Miracle-dz/UniFAR-FTUs huggingface dataset:
Key Protection for Private Computing on Public Platforms

Private Computing on Public Platforms (PCPP) is a new technology designed to enable secure and private execution of applications on remote, potentially hostile, public platforms. PCPP uses a host assessment to validate a host’s hardware and software configuration and then uses applied encryption techniques embedded in the operating system to isolate the protected application allowing its executable code, context
Classification of diabetic retinopathy using textural features in retinal color fundus image

Early, diagnosis is essential for diabetic patients to avoid partial or complete blindness. This work presents a new analysis method of texture features for classification of Diabetic Retinopathy (DR). The proposed method masks the blood vessels and optic disk segmented and directly extracts the textural features from the remaining retinal region. The proposed method is much simpler with comparison of the other methods that detect the defective regions first and then extract the required features for classification. The Haralick texture measures calculated are used for classification of DR. The proposed method is evaluated through a classification of DR using both Support Vector Machine (SVM) and Artificial Neural Network (ANN). The results of SVM have a better accuracy (87.5%) over ANN (79%). The performance of the proposed method is presented also in terms of sensitivity and specificity.
Adaptive exploration through covariance matrix adaptation enables developmental motor learning

The “Policy Improvement with Path Integrals” (PI2) [25] and “Covariance Matrix Adaptation — Evolutionary Strategy” [8] are considered to be state-of-the-art in direct reinforcement learning and stochastic optimization respectively. We have recently shown that incorporating covariance matrix adaptation into PI2- which yields the PICMA2 algorithm — enables adaptive exploration by continually and autonomously reconsidering the exploration/exploitation trade-off. In this article, we provide an overview of our recent work on covariance matrix adaptation for direct reinforcement learning [22–24], highlight its relevance to developmental robotics, and conduct further experiments to analyze the results. We investigate two complementary phenomena from developmental robotics. First, we demonstrate PICMA2’s ability to adapt to slowly or abruptly changing tasks due to its continual and adaptive exploration. This is an important component of life-long skill learning in dynamic environments. Second, we show on a reaching task how PICMA2 subsequently releases degrees of freedom from proximal to more distal limbs as learning progresses. A similar effect is observed in human development, where it is known as ‘proximodistal maturation’.
Investigating Bayesian robust experimental design with principles of global sensitivity analysis

The purpose of model-based experimental design is to maximise the information gathered for quantitative model identification. Instead of the commonly used optimal experimental design, robust experimental design aims to address parametric uncertainties in the design process. In this paper, the Bayesian robust experimental design is investigated, where both a Monte Carlo sampling strategy and local sensitivity evaluation at each sampling point are employed to achieve the robust solution. The link between global sensitivity analysis (GSA) and the Bayesian robust experimental design is established. It is revealed that a lattice sampling based GSA strategy, the Morris method, can be explicitly interpreted as the Bayesian A-optimal design for the uniform hypercube type uncertainties.
Intra- and Interspecies Conjugal Transfer of Tn916-Like Elements from Lactococcus lactis In Vitro and In Vivo

ABSTRACT Tetracycline-resistant Lactococcus lactis strains originally isolated from Polish raw milk were analyzed for the ability to transfer their antibiotic resistance genes in vitro, using filter mating experiments, and in vivo, using germfree rats. Four of six analyzed L. lactis isolates were able to transfer tetracycline resistance determinants in vitro to L. lactis Bu2-60, at frequencies ranging from 10−5 to 10−7 transconjugants per recipient. Three of these four strains could also transfer resistance in vitro to Enterococcus faecalis JH2-2, whereas no transfer to Bacillus subtilis YBE01, Pseudomonas putida KT2442, Agrobacterium tumefaciens UBAPF2, or Escherichia coli JE2571 was observed. Rats were initially inoculated with the recipient E. faecalis strain JH2-2, and after a week, the L. lactis IBB477 and IBB487 donor strains were introduced. The first transconjugants were detected in fecal samples 3 days after introduction of the donors. A subtherapeutic concentration of tetracycline did not have any significant effect on the number of transconjugants, but transconjugants were observed earlier in animals dosed with this antibiotic. Molecular analysis of in vivo transconjugants containing the tet(M) gene showed that this gene was identical to tet(M) localized on the conjugative transposon Tn916. Primer-specific PCR confirmed that the Tn916 transposon was complete in all analyzed transconjugants and donors. This is the first study showing in vivo transfer of a Tn916-like antibiotic resistance transposon from L. lactis to E. faecalis. These data suggest that in certain cases food lactococci might be involved in the spread of antibiotic resistance genes to other lactic acid bacteria.
Hybrid learning of large jigsaws

