Miracle-dz/UniFAR-FTUs huggingface dataset:
A robust automatic clustering scheme for image segmentation using wavelets

The optimal features with which to discriminate between regions and, thus, segment an image often differ depending on the nature of the image. Many real images are made up of both smooth and textured regions and are best segmented using different features in different areas. A scheme that automatically selects the optimal features for each pixel using wavelet analysis is proposed, leading to a robust segmentation algorithm. An automatic method for determining the optimal number of regions for segmentation is also developed.
Recognition of Human Behavior for Assisted Living Using Dictionary Learning Approach

The ambient assisted living (AAL) technology aims to provide more safety and self-sufficiency, while permitting older persons to live self-dependently in their homes. Monitoring of activities of daily living (ADL) is one of the key ideas of AAL. This becomes an interesting research idea in modern world, where condition monitoring of various ADLs and their automatic classification is a big challenge. This paper proposes a new approach for activity recognition of motion primitives relying on the sparse representation of signals, where signals are represented using a sparse combination of atoms from an over-complete dictionary. This paper intends to investigate the suitability of applying dictionary learning algorithms like K-singular value decomposition (K-SVD), which is usually used to construct an over-complete dictionary, for the effective progress of the ADL monitoring system. This paper proposes to formulate the classification approach by using SRC classifiers, based on the dictionaries learned using K-SVD algorithm. We have validated our proposed approach on a publicly available ADL data set of wrist-worn accelerometer sensor for activity recognition. Performance evaluations demonstrate that the proposed method outperforms several other competing methods.
Comparison of arterial input functions measured from ultra-fast dynamic contrast enhanced MRI and dynamic contrast enhanced computed tomography in prostate cancer patients

The purpose of this study was to evaluate the accuracy of arterial input functions (AIFs) measured from dynamic contrast enhanced (DCE) MRI following a low dose of contrast media injection. The AIFs measured from DCE computed tomography (CT) were used as ‘gold standard’. A total of twenty patients received CT and MRI scans on the same day. Patients received 120 ml Iohexol in DCE-CT and a low dose of (0.015 mM kg−1) of gadobenate dimeglumine in DCE-MRI. The AIFs were measured in the iliac artery and normalized to the CT and MRI contrast agent doses. To correct for different temporal resolution and sampling periods of CT and MRI, an empirical mathematical model (EMM) was used to fit the AIFs first. Then numerical AIFs (AIFCT and AIFMRI) were calculated based on fitting parameters. The AIFMRI was convolved with a ‘contrast agent injection’ function (AIFMRICON) to correct for the difference between MRI and CT contrast agent injection times (~1.5 s versus 30 s). The results show that the EMMs accurately fitted AIFs measured from CT and MRI. There was no significant difference (p > 0.05) between the maximum peak amplitude of AIFs from CT (22.1 ± 4.1 mM/dose) and MRI after convolution (22.3 ± 5.2 mM/dose). The shapes of the AIFCT and AIFMRICON were very similar. Our results demonstrated that AIFs can be accurately measured by MRI following low dose contrast agent injection.
Secrecy rate maximization for MIMO wiretap channels with a cooperative jammer using alternating optimization

In this paper, we study the secrecy rate maximization (SRM) problem for a multiple-input-multiple-output (MIMO) secrecy channel, where a multi-antenna cooperative jammer is employed to improve secret communication in the presence of multiple multi-antenna eavesdroppers. The SRM problem are not jointly convex in terms of the transmit covariance matrices of the legitimate transmitter and the cooperative jammer. To solve the non-convexity issues, we propose an alternating optimization (AO) approach to design the transmit covariance matrix of the legitimate transmitter and the cooperative jammer. Simulation results validated the performance of the proposed algorithms.
The Black Falcon: a teleoperated surgical instrument for minimally invasive surgery

This paper presents the Black Falcon, an eight degree-of-freedom teleoperator slave with a dextrous wrist for minimally invasive surgery (MIS). We show how teleoperation can address several key problems in MIS by increasing dexterity and degrees of freedom, by giving the surgeon some force feedback to feel instrument-tissue interactions and by eliminating geometric discrepancies between actual and observed tool motions. We discuss relevant design constraints, summarize the mechanism design and give data showing the quality of force reflection achieved. We demonstrate suturing along arbitrarily oriented suture lines in animal tissue, a task essentially impossible using current instruments.
Bayesian nonparametric inference for panel count data with an informative observation process

In this paper, the panel count data analysis for recurrent events is considered. Such analysis is useful for studying tumor or infection recurrences in both clinical trial and observational studies. A bivariate Gaussian Cox process model is proposed to jointly model the observation process and the recurrent event process. Bayesian nonparametric inference is proposed for simultaneously estimating regression parameters, bivariate frailty effects, and baseline intensity functions. Inference is done through Markov chain Monte Carlo, with fully developed computational techniques. Predictive inference is also discussed under the Bayesian setting. The proposed method is shown to be efficient via simulation studies. A clinical trial dataset on skin cancer patients is analyzed to illustrate the proposed approach.
Fast and Ultrafast Time‐Resolved Mid‐Infrared Spectrometry Using Lasers

