Strain measurements during rotation tests revealed that deformation of the C/C composite disks with or without notches was in agreement with analytical or finite element calculations in the elastic region, which suggests that the test fixtures of the spin tester have a negligible effect on the deformation of C/C composite disks. In the spin burst tests, the un-notched C/C composite disks burst at an average rotation speed of 25000 r.p.m. with a peripheral velocity of 393 m/s. A comparison between the mean hoop stress averaged over the radius and the tensile strength of smooth specimen suggests that the burst of the un-notched C/C composite disks obey the mean stress criterion. On the other hand, the fracture toughness criterion was shown to be applied for the notched specimen. The difference in the fracture criterion between the un-notched and the notched C/C composite disks is believed to correspond to that between the double edge notched specimens and specimens with a hole in the static tensile tests, (which was explained by the difference in the magnitude of the damaged zone where the stress concentrations would be relaxed).
Distributed Sensor Allocation for Multi-Target Tracking in Wireless Sensor Networks

In emerging tracking systems using large-scale wireless sensor networks, sensor management is an essential task in order to balance the tracking performance and costs subject to limited network resources in terms of energy, communication bandwidth, and sensing range. This paper considers the sensor allocation problem for multi-target tracking (MTT), in which a group of sensors are dynamically selected and allocated to track each of the multiple targets and collaborate within the group via track data fusion. The sensor assignments evolve over time as targets move, and are accomplished by solving a constrained optimization problem that is formulated to maximize the overall tracking performance for all targets, while conserving network energy and providing tracking coverage guarantee. The original integer-valued optimization problem is relaxed to a convex program for computational tractability, and then implemented in a distributed manner for network scalability and reduced communication costs. Through local one-hop communication with neighboring nodes, each sensor autonomously decides on whether to participate in data collection and how to contribute to track fusion. The proposed distributed sensor allocation algorithm, implemented via iterative subgradient search, is shown to converge to the global optimum of the centralized relaxed problem, and is near optimal for the original integer programming problem.
Robotic Motion Compensation for Respiratory Movement during Radiosurgery

Tumors in the chest and abdomen move during respiration. The ability of conventional radiation therapy systems to compensate for respiratory motion by moving the radiation source is inherently limited. Since safety margins currently used in radiation therapy increase the radiation dose by a very large amount, an accurate tracking method for following the motion of the tumor is of the utmost clinical relevance. We investigate methods to compensate for respiratory motion using robotic radiosurgery. Thus, the therapeutic beam is moved by a robotic arm, and follows the moving target tumor. To determine the precise position of the moving target, we combine infrared tracking with synchronized X-ray imaging. Infrared emitters are used to record the motion of the patient's skin surface. A stereo X-ray imaging system provides information about the location of internal markers. During an initialization phase (prior to treatment), the correlation between the motions observed by the two sensors (X-ray imaging and infrared tracking) is computed. This model is also continuously updated during treatment to compensate for other, non-respiratory motion. Experiments and clinical trials suggest that robot-based methods can substantially reduce the safety margins currently needed in radiation therapy.
An improved differential evolution algorithm with novel mutation strategy

As a modern Evolutionary Algorithm, Differential Evolution (DE) is usually criticized for its slow convergence when compared to Particle Swarm Optimization (PSO) on the PSO's benchmark functions. In this paper, by combing the merits of PSO and DE, we first present a new hybrid DE algorithm to accelerate its convergence speed. Then a novel mutation strategy with local and global search operators is proposed for balancing the exploration ability and the convergence rate of the improved DE. The new algorithm is applied to a set of benchmark test problems and compared with basic PSO and DE algorithms and their variants. The experimental results show the new algorithm shows better achievements on most test problems.
SAD2, an Importin β-Like Protein, Is Required for UV-B Response in Arabidopsis by Mediating MYB4 Nuclear Trafficking[W]

We report that the Arabidopsis thaliana mutant sensitive to ABA and drought2 (sad2), which harbors a T-DNA insertion in an importin β-like gene, is more tolerant to UV-B radiation than the wild type. Analysis of cyclobutane pyrimidine dimer accumulation revealed that less DNA damage occurred in sad2 than in the wild type during UV-B treatment. No significant growth difference was observed between sad2 and the wild type when treated with the genotoxic drug methyl methanesulfonate, suggesting that SAD2 functions in UV-B protection rather than in DNA damage repair. Whereas the R2R3-type transcription repressor MYB4 has previously been shown to negatively regulate the transcription of cinnamate 4-hydroxylase (C4H) and thus to regulate the synthesis of sinapate esters, expression of both MYB4 and C4H and accumulation of UV-absorbing compounds were significantly higher in sad2 than in the wild type. MYB4 did not localize to the nucleus in the sad2 mutant, suggesting that SAD2 is required for MYB4 nuclear trafficking. SAD2 and MYB4 coimmunoprecipitated, indicating that these proteins localize in the same complex in vivo. MYB4 protein specifically bound to its own promoter in gel shift assays and repressed its own expression, demonstrating that MYB4 protein and mRNA are part of a negative autoregulatory loop. This feedback loop is altered in the sad2 mutant due to the absence of MYB4 protein in the nucleus, leading to the constitutive expression of MYB4 and C4H and resulting in accumulation of UV-absorbing pigments that shield the plant from UV-B radiation.