A jigsaw is a recently proposed generative model that describes an image as a composition of non-overlapping patches of varying shape, extracted from a latent image. By learning the latent jigsaw image which best explains a set of images, it is possible to discover the shape, size and appearance of repeated structures in the images. A challenge when learning this model is the very large space of possible jigsaw pixels which can potentially be used to explain each image pixel. The previous method of inference for this model scales linearly with the number of jigsaw pixels, making it unusable for learning the large jigsaws needed for many practical applications. In this paper, we make three contributions that enable the learning of large jigsaws -a novel sparse belief propagation algorithm, a hybrid method which significantly improves the sparseness of this algorithm, and a method that uses these techniques to make learning of large jigsaws feasible. We provide detailed analysis of how our hybrid inference method leads to significant savings in memory and computation time. To demonstrate the success of our method, we present experimental results applying large jigsaws to an object recognition task.
Secured Creditor Recovery Rates from Management Buy-Outs in Distress

"bankruptcy" ; "secured debt" ; "financial distress resolution" . JEL classification: "G"33 , "G"32 Fina ncial support from Deloitte & Touche, Barclays Private Equity and Lloyds/TSB is gratefully acknowledged. The paper has benefited from comments of Eric de Bodt and other participants at the European Financial Management Association (EFMA) 2002 meeting in London. Thanks also to Ameziane Lasfer, Maria Carapeto, Steve Toms and participants at staff seminars at Nottingham University Business School and the Judge Institute, Cambridge, for comments on an earlier version. Copyright Blackwell Publishing Ltd, 2003.
Ferroelectric materials for phased array applications

Ferroelectric materials have the unique property of variable dielectric constant with applied DC bias voltage. This property can, in principle, be used to develop phase shifters for phased arrays. However, the high dielectric constant and the high loss of pure ferroelectric materials have limited their applications in phased arrays. Improvements in ferroelectric composite materials prompted renewed interest in developing phase shifters at microwave frequencies. A typical phased array uses several thousand radiating elements and phase shifters, and hence it is very expensive. The Naval Research Laboratory (NRL) is developing a lens antenna that incorporates bulk phase shifting using ferroelectric material. The ferroelectrics were developed at the Army Research Laboratory (ARL); they are low-cost, bulk oxide-ceramic composites of barium strontium titanate oxide (BSTO). The lens approach reduces the number of phase shifters, phase shifter drivers and controls resulting in a low-cost phased array. This paper describes the ferroelectric lens concept and methods of using this lens in developing low-cost phased arrays. The properties of ferroelectric materials and the improvements at the ARL, in reducing the dielectric constant and the loss tangent are then discussed. This is followed by the presentation of the theoretical and experimental results and conclusions.
Cardiac Cycle Phase Detection using an Artificial Neural Network

this paper proposes a new hybrid approach to estimate the cardiac cycle phases in 2-D echo-cardiographic images as a first step in cardiac volume estimation. The cardiac cycle phase identification has done by making out the anatomical information of the heart with the dataset of both the normal and infant cardiac images of the heart. These dataset here helps in extracting the information about the given image and also differentiates them under the two categories either the heart left ventricle is in the diastolic state or systole state. For identifying this state the mitral valve position of the heart is considered. In the first step, the noise in the image was removed using the median filter and wavelet transforms for the canny edge detection as the second step. In third step, feature extraction, mean and the standard deviation value for the dataset images were calculated. To classify the two states of the heart, the feed forward back propagation neural network was used in the fourth scenario. By training the neural network, the images of the heart were classified into diastole and systole and could be measured in both the manner and compared.
Low-power simplex ultrasound communication for indoor localization

