Miracle-dz/UniFAR-FTUs huggingface dataset:
Parallel proximal support vector machine for high-dimensional pattern classification

Proximal support vector machine (PSVM) is a simple but effective classifier, especially for solving large-scale data classification problems. An inherent deficiency of PSVM lies on its inefficiency for dealing with high-dimensional data. In this paper, we propose a parallel version of PSVM (PPSVM). Based on random dimensionality partitioning, PPSVM can obtain partitioned local model parameters in parallel, with combined parameters to form the final global solution. In fact, PPSVM enjoys two properties: 1) It can calculate model parameters in parallel and is therefore a fast learning method with theoretically proved convergence; and 2) It can avoid the inversion of large matrix, which makes it suitable for high-dimensional data. In the paper, we also propose a random PPSVM with randomly partitioned data in each iteration to improve the performance of PSVM. Experimental results on real-world data demonstrate that the proposed methods can obtain similar or even better prediction accuracy than PSVM with much better runtime efficiency.
Relationship between jump landing kinematics and peak ACL force during a jump in downhill skiing: A simulation study

Recent data highlight that competitive skiers face a high risk of injuries especially during off‐balance jump landing maneuvers in downhill skiing. The purpose of the present study was to develop a musculo‐skeletal modeling and simulation approach to investigate the cause‐and‐effect relationship between a perturbed landing position, i.e., joint angles and trunk orientation, and the peak force in the anterior cruciate ligament (ACL) during jump landing. A two‐dimensional musculo‐skeletal model was developed and a baseline simulation was obtained reproducing measurement data of a reference landing movement. Based on the baseline simulation, a series of perturbed landing simulations (n = 1000) was generated. Multiple linear regression was performed to determine a relationship between peak ACL force and the perturbed landing posture. Increased backward lean, hip flexion, knee extension, and ankle dorsiflexion as well as an asymmetric position were related to higher peak ACL forces during jump landing. The orientation of the trunk of the skier was identified as the most important predictor accounting for 60% of the variance of the peak ACL force in the simulations. Teaching of tactical decisions and the inclusion of exercise regimens in ACL injury prevention programs to improve trunk control during landing motions in downhill skiing was concluded.
Energization of interstellar media and cosmic ray production by jets from X-ray binaries

Drawing on recent estimates of the power of jets from X-ray binary systems as a function of X-ray luminosity, combined with improved estimates of the relevant log(N )‐log(L X) luminosity functions, we calculate the total energy input to the interstellar medium (ISM) from these objects. The input of kinetic energy to the ISM via jets is dominated by those of the black hole systems, in contrast to the radiative input, which is dominated by accreting neutron stars. Summing the energy input from black hole jets LJ in the Milky Way, we find that it is likely to correspond to 1 per cent of LSNe, the time-averaged kinetic luminosity of supernovae, and 5 per cent of LCR, the cosmic ray luminosity. Given uncertainties in jet power estimates, significantly larger contributions are possible. Furthermore, in elliptical galaxies with comparable distributions of low mass X-ray binaries, but far fewer supernovae, the ratio L J/L SNe is likely to be larger by a factor of ∼5. We conclude that jets from X-ray binaries may be an important, distributed, source of kinetic energy for the ISM in the form of relativistic shocks, and as a result are likely to be a major source of cosmic rays. Ke yw ords: black hole physics ‐ binaries: close ‐ ISM: jets and outflows.
Voting on Multiattribute Domains with Cyclic Preferential Dependencies

In group decision making, often the agents need to decide on multiple attributes at the same time, so that there are exponentially many alternatives. In this case, it is unrealistic to ask agents to communicate a full ranking of all the alternatives. To address this, earlier work has proposed decomposing such voting processes by using local voting rules on the individual attributes. Unfortunately, the existing methods work only with rather severe domain restrictions, as they require the voters' preferences to extend acyclic CP-nets compatible with a common order on the attributes. We first show that this requirement is very restrictive, by proving that the number of linear orders extending an acyclic CP-net is exponentially smaller than the number of all linear orders. Then, we introduce a very general methodology that allows us to aggregate preferences when voters express CP-nets that can be cyclic. There does not need to be any common structure among the submitted CP-nets. Our methodology generalizes the earlier, more restrictive methodology. We study whether properties of the local rules transfer to the global rule, and vice versa. We also address how to compute the winning alternatives.
Autocorrelation method for high-quality time/pitch-scaling