The sections in this article are Introduction Fast TRIR Spectroscopy Fast TRIR Spectroscopy Using IR Lasers CW CO Lasers CW CO2 Lasers Tunable IR Diode Lasers Color Center Lasers Difference Frequency Generation Ultrafast TRIR Studies Two-Dimensional IR Spectroscopy Conclusions
Effects of Well-Controlled HIV Infection on Complement Activation and Function

Introduction:Uncontrolled HIV infection is known to activate the complement system, leading to an increase in chronic inflammation. Whether or not this activation of complement persists and contributes to chronic inflammation in subjects with HIV infection that is well controlled through use of antiretroviral therapy has not been studied. Methods:We conducted an observational, cross-sectional study using sera from 305 adults with well-controlled HIV infection and 30 healthy controls. Sera was tested for markers of complement activation (C3a and C5a levels), complement function (CH50 assay), and immunoglobulin levels (IgG1–IgG4) as IgG can activate complement. We evaluated the association of well-controlled HIV infection with C3a, C5a, CH50, IgG1–IgG4, and total IgG levels using both univariate and multivariate analyses, controlling for factors such as age, sex, race, comorbidities (including hepatitis C coinfection), smoking status, and statin use. Results:Well-controlled HIV infection was associated with a 54% increase in complement activation as measured by C3a levels compared with healthy controls (P < 0.0001). Hepatitis C coinfection was associated with a further 52% increase in complement activation, as measured by C3a levels, over HIV alone (P = 0.003). Conclusion:These results suggest that complement activation may contribute to a proinflammatory state even in well-controlled HIV infection. Furthermore, hepatitis C virus coinfection may be even more proinflammatory, in complement activation, compared with HIV infection alone.
A Bayesian Hidden Markov Model for Motif Discovery Through Joint Modeling of Genomic Sequence and ChIP‐Chip Data

Summary We propose a unified framework for the analysis of chromatin (Ch) immunoprecipitation (IP) microarray (ChIP‐chip) data for detecting transcription factor binding sites (TFBSs) or motifs. ChIP‐chip assays are used to focus the genome‐wide search for TFBSs by isolating a sample of DNA fragments with TFBSs and applying this sample to a microarray with probes corresponding to tiled segments across the genome. Present analytical methods use a two‐step approach: (i) analyze array data to estimate IP‐enrichment peaks then (ii) analyze the corresponding sequences independently of intensity information. The proposed model integrates peak finding and motif discovery through a unified Bayesian hidden Markov model (HMM) framework that accommodates the inherent uncertainty in both measurements. A Markov chain Monte Carlo algorithm is formulated for parameter estimation, adapting recursive techniques used for HMMs. In simulations and applications to a yeast RAP1 dataset, the proposed method has favorable TFBS discovery performance compared to currently available two‐stage procedures in terms of both sensitivity and specificity.
A low-complexity noncoherent MAP detector for turbo-coded unitary space-time modulation scheme

In non-coherent multiple-input multiple-output (MIMO) systems, the conventional noncoherent MAP detector is too complex to be performed if the number of labelling bits conveyed within one single coherence block is high. In order to overcome this difficulty, in this paper, we propose a noncoherent MAP (maximum a posteriori) detector with very low complexity for orthogonally designed USTM (unitary space-time modulation) schemes. In addition, we show that the detected soft values are independent of the extrinsic information fed back from the turbo decoder. As a result, iterative detection and decoding between the noncoherent detector and the turbo decoder can be avoided without performance degradation. Compared to the scheme using the exhaustive-search-based noncoherent MAP detector, the proposed scheme achieves the same error performance, and has a much lower detection complexity.
Using linguistically defined specific details to detect deception across domains

Abstract Current automatic deception detection approaches tend to rely on cues that are based either on specific lexical items or on linguistically abstract features that are not necessarily motivated by the psychology of deception. Notably, while approaches relying on such features can do well when the content domain is similar for training and testing, they suffer when content changes occur. We investigate new linguistically defined features that aim to capture specific details, a psychologically motivated aspect of truthful versus deceptive language that may be diagnostic across content domains. To ascertain the potential utility of these features, we evaluate them on data sets representing a broad sample of deceptive language, including hotel reviews, opinions about emotionally charged topics, and answers to job interview questions. We additionally evaluate these features as part of a deception detection classifier. We find that these linguistically defined specific detail features are most useful for cross-domain deception detection when the training data differ significantly in content from the test data, and particularly benefit classification accuracy on deceptive documents. We discuss implications of our results for general-purpose approaches to deception detection.
Multimedia simplification for optimized MMS synthesis