We propose an ultrasound communication system designed for time difference of arrival (TDOA) based indoor localization. The concept involves an infrastructure of stationary and independent senders tracking mobile receivers. The main goal is pure line-of-sight (LOS) communication for correct localization. When ignoring the reception energy of multipaths the transmission range is reduced to 20 meters and we need more devices to cover the same area (0.03 devices/m2). Thus, for cost-effectiveness and easy installation, we focus in the sender design on low power consumption for long battery or even energy independent operation. Moreover, we use the energy efficient π=4-DQPSK modulation technique to send 8 data bits in 3.5 ms. An identifier in each message along with the reception time can be used for TDOA localization. The frame synchronization error for a distance of 20m at 3 dB SNR is 11.2 ns. Thus, for speed of sound the distance measurement error is 3.7 μm.
The Role of Prosody in Russian Voicing 1

Though Russian voicing assimilation and final devoicing have received a great deal of attention in the literature on generative phonology, there are still basic aspects of the data that are not widely understood or agreed upon. They can be unified under the question, What role does prosody play in the Russian voicing facts? The answer given here will in some ways affirm the role of prosody in the Russian facts and in others exclude it. On the affirmative side, Russian voicing assimilation cannot be understood without reference to higher prosodic units such as the prosodic word. I will present an analysis of the word-level prosody of Russian inspired by Ito and Mester (2007; 2009; see also this volume) (an approach also pursued by Selkirk to appear), one which eschews the ‘Clitic Group’ and other categories apart from the Phonological Word and the Phonological Phrase. On the negative side, I argue that characterizing the triggers and targets of the voicing processes by means of syllable position cannot work for Russian; the account instead requires a cue-based approach, of the sort advocated by Steriade (1997).
ELAN development, keeping pace with communities’ needs

ELAN is a versatile multimedia annotation tool that is being developed at the Max Planck Institute for Psycholinguistics. About a decade ago it emerged out of a number of corpus tools and utilities and it has been extended ever since. This paper focuses on the efforts made to ensure that the application keeps up with the growing needs of that era in linguistics and multimodality research; growing needs in terms of length and resolution of recordings, the number of recordings made and transcribed and the number of levels of annotation per transcription.
Measurement-Based Admission Control in Wireless Sensor Networks

Wireless sensor networks have today emerged as a feasible infrastructure for healthcare applications. This paper addresses the non-trivial performance problems in contention based wireless networks. We present a method for admission control in contention-based networks, implemented as a component of a performance management system. The test results show that admission control can improve the predictability and level of performance in wireless sensor networks. The system can be used as a tool for dimensioning and configuration as well as for real-time admission control. The often unpredictable dynamics in contention-based access networks means that continuous performance control is needed to maintain a desired quality of service.
Airborne Ultrasound Pulse Force Device for Palpation Simulation

This research is investigating the use of acoustic radiation pressure from ultrasound emitters to produce tactile feedback in medical simulators. An initial application would be to simulate a pulse palpation where the trainee doctor or nurse actively searches for a pulse with their fingers to locate an artery within the body. Our first steps towards achieving this aim are summarised below.
L ∞ minimization in geometric reconstruction problems