A new method is described for high-quality time or pitch modifications of audio signals. The method is a simple but efficient improvement of the splice method. Thanks to its simplicity, the algorithm can be implemented to run in real-time on standard microprocessors. Informal listening tests have demonstrated the method's capability to modify high-quality audio signals without introducing audible artifacts for moderate modification factors (up to 15%). <<ETX>>
An Intrusion-Tolerant Mechanism for Intrusion Detection Systems

In accordance with the increasing importance of intrusion detection systems (IDS), users justifiably demand the trustworthiness of the IDS. However, sophisticated attackers attempt to disable the IDS before they launch a thorough attack. Therefore, to accomplish its function, an IDS should have some mechanism to guarantee uninterrupted detection service even in the face of IDS component failures due to attacks. In this paper, we propose an intrusion-tolerant mechanism for network intrusion detection systems (NIDS) that employ multiple independent components. The mechanism monitors the detection units and the hosts on which the units reside and enables the IDS to survive component failure due to intrusions. As soon as a failed IDS component is discovered, a copy of the component is installed to replace it and the detection service continues. We implement the intrusion-tolerant mechanism based on the CSI-KNN-based NIDS and evaluate the prototype in the face of component failures. The results demonstrate that the mechanism can effectively tolerate intrusions.
Energy-aware scheduling for infrastructure clouds

More and more data centers are built, consuming ever more kilo watts of energy. Over the years, energy has become a dominant cost factor for data center operators. Utilizing low-power idle modes is an immediate remedy to reduce data center power consumption. We use simulation to quantify the difference in energy consumption caused exclusively by virtual machine schedulers. Besides demonstrating the inefficiency of wide-spread default schedulers, we present our own optimized scheduler. Using a range of realistic simulation scenarios, our customized scheduler OptSched reduces cumulative machine uptime by up to 60.1%. We evaluate the effect of data center composition, run time distribution, virtual machine sizes, and batch requests on cumulative machine uptime. IaaS administrators can use our results to quickly assess possible reductions in machine uptime and, hence, untapped energy saving potential.
Speed-accuracy trade-off in dwell-based eye pointing tasks at different cognitive levels

In this paper, we present a target searching experiment to investigate how long is long enough to maintain the speed-accuracy trade-off in eye pointing tasks that use dwell time as the activation mechanism. The experimental task, which took account of three factors including cognitive complexity, dwell time and visual feedback mode, mixes visual search and target acquisition together. In other words, the subjects need to search for and recognize the target before the final selection in each trial. The results clarify the suitable ranges of dwell time for users to avoid wrong selections as possible as they can under different cognitive load conditions. We also discussed the implications for user interface designs.
A convergence analysis for pose graph optimization via Gauss-Newton methods

In this work we present a convergence analysis of the pose graph optimization problem, that arises in the context of mobile robots localization and mapping. The analysis is performed under some simplifying assumptions on the structure of the measurement covariance matrix and provides non trivial results on the aspects affecting convergence in nonlinear optimization based on Gauss-Newton methods. We also provide estimates for the basin of attraction of the maximum likelihood solution and results on the uniqueness of such solution. The results confirm observations of related work and explain why common Simultaneous Localization and Mapping (SLAM) instances are so well-behaved in terms of convergence. Moreover, as a by-product of the derivation, we present different techniques that can enlarge the convergence radius a-priori (i.e., during robot operation) or a-posteriori (i.e., given the data). We validate the theoretical derivation with experiments on standard benchmarking datasets.
Focal : An Eye-Tracking Musical Expression Controller

We present Focal , an eye-tracking musical expression controller which allows hands-free control over audio effects and synthesis parameters during performance. A see-through head-mounted display projects virtual dials and switches into the visual field. The performer controls these with a single expression pedal, switching context by glancing at the object they wish to control. This simple interface allows for minimal physical disturbance to the instrumental musician, whilst enabling the control of many parameters otherwise only achievable with multiple foot pedalboards. We describe the development of the system, including the construction of the eye-tracking display, and the design of the musical interface. We also present a comparison of a performance between Focal and conventional controllers.
Quantitative assessment of macroporous cell scaffold structures using optical coherence tomography (OCT)

We present the application of optical coherence tomography (OCT) for non-destructive imaging and quantitative characterization of the macroporous cyclic acetal hydrogel cell scaffolds. High-resolution OCT visualizes the microstructures of the tissue engineering scaffolds in three-dimension. The porosity, pore size, and the interconnectivity of the hydrogel scaffolds were characterized quantitatively using OCT.
Secure Anonymous Protocols for Local and Multicast Environments

