Miracle-dz/UniFAR-FTUs huggingface dataset:
Structural optimization for nonlinear dynamic response

Much is known about the nonlinear resonant response of mechanical systems, but methods for the systematic design of structures that optimize aspects of these responses have received little attention. Progress in this area is particularly important in the area of micro-systems, where nonlinear resonant behaviour is being used for a variety of applications in sensing and signal conditioning. In this work, we describe a computational method that provides a systematic means for manipulating and optimizing features of nonlinear resonant responses of mechanical structures that are described by a single vibrating mode, or by a pair of internally resonant modes. The approach combines techniques from nonlinear dynamics, computational mechanics and optimization, and it allows one to relate the geometric and material properties of structural elements to terms in the normal form for a given resonance condition, thereby providing a means for tailoring its nonlinear response. The method is applied to the fundamental nonlinear resonance of a clamped–clamped beam and to the coupled mode response of a frame structure, and the results show that one can modify essential normal form coefficients by an order of magnitude by relatively simple changes in the shape of these elements. We expect the proposed approach, and its extensions, to be useful for the design of systems used for fundamental studies of nonlinear behaviour as well as for the development of commercial devices that exploit nonlinear behaviour.
Filtering with State-Observation Examples via Kernel Monte Carlo Filter

This letter addresses the problem of filtering with a state-space model. Standard approaches for filtering assume that a probabilistic model for observations (i.e., the observation model) is given explicitly or at least parametrically. We consider a setting where this assumption is not satisfied; we assume that the knowledge of the observation model is provided only by examples of state-observation pairs. This setting is important and appears when state variables are defined as quantities that are very different from the observations. We propose kernel Monte Carlo filter, a novel filtering method that is focused on this setting. Our approach is based on the framework of kernel mean embeddings, which enables nonparametric posterior inference using the state-observation examples. The proposed method represents state distributions as weighted samples, propagates these samples by sampling, estimates the state posteriors by kernel Bayes’ rule, and resamples by kernel herding. In particular, the sampling and resampling procedures are novel in being expressed using kernel mean embeddings, so we theoretically analyze their behaviors. We reveal the following properties, which are similar to those of corresponding procedures in particle methods: the performance of sampling can degrade if the effective sample size of a weighted sample is small, and resampling improves the sampling performance by increasing the effective sample size. We first demonstrate these theoretical findings by synthetic experiments. Then we show the effectiveness of the proposed filter by artificial and real data experiments, which include vision-based mobile robot localization.
Free-Form B-spline Deformation Model for Groupwise Registration.

In this work, we extend a previously demonstrated entropy based groupwise registration method to include a free-form deformation model based on B-splines. We provide an efficient implementation using stochastic gradient descents in a multi-resolution setting. We demonstrate the method in application to a set of 50 MRI brain scans and compare the results to a pairwise approach using segmentation labels to evaluate the quality of alignment. Our results indicate that increasing the complexity of the deformation model improves registration accuracy significantly, especially at cortical regions.
Open vocabulary speech recognition with flat hybrid models

Today’s speech recognition systems are able to recognize arbitrary sentences over a large but finite vocabulary. However, many important speech recognition tasks feature an open, constantly changing vocabulary. (E.g. broadcast news transcription, translation of political debates, etc. Ideally, a system designed for such open vocabulary tasks would be able to recognize arbitrary, even previously unseen words. To some extent this can be achieved by using sub-lexical language models. We demonstrate that, by using a simple flat hybrid model, we can significantly improve a well-optimized state-ofthe-art speech recognition system over a wide range of out-of-vocabulary rates.
Soluble CD14 participates in the response of cells to lipopolysaccharide