We investigate the use of the L∞ cost function in geometric vision problems. This cost function measures the maximum of a set of model-fitting errors, rather than the sumof-squares, or L2 cost function that is commonly used (in least-squares fitting). We investigate its use in two problems; multiview triangulation and motion recovery from omnidirectionalcameras, though the results may also apply to other related problems. It is shown that for these problems the L∞ cost function is significantly simpler than the L2 cost. In particular L∞ minimization involvesfinding the minimum of a cost function with a single local (and hence global)minimumon a convexparameter domain. Theproblem may be recast as a constrained minimization problem and solved using commonly available software. The optimal solution was reliably achieved on problems of small dimension.
Alterations in antioxidant status during neonatal sepsis

Summary Septicaemia is a major threat to survival during the early stages of life. There are several reports that suggest that reactive oxygen species (ROs) play a role in a wide variety of diseases. We estimated the activity of xanthine oxidase (XO), malondialdehyde (MDA) content, creatine phosphokinase (CPK) activity, activities of key enzymatic antioxidants, such as superoxide dismutase (SOD), glutathione peroxidase (GPx) and peroxidase (PO), and non-enzymatic antioxidants, viz. uric acid (UA) and albumin (ALB), in 30 neonates with sepsis and 20 age-matched controls. The babies were categorized as preterm/term, early onset/late onset, and shock/without shock, as per clinical and laboratory investigations. The study was carried out to evaluate the status of antioxidant enzymes and non-enzymatic antioxidants with a view to suggesting the introduction of antioxidant therapy in neonatal sepsis. The activities of serum XO, CPK, SOD and GPx, and the content of MDA were found to be significantly elevated in the neonates with sepsis when compared with controls. Conversely, the activity of PO and the levels of UA and ALB were decreased. The septic, full-term neonates registered significantly higher CPK activity (70%) than the preterm septic neonates. However, infants with late-onset and shock sepsis had a significant decrease in CPK activity (p < 0.05) compared with their corresponding sub-groups. Likewise, UA levels were found to be 28% depressed (p <0.05) in the babies with late-onset sepsis and 51% increased (p < 0.001) in babies with shock compared with their respective sub-groups. Neonates with septic shock also registered a significant elevation in GPx activity (28%) compared with those without shock. This study suggests increased production of ROs in neonates with sepsis, as evidenced by the positive regulation of XO, SOD and GPx activity. The elevation of antioxidant enzymes, however, was not so effective as to protect from cellular damage and thereby result in higher MDA production. It is evident that antioxidant therapy might be useful in the management of neonates with sepsis but further detailed clinico-biochemical investigations are required to define effective antioxidant therapy.
Possibilities for examining the neural control of gait in humans with fNIRS

Although the existence of a central pattern generator system modulated by sensory information has become broadly accepted in the control of gait, many findings indicate that the cortex also plays a role of primary importance in human walking (Miyai et al., 2001; Gwin et al., 2011; Petersen et al., 2012). Examining the neural control of gait in humans requires recording cortical activity during gait. Direct evidence for cortical involvement in human locomotion comes from neuroimaging studies using position emission tomography (la Fougere et al., 2010), electroencephalography (EEG, Gwin et al., 2011) and functional near-infrared spectroscopy (fNIRS, Miyai et al., 2001) methods. Among possible neuroimaging methods, fNIRS is ideally suited to perform brain imaging during walking as it represents several advantages over other methods (Perrey, 2008).
Learning Discriminative Features from Spectrograms Using Center Loss for Speech Emotion Recognition

Identifying the emotional state from speech is essential for the natural interaction of the machine with the speaker. However, extracting effective features for emotion recognition is difficult, as emotions are ambiguous. We propose a novel approach to learn discriminative features from variable length spectrograms for emotion recognition by cooperating soft-max cross-entropy loss and center loss together. The soft-max cross-entropy loss enables features from different emotion categories separable, and center loss efficiently pulls the features belonging to the same emotion category to their center. By combining the two losses together, the discriminative power will be highly enhanced, which leads to network learning more effective features for emotion recognition. As demonstrated by the experimental results, after introducing center loss, both the unweighted accuracy and weighted accuracy are improved by over 3% on Mel-spectrogram input, and more than 4% on Short Time Fourier Transform spectrogram input.
The Barley stripe mosaic virus γb protein promotes chloroplast-targeted replication by enhancing unwinding of RNA duplexes