The rapid increase in Internet users triggered a number of new Internet services and applications such as online shopping, video conferencing, Internet games or distance education. A larger part of those ones requires multicast support for efficient data distribution. A number of secure group communication protocols have been published recently, but the preservation of privacy of the single group member is still an unsolved problem. This paper presents a novel approach to secure and anonymous group communication. First, we propose a solution for anonymity in a local environment based on state-of-the art approaches such as pseudonym servers and anonymizers combined with encryption techniques on different protocol levels (network or application) in order to guarantee an anonymous way of communication between end-users. Then, we introduce the secure and anonymous multicast (SAM) framework and we show how it can be used as a configurable, scalable architecture in combination with local anonymity and hybrid authentication mechanisms.
Targeted Delivery System of Nanobiomaterials in Anticancer Therapy: From Cells to Clinics

Targeted delivery systems of nanobiomaterials are necessary to be developed for the diagnosis and treatment of cancer. Nanobiomaterials can be engineered to recognize cancer-specific receptors at the cellular levels and to deliver anticancer drugs into the diseased sites. In particular, nanobiomaterial-based nanocarriers, so-called nanoplatforms, are the design of the targeted delivery systems such as liposomes, polymeric nanoparticles/micelles, nanoconjugates, norganic materials, carbon-based nanobiomaterials, and bioinspired phage system, which are based on the nanosize of 1–100 nm in diameter. In this review, the design and the application of these nanoplatforms are discussed at the cellular levels as well as in the clinics. We believe that this review can offer recent advances in the targeted delivery systems of nanobiomaterials regarding in vitro and in vivo applications and the translation of nanobiomaterials to nanomedicine in anticancer therapy.
A Bayesian Approach to Biomedical Text Summarization

Automatic text summarization tools help biomedical researchers and clinicians to acquire intended information from various textual resources in shorter time and with less effort. Some of the biomedical text summarization systems put the basis of their sentence selection approach on the frequency of concepts extracted from the input text. However, it seems that exploring other measures rather than the frequency for identifying the valuable content of the input document, and considering the correlations existing between concepts can be more useful for this type of summarization. In this paper, we propose a biomedical text summarization method based on the na\"ive Bayes classifier. The input text is mapped to the Unified Medical Language System (UMLS) concepts, and the important ones are selected to be used as the classification features. We introduce different feature selection approaches to identify the most important concepts of the text and select the most informative content according to the distribution of these concepts. We show that with the use of an appropriate feature selection approach, our Bayesian biomedical summarizer can improve the performance of summarization. An extensive evaluation was performed on a collection of scientific papers in biomedical domain. The results show that our proposed method outperforms the biomedical summarizers that rely on the frequency of concepts, the domain-independent and baseline methods based on the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics. Moreover, the results suggest that using the meaningfulness measure and considering the correlations of concepts in the feature selection step yield a desirable summarization performance.
Wavelet Analysis of One-Dimensional Cosmological Density Fluctuations

Wavelet analysis is proposed as a new tool for studying the large·scale structure formation of the universe. To reveal its usefulness, the wavelet decomposition of a one-dimensional model of cos· mological density fluctuations is performed. In contrast with Fourier analysis, the wavelet analysis has the advantage of the ability to retain information concerning location of local density peaks in addition to that of their scales. The wavelet decomposition of evolving density fluctuations with various initial conditions is examined. We argue that the wavelet analysis is an alternative useful tool for data analysis as well as nonlinear analysis and may be usefully employed in the Sloan Digital Sky Survey and COBE analyses. Recent large-scale sky survey (e.g. CfA) reveals the fertile large-scale structure of the universe. l ) It is natural to consider this structure as a consequence of gravita­ tional instability from small-amplitude primordial fluctuations. The origin of the primordial fluctuations is usually ascribed to quantum fluctuations around the Planck time which are stretched to a scale larger than the Hubble horizon during the inflationary stage and classicalize due to decoherence. Although the above picture is yet rather speculative, it encourages us to explain the large scale structure of the present universe from a physical point of view. On the basis of the cosmological principle, the fairness of the observational data is assumed, and the results are statistically interpreted. Then it is important to determine the statistical properties of the various cosmic fields that can be used to describe the matter distribution and motion in the expanding universe. For this purpose, correlation functions are useful. Indeed, two-point correlation functions
High levels of dietary fat impair glucose homeostasis in rainbow trout