CD14 is a 55-kD protein found both as a glycosylphosphatidyl inositol- linked protein on the surface of mononuclear phagocytes and as a soluble protein in the blood. CD14 on the cell membrane (mCD14) has been shown to serve as a receptor for complexes of lipopolysaccharide (LPS) with LPS binding protein, but a function for soluble CD14 (sCD14) has not been described. Here we show that sCD14 enables responses to LPS by cells that do not express CD14. We have examined induction of endothelial-leukocyte adhesion molecule 1 expression by human umbilical vein endothelial cells, interleukin 6 secretion by U373 astrocytoma cells, and cytotoxicity of bovine endothelial cells. None of these cell types express mCD14, yet all respond to LPS in a serum-dependent fashion, and all responses are completely blocked by anti-CD14 antibodies. Immunodepletion of sCD14 from serum prevents responses to LPS, and the responses are restored by addition of sCD14. These studies suggest that a surface anchor is not needed for the function of CD14 and further imply that sCD14 must bind to additional proteins on the cell surface to associate with the cell and transduce a signal. They also indicate that sCD14 may have an important role in potentiating responses to LPS in cells lacking mCD14.
A new nu-support vector machine for training sets with duplicate samples

Analyzed theoretically, /spl nu/-SVM was found to be over-dependent on each training sample, even if the samples have same value. This dependence would result in more time for training, more support vectors and more decision time. In order to overcome this problem, we propose a new /spl nu/-SVM. This new /spl nu/-SVM multiplies each slack variable in the objective function by a weight factor, and automatically computes each weight factor by the number of corresponding samples with same value before training. Theoretical analysis and the results of experiments show that the new /spl nu/-SVM has the same classification precision rate as the standard /spl nu/-SVM and the new /spl nu/-SVM is faster than the /spl nu/-SVM in training and decision if the training sets have same value samples.
POSTER: Improving NUMA System Efficiency with a Utilization-Based Co-scheduling

This work proposes a co-scheduling technique for co-located parallel applications on Non-Uniform Memory Access (NUMA) multi-socket multi-core platforms. The technique allocates core resources for running parallel applications such that both the utilization of the memory controllers and the CPU cores are maximized. Utilization is predicted using an online performance prediction model based on queuing systems. At runtime, the core allocation is periodically re-evaluated and cores are re-assigned to executing applications. Experimental results show that the proposed co-scheduling technique is able to execute co-located parallel applications in significantly less total execution time than the default Linux scheduler and a conventional scalability-based scheduler.
Cost-effectiveness analysis of mechanical thrombectomy in acute ischemic stroke.

OBJECT Mechanical thrombectomy is increasingly being used for the treatment of large-vessel ischemic stroke in patients who arrive outside of the 3-hour tissue plasminogen activator time window. In this study, the authors evaluated the cost and effectiveness of mechanical thrombectomy compared with standard medical therapy in patients who are ineligible to receive tissue plasminogen activator. METHODS Clinical outcomes of an open-label study of mechanical thrombectomy were compared with a hypothetical control group with a lower recanalization rate (18 vs 60%) and a lower rate of symptomatic intracranial hemorrhage (0.6 vs 7.8%) than the active treatment group. A Markov cost-effectiveness model was built to compare the health benefits and costs associated with mechanical thrombectomy compared with standard medical therapy. All probabilities, quality-of-life factors, and costs were estimated from the published literature. Univariate sensitivity analyses were performed to assess how variations in model parameters affect health and economic outcomes. RESULTS Treatment of acute ischemic stroke with mechanical thrombectomy increased survival time by 0.54 quality-adjusted life years (QALYs), compared with standard medical therapy (2.37 vs 1.83 QALYs), at an increased cost of $6600. This yielded an incremental cost-effectiveness ratio (ICER) of $12,120 per QALY gained, a value generally considered cost-effective. Sensitivity analysis showed that mechanical thrombectomy remained cost-effective (ICER < $50,000 per QALY gained) for all model inputs varied over a reasonable range, except for age at stroke treatment. For patients older than 82 years of age, the treatment was only borderline cost-effective (ICER of $50,000-100,000 per QALY gained). CONCLUSIONS The treatment of large-vessel ischemic stroke with mechanical thrombectomy appears to be costeffective. These results require validation when data from a randomized, controlled trial of mechanical thrombectomy become available.
Determination of the primitive karyotype for Peromyscus.

