Miracle-dz/UniFAR-FTUs huggingface dataset:
A Tractable Fault Detection and Isolation Approach for Nonlinear Systems With Probabilistic Performance

This article presents a novel perspective along with a scalable methodology to design a fault detection and isolation (FDI) filter for high dimensional nonlinear systems. Previous approaches on FDI problems are either confined to linear systems or they are only applicable to low dimensional dynamics with specific structures. In contrast, shifting attention from the system dynamics to the disturbance inputs, we propose a relaxed design perspective to train a linear residual generator given some statistical information about the disturbance patterns. That is, we propose an optimization-based approach to robustify the filter with respect to finitely many signatures of the nonlinearity. We then invoke recent results in randomized optimization to provide theoretical guarantees for the performance of the proposed filer. Finally, motivated by a cyber-physical attack emanating from the vulnerabilities introduced by the interaction between IT infrastructure and power system, we deploy the developed theoretical results to detect such an intrusion before the functionality of the power system is disrupted.
Homogeneity in Web Search Results

Access to diverse perspectives nurtures an informed citizenry. Google and Bing have emerged as the duopoly that largely arbitrates which English-language documents are seen by web searchers. We present our empirical study over the search results produced by Google and Bing that shows a large overlap. Thus, citizens may not gain different perspectives by simultaneously probing them for the same query. Fortunately, our study also shows that by mining Twitter data, one can obtain search results that are quite distinct from those produced by Google, Bing, and Bing News. Additionally, the users found those results to be quite informative. We also present two novel tools we designed for this study. One uses tensor analysis to derive low-dimensional compact representation of search results and study their behavior over time. The other uses machine learning and quantifies the similarity of results between two search engines by framing it as a prediction problem. Although these tools have different underpinnings, the analytical results obtained using them corroborate each other, which reinforces the confidence one can place in them for finding meaningful insights from big data.
Human group activity recognition based on modelling moving regions interdependencies

In this research study, we model the interdependency of actions performed by people in a group in order to identify their activity. Unlike single human activity recognition, in interacting groups the local movement activity is usually influenced by the other persons in the group. We propose a model to describe the discriminative characteristics of group activity by considering the relations between motion flows and the locations of moving regions. The inputs of the proposed model are jointly represented in time-space and time-movement spaces. These spaces are modelled using Kernel Density Estimation (KDE) which is then fed into a machine learning classifier. Unlike in other group-based human activity recognition algorithms, the proposed methodology is automatic and does not rely on any pedestrian detection or on the manual annotation of tracks.
Empirical Analysis of the Relationship between FDI and Processing Trade: 1985-2008

Processing trade has gradually replaced the general trade, becoming China’s main trade pattern. Then, what’s the relationship between FDI and processing trade This paper, based on VAR model, by employing Granger causality test approach, impulse response function and variance decomposition, gives an empirical analysis and test of the dynamic correlation between FDI and processing trade. The empirical result denotes that there exists bilateral Granger causality relationship between FDI and processing trade. For a short period, the impact of FDI on the responsiveness of processing trade is more intense. The impact of processing trade on the responsiveness of FDI is also more intense and the volatility of fluctuations is larger; for a long period, there exists a long-run stable relationship between FDI and processing trade.
EEG error potentials detection and classification using time-frequency features for robot reinforcement learning

In thought-based steering of robots, error potentials (ErrP) can appear when the action resulting from the brain-machine interface (BMI) classifier/controller does not correspond to the user's thought. Using the Steady State Visual Evoked Potentials (SSVEP) techniques, ErrP, which appear when a classification error occurs, are not easily recognizable by only examining the temporal or frequency characteristics of EEG signals. A supplementary classification process is therefore needed to identify them in order to stop the course of the action and back up to a recovery state. This paper presents a set of time-frequency (t-f) features for the detection and classification of EEG ErrP in extra-brain activities due to misclassification observed by a user exploiting non-invasive BMI and robot control in the task space. The proposed features are able to characterize and detect ErrP activities in the t-f domain. These features are derived from the information embedded in the t-f representation of EEG signals, and include the Instantaneous Frequency (IF), t-f information complexity, SVD information, energy concentration and sub-bands' energies. The experiment results on real EEG data show that the use of the proposed t-f features for detecting and classifying EEG ErrP achieved an overall classification accuracy up to 97% for 50 EEG segments using 2-class SVM classifier.
Asymmetric gravitational lenses in TeVeS and application to the bullet cluster