RNA viruses encode various RNA binding proteins that function in many steps of viral infection cycles. These proteins function as RNA helicases, methyltransferases, RNA-dependent RNA polymerases, RNA silencing suppressors, RNA chaperones, movement proteins, and so on. Although many of the proteins bind the viral RNA genome during different stages of infection, our knowledge about the coordination of their functions is limited. In this study, we describe a novel role for the Barley stripe mosaic virus (BSMV) γb as an enhancer of αa RNA helicase activity, and we show that the γb protein is recruited by the αa viral replication protein to chloroplast membrane sites of BSMV replication. Mutagenesis or deletion of γb from BSMV resulted in reduced positive strand (+) RNAα accumulation, but γb mutations abolishing viral suppressor of RNA silencing (VSR) activity did not completely eliminate genomic RNA replication. In addition, cis- or trans-expression of the Tomato bushy stunt virus p19 VSR protein failed to complement the γb replication functions, indicating that the direct involvement of γb in BSMV RNA replication is independent of VSR functions. These data support a model whereby two BSMV-encoded RNA-binding proteins act coordinately to regulate viral genome replication and provide new insights into strategies whereby double-stranded viral RNA unwinding is regulated, as well as formation of viral replication complexes.
Language choice in the earliest utterances: a case study with methodological implications

In this case study we investigate how early a developing bilingual exposed simultaneously to English and Spanish can make appropriate language choices. We propose two methodological requirements for studies of this kind: (1) detailed records of the development of the child's lexicon; and (2) data collection in more than one language context. Our own study relies on detailed records of the child's cumulative vocabulary from the first word at ten months, and on weekly audiovideo recordings in both English and Spanish contexts from age 1;3. Analysis of the data shows a strong tendency for the language of the child's utterances to match that of the context at ages 1;7–1;8.
Deformable Surface Augmentation in Spite of Self-Occlusions

The augmentation problem for images of a deforming surface has been studied since recently. The surface is usually assumed not to be self-occluding. Two dimensional deformation estimation in the presence of self-occlusions is very challenging. This paper proposes a specific framework explicitly modeling self-occlusions for augmented reality applications. The basic idea is to detect self-occlusions as warp shrinkage areas. Deformations are initially estimated via direct non-rigid image registration. Temporal smoothness is then used to refine the warps and the image are augmented. Experimental results on several challenging datasets show that our approach convincingly augments self-occluded surfaces. Associated videos are available on the first author's Web homepage.
Analyzing and improving clustering based sampling for microprocessor simulation

We propose a set of statistical metrics for making a comprehensive, fair, and insightful evaluation of features, clustering algorithms, and distance measures in representative sampling techniques for microprocessor simulation. Our evaluation of clustering algorithms using these metrics shows that CLARANS clustering algorithm produces better quality clusters in the feature space and more homogeneous phases for CPI compared to the popular k-means algorithm. We also propose a new micro-architecture independent data locality based feature, reuse distance distribution (RDD), for finding phases in programs, and show that the RDD feature consistently results in more homogeneous phases than basic block vector (BBV) for many SPEC CPU2000 benchmark programs.
Higher-order Nonnegative CANDECOMP/PARAFAC Tensor Decomposition Using Proximal Algorithm

Tensor decomposition is a powerful tool for analyzing multiway data. Nowadays, with the fast development of multisensor technology, more and more data appear in higher-order (order > 4) and nonnegative form. However, the decomposition of higher-order nonnegative tensor suffers from poor convergence and low speed. In this study, we propose a new nonnegative CANDECOM/PARAFAC (NCP) model using proximal algorithm. The block principal pivoting method in alternating nonnegative least squares (ANLS) framework is employed to minimize the objective function. Our method can guarantee the convergence and accelerate the computation. The results of experiments on both synthetic and real data demonstrate the efficiency and superiority of our method.