In order to determine the most probable primitive karyotype for the Peromyscus maniculatus complex and for the genus Peromyscus , G- and C-band data for P. floridanus and P. gossypinus are analyzed and compared to data available for other species. It is concluded that the primitive karyotype for the genus is characterized by chromosomes with heterochromatin restricted to centromeric regions. Heterochro-matic short arms appear to have been added independently to several Peromyscus lineages, suggesting that heterochromatin provides some selective advantage in this genus. When autosomes are identified according to the standardized karyotype for Peromyscus (Committee, 1977), the primitive karyotype is biarmed for pairs 1, 22, and 23; pairs 2, 3, 6, and 9 may or may not be biarmed, and all other pairs are acrocentric. Based on the trend in the P. maniculatus group for orthoselection, which increases the number of biarmed elements, it is hypothesized that chromosomes 2, 3, 6, and possibly 9 are acrocentric in the primitive karyotype and that P. crinitus , with only chromosomes 1, 22, and 23 biarmed, has retained an essentially primitive karyotype for the genus. The euchromatic long arm of the X chromosome has remained stable throughout all species examined, although heterochromatic short arms on the X are quite variable. Y chromosomes are biarmed and C-band positive in all species examined, but position of the centromere varies. Chromosome banding data support the hypothesis of Bowers et al. (1973) that the primitive karyotype for the P. maniculatus group is like that of P. melanotis with 30 acrocentric elements. C-bands reveal that heterochromatin in the primitive karyotype of the P. maniculatus group was confined to the centromeric regions. The primitive karyotype for this group has pairs 1, 2, 3, 6, 9, 20, 22, and 23 biarmed and all other pairs acrocentric. Peromyscus floridanus (subgenus Podomys ) shares what are believed to be derived chromosomal inversions with members of the subgenus Peromyscus , suggesting evolutionary affinities between these subgenera. Chromosomal banding data appear to have considerable promise as a tool for determining clades within Peromyscus .
Nonparametric Discriminant Analysis for Face Recognition

In this paper, we develop a new framework for face recognition based on nonparametric discriminant analysis (NDA) and multi-classifier integration. Traditional LDA-based methods suffer a fundamental limitation originating from the parametric nature of scatter matrices, which are based on the Gaussian distribution assumption. The performance of these methods notably degrades when the actual distribution is Non-Gaussian. To address this problem, we propose a new formulation of scatter matrices to extend the two-class nonparametric discriminant analysis to multi-class cases. Then, we develop two more improved multi-class NDA-based algorithms (NSA and NFA) with each one having two complementary methods based on the principal space and the null space of the intra-class scatter matrix respectively. Comparing to the NSA, the NFA is more effective in the utilization of the classification boundary information. In order to exploit the complementary nature of the two kinds of NFA (PNFA and NNFA), we finally develop a dual NFA-based multi-classifier fusion framework by employing the over complete Gabor representation to boost the recognition performance. We show the improvements of the developed new algorithms over the traditional subspace methods through comparative experiments on two challenging face databases, Purdue AR database and XM2VTS database.
Vagus nerve stimulation: An evolving adjunctive treatment for cardiac disease

The vagus nerve is a major component of the autonomic nervous system and plays a critical role in many body functions including for example, speech, swallowing, heart rate and respiratory control, gastric secretion, and intestinal motility. Vagus nerve stimulation (VNS) refers to any technique that stimulates the vagus nerve, with electrical stimulation being the most important. Implantable devices for VNS are approved therapy for refractory epilepsy and for treatment-resistant depression. In the case of heart disease applications, implantable VNS has been shown to be beneficial for treating heart failure in both preclinical and clinical studies. Adverse effects of implantable VNS therapy systems are generally associated with the implantation procedure or continuous on-off stimulation. The most serious implantation-associated adverse effect is infection. The effectiveness of non-invasive transcutaneous VNS for epilepsy, depression, primary headaches, heart failure, and other conditions remains under investigation. VNS merits further study for its potentially favorable effects on cardiovascular disease, especially heart failure.
Scalable consistency checking between diagrams - the VIEWINTEGRA approach