Aims. We explore the lensing properties of asymmetric matter density distributions in Bekenstein's tensor-vector-scalar theory (TeVeS). Methods. Using an iterative Fourier-based solver for the resulting non-linear scalar field equation, we numerically calculate the total gravitational potential and derive the corresponding TeVeS lensing maps. Results. Considering variations on rather small scales, we show that the lensing properties significantly depend on the lens' extent along the line of sight. Furthermore, all simulated TeVeS convergence maps strongly track the dominant baryonic components; non-linear effects, being capable of counteracting this trend, turn out to be very small. Setting up a toy model for the cluster merger 1E0657- 558, we infer that TeVeS cannot explain observations without assuming an additional dark mass component in both cluster centers, which is in accordance with previous work.
Robust Image Retargeting via Axis‐Aligned Deformation

We propose the space of axis‐aligned deformations as the meaningful space for content‐aware image retargeting. Such deformations exclude local rotations, avoiding harmful visual distortions, and they are parameterized in 1D. We show that standard warping energies for image retargeting can be minimized in the space of axis‐aligned deformations while guaranteeing that bijectivity constraints are satisfied, leading to high‐quality, smooth and robust retargeting results. Thanks to the 1D parameterization, our method only requires solving a small quadratic program, which can be done within a few milliseconds on the CPU with no precomputation overhead. We demonstrate how the image size and the saliency map can be changed in real time with our approach, and present results on various input images, including the RetargetMe benchmark. We compare our results with six other algorithms in a user study to demonstrate that the space of axis‐aligned deformations is suitable for the problem at hand.
Weight-loss control sampling for the training of boosted pedestrian detectors

When we apply AdaBoost in pedestrian detection, a large number of examples are needed to train a detector. Except for designing features, a reasonable utilization of training examples is also significant to the detection accuracy and training time. In this paper, we propose a new method, named Weight-Loss Control Sampling (WLCS), to deal with the negative training examples by improving the training process of AdaBoost. The WLCS updates the negative training set by sampling hard examples from original negative set. It determines when to implement sampling process by the weight-loss of the negative training set. And we reduce the capacity of negatives after each sampling to accelerate the training. In this paper, we implement experiments to prove that the WLCS can train a better classifier in a short training time via a specific pedestrian detection method.
Inactive and Alone: Physical Activity Within the First 14 Days of Acute Stroke Unit Care

Background and Purpose— One way that stroke units may improve outcome is by reducing complications of immobility through early mobilization; however, this intervention needs testing. The purpose of this study was to determine the physical activity patterns of stroke patients managed within acute stroke units as a first step in developing an early mobilization protocol. Methods— We recruited 64 patients within 14 days after stroke from 5 metropolitan stroke units and observed them for 2 consecutive days at 10-minute intervals between the hours of 8 am and 5 pm. At each observation, we ascertained physical activity, location, and other person(s) present. Therapists recorded therapy details. Results— The 58 patients who completed the study had a mean age of 71.3 years. Stroke severity ranged from mild (National Institutes of Health Stroke Scale score, 1) to severe (score, 27), and mean time after stroke at observation was 5.6 days (range, 0 to 13 days). Only 9 patients were restricted to bed. During the therapeutic day, patients spent >50% resting in bed, 28% sitting out of bed, and only 13% engaged in activities with the potential to prevent complications and improve recovery of mobility. Patients were alone >60% of the time. Conclusions— This is the first multicenter study of physical activity early after stroke. We believe the next step is to conduct a randomized controlled trial to evaluate the effect of increased levels of physical activity early after stroke compared with current standards of care.
MULTIVARIATE HYPOTHESIS TESTING OF DTI DATA FOR TISSUE CLUSTERING

In this work we investigate the feasibility and effectiveness of unsupervised tissue clustering and classification algorithms for DTI data. Tissue clustering and classification are among the most challenging tasks in DT image analysis. While clustering separates acquired data into objects, tissue classification provides in-depth information about each region of interest. The unsupervised clustering algorithm utilizes a framework proposed by Hext and Snedecor, where the null hypothesis of diffusion tensors arising from the same distribution is determined by an F-test. Tissue type is classified according to one of three possible diffusion models (general anisotropic, prolate, or oblate), which is determined with a parsimonious model selection framework. This approach, also adapted from Snedecor, chooses among different models of diffusion within a voxel using a series of F-tests. Both numerical phantoms and DWI data obtained from excised rat spinal cord are used to test and validate these tissue clustering and classification approaches.
Towards Automatic Personalized Content Generation for Platform Games

In this paper, we show that personalized levels can be auto- matically generated for platform games. We build on previ- ous work, where models were derived that predicted player experience based on features of level design and on playing styles. These models are constructed using preference learn- ing, based on questionnaires administered to players after playing different levels. The contributions of the current pa- per are (1) more accurate models based on a much larger data set; (2) a mechanism for adapting level design parameters to given players and playing style; (3) evaluation of this adap- tation mechanism using both algorithmic and human players. The results indicate that the adaptation mechanism effectively optimizes level design parameters for particular players.
EF-Tu dynamics during pre-translocation complex formation: EF-Tu·GDP exits the ribosome via two different pathways