SUMMARY This study was designed to assess the effects of dietary fat levels on glucose homeostasis in rainbow trout under prolonged hyperglycaemia induced by high carbohydrate intake. Trout were fed identical amounts of one of two iso-energetic diets containing either a low (LFD, 3%) or a high fat level (HFD, 20%) and similar amounts of digestible carbohydrates (26–30%) for 14 days. While a single high fat meal reduced glycaemia compared with a low fat meal, the consumption of a high fat diet for 14 days resulted in prolonged hypergylcaemia and reduced plasma glucose clearance in response to an exogenous glucose or insulin challenge. The hyperglycaemic phenotype in trout was characterised by a reduction of the activities of lipogenic and glucose phosphorylating enzymes with a concomitant stimulation of enzymes involved in glucose production in the liver and reduced glycogen levels in the white muscle. Impaired glucose tolerance (IGT) was further associated with a significant reduction of insulin receptor substrate 1 (IRS1) protein content in muscle, and with a poor response of HFD fed fish to an exogenous insulin load, suggestive of impaired insulin signalling in trout fed with a HFD. To our knowledge, this is the first study showing that a teleost can also develop a high fat-induced IGT, characterised by persistent hyperglycaemia and reduced insulin sensitivity, established symptoms of IGT and the prediabetic insulin-resistant state in mammals. Our results also provide evidence that persistent hyperglycaemia after a high carbohydrate meal stems from a metabolic interaction between dietary macronutrients rather than from high carbohydrate intake alone.
Acoustic modeling and parameter generation using relevance vector machines for speech synthesis

In this paper, we propose a relevance vector machine (RVM) for modeling and generation of a speech feature sequence. In the conventional method, the mean parameter of the hidden Markov model (HMM) state can not consider temporal correlation among corresponding data frames. Since the RVM can be utilized to solve a nonlinear regression problem, we apply it to replace the model parameters of the state output distributions. In the proposed system, RVMs are employed to model the statistically representative process of the state or phone segment which is obtained from normalized training feature sequences by using the semi-parametric nonlinear regression method. We conducted comparative experiments for the proposed RVMs with conventional HMM. It is shown that the proposed state-level RVM-based method performed better than the conventional technique.
On the Measurement of Technological Progress Across Countries

We construct 14 alternative measures of technological progress for 19 OECD countries over the period 1970–2000, distinguishing between measures of productivity gains actually obtained in a given country (TFP growth, Malmquist index) and technological progress at the world technology frontier (potential TFP growth, the “frontier shift” index). We then compare these measures according to a range of characteristics, shedding light on some of their relative weaknesses and strengths. We find that these characteristics are sensitive to the precision of estimates of the world technology frontier, and then we demonstrate that this precision can be increased substantially by allowing for imperfect substitutability between unskilled and skilled labor and using US state-level data apart from cross-country data for estimating the world technology frontier. Because none of the 14 measures dominates all others on all dimensions, we conclude that the choice of appropriate measurement method should be suited to the question addressed in each particular study.
Information theory meets circuit design: Why capacity-approaching codes require more chip area and power

It is generally thought that good codes, i.e. codes that operate at rates close to capacity and attain low error probabilities, are sophisticated constructions that require high encoding and decoding circuit power. In this paper, we rigorously show that this intuition is correct by deriving an information-theoretic lower bound on power consumption for encoding circuits using communication-complexity techniques. We first lower bound the “VLSI complexity” — measured as the product Awiresl2 where Awires is the wire-area and l is the number of clock cycles in implementation — for encoding. Using the lower bound on VLSI complexity, we derive a lower bound on power consumption of any fully-parallel encoding implementation for any code, and show that the consumed power must diverge to infinity as the error probability approaches zero. Further, the speed of this divergence increases as the rate approaches channel capacity. We also provide a refinement of an earlier result on VLSI complexity by El Gamal, Greene and Pang, which derives a lower bound on Achipl2, where Achip is the entire chip area required for encoding.
A new supervised local modelling classifier based on information theory

In this paper, a novel supervised architecture for binary classification based on local modelling and information theory is described. The architecture is composed of two steps: in the first one, a separating borderline between the two classes is piecewise constructed by a set of centroids calculated by a modified clustering algorithm, based on information theory; each of these centroids define a region where, in the second step of the proposed architecture, a hyperplane is constructed and adjusted by means of one-layer neural networks. This new method allows for binary classification while maintaining adequate use of computational resources, a common problem for machine learning methods. The proposed architecture is applied over classical benchmark classification problems and data sets, and its results are compared with those obtained by other well-known statistical and machine learning classifiers.
Recognising Cortical Sulci and Gyri in MR Images

We describe an automatic technique for segmenting the outer cortical surface from axial MR images. The scalp outline defines a search area within which the cortex can be expected to lie. The image is sampled radially and a special purpose edge-detector is used to create an initial estimate of the cortical surface. This estimate is then relaxed under the control of local edge constraints. The cortex outline then guides a search for sulci. A physical description of the sulci and gyri of the cortex is derived, which allows a 3D reconstruction of the cortical surface and its sulci to be displayed.