The Unified Modeling Language (UML) supports a wide range of diagrams for modeling software development concerns. UML diagrams are independent but connected; their meta-model describes them under a common roof. Despite the advances of UML, we found that the problem of ensuring consistency between UML diagrams has not been solved. We have developed an approach for automated consistency checking, called VIEWINTEGRA. Our approach provides excellent support for active (preventive) and passive (detective) consistency checking. We make use of consistent transformation to translate diagrams into interpretations and we use consistency comparison to compare those interpretations to other diagrams. Our approach was applied to a number of applications where we found the separation of transformation and comparison to be highly beneficial in addressing consistency-checking scalability and usability issues. The paper introduces our UML-based transformation framework, discusses how it aids comparison, and demonstrates how it improves consistency checking.
Deterministic design for neural network learning: an approach based on discrepancy

The general problem of reconstructing an unknown function from a finite collection of samples is considered, in case the position of each input vector in the training set is not fixed beforehand but is part of the learning process. In particular, the consistency of the empirical risk minimization (ERM) principle is analyzed, when the points in the input space are generated by employing a purely deterministic algorithm (deterministic learning). When the output generation is not subject to noise, classical number-theoretic results, involving discrepancy and variation, enable the establishment of a sufficient condition for the consistency of the ERM principle. In addition, the adoption of low-discrepancy sequences enables the achievement of a learning rate of O(1/L), with L being the size of the training set. An extension to the noisy case is provided, which shows that the good properties of deterministic learning are preserved, if the level of noise at the output is not high. Simulation results confirm the validity of the proposed approach.
Face Recognition Using Unlabeled Data

Los sistemas de reconocimiento de rostros normalmente obtienen buenos resultados cuando tienen disponibles conjuntos de entrenamiento grandes. Sin embargo, cuando no hay un conjunto de entrenamiento grande disponible, su desempeno no es satisfactorio. En este trabajo presentamos un metodo para reconocimiento de rostros que obtiene buenos resultados cuando solo se tiene disponible un conjunto de entrenamiento pequeno (incluso una sola imagen por persona). El metodo se basa en expandir el conjunto de entrenamiento original usando datos no etiquetados previamente (esto es, imagenes de rostros con identidad desconocida). Inicialmente, aplicamos la tecnica de eigenrostros para reducir la dimensionalidad del espacio de atributos, despues realizamos un proceso iterativo, clasificando todos los datos no etiquetados con un ensamble de clasificadores construido a partir del conjunto de entrenamiento actual y agregando al conjunto de entrenamiento los ejemplos que han sido clasificados correctamente con un alto nivel de confianza, de acuerdo al ensamble. Realizamos experimentos usando ensambles basados en el algoritmo de k vecinos mas cercanos, redes neuronales artificiales, y regresion lineal localmente ponderada. Los resultados experimentales demuestran que el uso de datos no etiquetados mejora la clasificacion en todos los casos. Los mejores resultados, con un porcentaje de clasificacion correcta de 92.07, fueron obtenidos con regresion lineal localmente ponderada usando 30 eigenrostros y agregando 3 ejemplos de cada clase en cada iteracion. Como comparacion, usando unicamente los datos etiquetados, solo se clasificaron correctamente el 34.81% de los ejemplos
Predicting species distribution and abundance responses to climate change: why it is essential to include biotic interactions across trophic levels