The G-protein EF-Tu, which undergoes a major conformational change when EF-Tu·GTP is converted to EF-Tu·GDP, forms part of an aminoacyl(aa)-tRNA·EF-Tu·GTP ternary complex (TC) that accelerates the binding of aa-tRNA to the ribosome during peptide elongation. Such binding, placing a portion of EF-Tu in contact with the GTPase Associated Center (GAC), is followed by GTP hydrolysis and Pi release, and results in formation of a pretranslocation (PRE) complex. Although tRNA movement through the ribosome during PRE complex formation has been extensively studied, comparatively little is known about the dynamics of EF-Tu interaction with either the ribosome or aa-tRNA. Here we examine these dynamics, utilizing ensemble and single molecule assays employing fluorescent labeled derivatives of EF-Tu, tRNA, and the ribosome to measure changes in either FRET efficiency or fluorescence intensity during PRE complex formation. Our results indicate that ribosome-bound EF-Tu separates from the GAC prior to its full separation from aa-tRNA, and suggest that EF-Tu·GDP dissociates from the ribosome by two different pathways. These pathways correspond to either reversible EF-Tu·GDP dissociation from the ribosome prior to the major conformational change in EF-Tu that follows GTP hydrolysis, or irreversible dissociation after or concomitant with this conformational change.
Static Power Optimization Using Dual Sub-Threshold Supply Voltages in Digital CMOS VLSI Circuits

Power dissipation in high performance systems requires more expensive packaging. In this situation, low power VLSI design has assumed great importance as an active and rapidly developing field. As the density and operating speed of CMOS VLSI chip increases, static power dissipation becomes more significant. This is due to the leakage current when the transistor is off this is threshold voltage dependent. This can be observed in the combinational and sequential circuits. Static power reduction techniques are achieved by means of operating the transistor either in Cut-off or in Saturation region completely and avoiding the clock in unnecessary circuits. In this work, “Dual sub-threshold voltage supply” technique is used to operate the transistor under off state or either in on state by applying some voltage at the gate of the MOS transistor. This static power reduction technique is to digital circuits, so that the power dissipation is reduced and the performance of the circuit is increased. The designed circuits can be simulated by using Mentor Graphics Backend Tool.
Expression and polarized localization of the hemochromatosis gene product HFE in retinal pigment epithelium.

PURPOSE Hereditary hemochromatosis is an autosomal recessive disorder of iron overload leading to oxidative stress. Mutations in HFE are responsible for approximately 90% of cases of this disease. HFE is the principal regulator of iron homeostasis, and the process involves interaction with transferrin receptor (TfR)-1, transferrin receptor (TfR)-2, and beta2-microglobulin (beta2M). Expression of HFE has not been investigated in the retina. In the present study, the expression of HFE and the HFE-interacting proteins TfR1, TfR2, and beta2M were analyzed in mouse retina. METHODS RT-PCR was used to detect the expression of HFE mRNA in neural retina and the RPE-eyecup. Expression of HFE in intact retina was investigated by in situ hybridization, immunofluorescence, and immunogold electron microscopy. Expression of HFE-interacting proteins was also analyzed using similar techniques. RESULTS RT-PCR showed predominant expression of HFE mRNA in the RPE-eyecup. In situ hybridization in intact retina revealed that HFE mRNA is expressed almost exclusively in RPE Immunofluorescence and immunogold electron microscopy showed that HFE protein was specifically associated with the basolateral membrane of RPE. Expression of the HFE-interacting proteins TfR1, TfR2, and beta2M was also evident in the retina. CONCLUSIONS This is the first report on the expression of HFE in the retina. The specific localization of HFE and its interacting proteins, TfR1 and TfR2, at the basolateral membrane of RPE is relevant to the regulation of iron homeostasis in this cell. Patients with hemochromatosis may have impairment of iron homeostasis in RPE, potentially contributing to age-related RPE dysfunction and retinal degeneration.
Packet classification in large ISPs: design and evaluation of decision tree classifiers

Packet classification, although extensively studied, is an evolving problem. Growing and changing needs necessitate the use of larger filters with more complex rules. The increased complexity and size pose implementation challenges on current hardware solutions and drive the development of software classifiers, in particular, decision-tree based classifiers. Important performance measures for these classifiers are time and memory due to required high throughput and use of limited fast memory.We analyze Tier 1 ISP data that includes filters and corresponding traffic from over a hundred edge routers and thousands of interfaces. We provide a comprehensive view on packet classification in an operational network and glean insights that help us design more effective classification algorithms.We propose and evaluate decision tree classifiers with common branches. These classifiers have linear worst-case memory bounds and require much less memory than standard decision tree classifiers, but nonetheless, we show that on our data have similar average and worst-case time performance. We argue that common-branches exploit structure that is present in real-life data sets.We observe a strong Zipf-like pattern in the usage of rules in a classifier, where a very small number of rules resolves the bulk of traffic and most rules are essentially never used. Inspired by this observation, we propose traffic-aware classifiers that obtain superior average-case and bounded worst-case performance. Good average-case can boost performance of software classifiers that can be used in small to medium sized routers and are also important for traffic analysis and traffic engineering.
Loss of heterozygosity at the calcium regulation gene locus on chromosome 10q in human pancreatic cancer.

BACKGROUND Loss of heterozygosity (LOH) on chromosomal regions is crucial in tumor progression and this study aimed to identify genome-wide LOH in pancreatic cancer. MATERIALS AND METHODS Single-nucleotide polymorphism (SNP) profiling data GSE32682 of human pancreatic samples snap-frozen during surgery were downloaded from Gene Expression Omnibus database. Genotype console software was used to perform data processing. Candidate genes with LOH were screened based on the genotype calls, SNP loci of LOH and dbSNP database. Gene annotation was performed to identify the functions of candidate genes using NCBI (the National Center for Biotechnology Information) database, followed by Gene Ontology, INTERPRO, PFAM and SMART annotation and UCSC Genome Browser track to the unannotated genes using DAVID (the Database for Annotation, Visualization and Integration Discovery). RESULTS The candidate genes with LOH identified in this study were MCU, MICU1 and OIT3 on chromosome 10. MCU was found to encode a calcium transporter and MICU1 could encode an essential regulator of mitochondrial Ca2+ uptake. OIT3 possibly correlated with calcium binding revealed by the annotation analyses and was regulated by a large number of transcription factors including STAT, SOX9, CREB, NF-kB, PPARG and p53. CONCLUSIONS Global genomic analysis of SNPs identified MICU1, MCU and OIT3 with LOH on chromosome 10, implying involvement of these genes in progression of pancreatic cancer.
Clinical Guidelines Versus Universal Molecular Testing: Are We Ready to Choose an Optimal Strategy for Lynch Syndrome Identification?

Lynch syndrome, also known as hereditary non-polyposis colorectal cancer (HNPCC), is the most common form of inherited colorectal cancer (CRC). Affected individuals need to undergo intensive CRC surveillance, screening for associated cancers, and possibly prophylactic surgery. Clinically-based guidelines have been used as the basis for Lynch syndrome screening in CRC patient populations. More recently, it has been argued that the universal molecular testing strategies should be implemented to increase the identification of patients who should get germline testing for Lynch syndrome. In this issue of American Journal of Gastroenterology, Julie et al. compare the performance of clinical guidelines with a molecular strategy based on universal microsatellite instability (MSI) testing for identifying CRC patients who have Lynch syndrome. Although there is insufficient evidence to support universal molecular testing for all CRC patients at the current time, the study highlights the need for a systematic approach to identify patients with Lynch syndrome. Physicians and health care systems need to do a better job of identifying patients and families with early-onset of CRC and/or a consistent cancer family history so that they may undergo appropriate molecular evaluation, genetic counseling, and cancer risk management.
Using Multivariate Regression Methods to Resolve Overlapped Electrochemical Signals

This paper proposes the application of Gaussian process regression (GPR) as an alternative regression model to resolve the hard overlapped electrochemical signals belonging to the 2,4,6trichlorophenol/2,6-dichlorophenol (TCP/DCP) system. Gaussian process derives from the perspective of Bayesian non-parametric regression methods, in terms of the parameterization of the covariance function, results in its good performance for the development of a calibration model for both linear and non-linear data sets. The multivariate regression model developed by GPR was compared with some traditional regression methods such as partial least squares regression (PLSR), and support vector regression (SVR). The comparative results were satisfied. The satisfactory results obtained through GPR method suggest that it can be used as a more effective and promising tool for multivariate regression tasks than the others.
Polymer nanocomposites for structural applications: Recent trends and new perspectives

ABSTRACT Due to their outstanding mechanical, thermal, and durability properties, polymer matrix nanocomposites (PMCs) are currently a prominent area of research. The opportunity of applying PMCs in structural reinforcement and rehabilitation of damaged infrastructures, as well as working as a new structural material, justifies the increasing number of recent studies. In this review article, the effect of adding different reinforcements at nano-scale, such as carbon nanotubes, nanoclay, graphene, or nanosilica to polymer matrices, is discussed and the improvement in mechanical properties of PMCs is evaluated. Some concluding remarks and new perspectives on the use of PMCs in structures are given.