Current predictions on species responses to climate change strongly rely on projecting altered environmental conditions on species distributions. However, it is increasingly acknowledged that climate change also influences species interactions. We review and synthesize literature information on biotic interactions and use it to argue that the abundance of species and the direction of selection during climate change vary depending on how their trophic interactions become disrupted. Plant abundance can be controlled by aboveground and belowground multitrophic level interactions with herbivores, pathogens, symbionts and their enemies. We discuss how these interactions may alter during climate change and the resulting species range shifts. We suggest conceptual analogies between species responses to climate warming and exotic species introduced in new ranges. There are also important differences: the herbivores, pathogens and mutualistic symbionts of range-expanding species and their enemies may co-migrate, and the continuous gene flow under climate warming can make adaptation in the expansion zone of range expanders different from that of cross-continental exotic species. We conclude that under climate change, results of altered species interactions may vary, ranging from species becoming rare to disproportionately abundant. Taking these possibilities into account will provide a new perspective on predicting species distribution under climate change.
Multifractal analysis of human retinal vessels

In this paper, it is shown that vascular structures of the human retina represent geometrical multifractals, characterized by a hierarchy of exponents rather then a single fractal dimension. A number of retinal images from the STARE database are analyzed, corresponding to both normal and pathological states of the retina. In all studied cases, a clearly multifractal behavior is observed, where capacity dimension is always found to be larger then the information dimension, which is in turn always larger then the correlation dimension, all the three being significantly lower then the diffusion limited aggregation (DLA) fractal dimension. We also observe a tendency of images corresponding to the pathological states of the retina to have lower generalized dimensions and a shifted spectrum range, in comparison with the normal cases
Translocation and rotation of microtubules caused by multiple species of Chlamydomonas inner-arm dynein

Dynein was extracted from outer arm-less axonemes of the mutant oda1 and fractionated by high-pressure liquid chromatography on a MonoQ column into seven distinct subspecies (named a-g). Each subspecies contained one or two heavy chains and several medium-sized and light chains; by vanadate/UV-induced photocleavage and SDS- polyacrylamide gel electrophoresis, eight distinct heavy chains were identified. Analysis of the mutant axonemes indicated that the subspecies f (containing two heavy chains) is missing in the inner-arm mutant ida1 and the subspecies a, c and d are missing in the mutant ida4. Six subspecies (all but f) supported microtubule translocation with the maximal rate ranging from 2 to 12 micrometre s-1 and the apparent Km for ATP ranging from about 10 to 100 micromolar. All the subspecies translocated microtubules with the plus end leading, indicating that all the inner-arm dyneins are minus end-directed motors. Five subspecies (all but b and f) displayed microtubule rotation during translocation at rates of up to about 10 Hz. Unexpectedly, the Km values for ATP for translocation and rotation did not always agree; because of this, the pitch of the movement was variable with some subspecies. These observations indicate that axonemes are equipped with several inner-arm subspecies and that torque generation is a feature common to many of them.
Performance analysis of DS-CDMA using different chaotic sequences

Spread spectrum communication is one of the key technologies utilized to prevent disruption of communication and interception of signal by unintended receivers. Security of communication depends on the spreading sequences which require low probability of interception (LPI). Chaotic spreading sequences have low probability of interception. This paper compares the performance of various spreading sequences for Direct Sequence Spread Spectrum communication (DS-CDMA) such as Logistic chaotic, Modified Bernoulli chaotic, Logistic Bernoulli chaotic and gold sequences in terms of Bit Error Rate (BER), cross correlation and Multiple Access Interference (MAI). A Method to reduce cross correlation of generated chaotic sequence is presented.
Reading Rate and Comprehension.

Step One: Situate the reading in the context of the course You will be able to approach the reading in a more informed way if you understand how it fits with the goals of the course and if you have a clear sense of why you are doing the reading in the first place. Ask yourself: • Why did the instructor want us to read this? • What did the professor say about the reading in class or on the syllabus? • How does this reading fit with other material in the course?