We propose a novel transcoding technique called multimedia simplification which is based on experiential sampling. Multimedia simplification helps optimize the synthesis of MMS (multimedia messaging service) messages for mobile phones. Transcoding is useful in overcoming the limitations of these compact devices. The proposed approach aims at reducing the redundancy in the multimedia data captured by multiple types of media sensors. The simplified data is first stored into a gallery for further usage. Once a request for MMS is received, the MMS server makes use of the simplified media from the gallery. The multimedia data is aligned with respect to the timeline for MMS message synthesis. We demonstrate the use of the proposed techniques for two applications, namely, soccer video and home care monitoring video. The MMS sent to the receiver can basically reflect the gist of important events of interest to the user. Our technique is targeted towards users who are interested in obtaining salient multimedia information via mobile devices.
Software intelligence: the future of mining software engineering data

Mining software engineering data has emerged as a successful research direction over the past decade. In this position paper, we advocate Software Intelligence (SI) as the future of mining software engineering data, within modern software engineering research, practice, and education. We coin the name SI as an inspiration from the Business Intelligence (BI) field, which offers concepts and techniques to improve business decision making by using fact-based support systems. Similarly, SI offers software practitioners (not just developers) up-to-date and pertinent information to support their daily decision-making processes. SI should support decision-making processes throughout the lifetime of a software system not just during its development phase. The vision of SI has yet to become a reality that would enable software engineering research to have a strong impact on modern software practice. Nevertheless, recent advances in the Mining Software Repositories (MSR) field show great promise and provide strong support for realizing SI in the near future. This position paper summarizes the state of practice and research of SI, and lays out future research directions for mining software engineering data to enable SI.
A comparative study of event-related coupling patterns during an auditory oddball task in schizophrenia

Objective. The aim of this research is to explore the coupling patterns of brain dynamics during an auditory oddball task in schizophrenia (SCH). Approach. Event-related electroencephalographic (ERP) activity was recorded from 20 SCH patients and 20 healthy controls. The coupling changes between auditory response and pre-stimulus baseline were calculated in conventional EEG frequency bands (theta, alpha, beta-1, beta-2 and gamma), using three coupling measures: coherence, phase-locking value and Euclidean distance. Main results. Our results showed a statistically significant increase from baseline to response in theta coupling and a statistically significant decrease in beta-2 coupling in controls. No statistically significant changes were observed in SCH patients. Significance. Our findings support the aberrant salience hypothesis, since SCH patients failed to change their coupling dynamics between stimulus response and baseline when performing an auditory cognitive task. This result may reflect an impaired communication among neural areas, which may be related to abnormal cognitive functions.
High-resolution transmission electron microscopy of heat-treated C60 nanotubes

Fullerene nanowhiskers and nanotubes are single crystals consisting of C60 molecules with a high aspect ratio of length to diameter. In this study, C60 nanowhiskers and nanotubes were heat-treated at various temperatures up to 3000 °C. The structural characteristics of the heated samples were examined using transmission electron microscopy (TEM) and Raman spectroscopy. By the high-temperature heat treatment, the C60 nanotubes turned to glassy carbon composed of randomly oriented ribbons with graphitic layers with an interlayer spacing which is close to that of the (002) planes of graphite.
Microsystems for cellular force measurement: a review

Microsystems are providing key advances in studying single cell mechanical behavior. The mechanical interaction of cells with their extracellular matrix is fundamentally important for cell migration, division, phagocytosis and aptoptosis. This review reports the development of microsystems on studying cell forces. Microsystems provide advantages of studying single cells since the scale of cells is on the micron level. The components of microsystems provide culture, loading, guiding, trapping and on chip analysis of cellular mechanical forces. This paper gives overviews on how MEMS are advancing in the field of cell biomechno sensory systems. It presents different materials, and mode of studying cell mechanics. Finally, we comment on the future directions and challenges on the state of art techniques.
Mimicking voice recognition using MFCC-GMM framework

Evaluating the quality of mimicked speech has started more attention nowadays since it may affect speaker verification system as in spoof attack. In this paper, mel frequency cepstral coefficients (MFCC) are effectively utilized for evaluating the quality of text independent mimicked speech. Gaussian mixture model (GMM) based classifier is used in the classification phase to make decision based on the log-likelihood score. The experiment evaluates the competence of 5 artists in mimicking 5 target speakers and rank them according to the scores of a classifier. This is tested against the mean score obtained through the perception test conducted on 20 listeners. If the artist with highest mean score is identified as rank-1 by the proposed system, a hit occurs. The results show the promise of MFCC in evaluating voice mimicking performance.
Durability and Microstructure of Ultra-High Performance Concrete Having High Volume of Steel Slag Powder and Ultra-Fine Fly Ash

The durability, such as chloride ion permeability, freeze-thaw and sulfate attack resistance of ultra-high performance concrete (UHPC) having a large amount, ranged from 42% to 48% by weight of binder, of mineral admixtures including steel slag powder (SS), ultrafine fly ash (UFFA) and silica fume (SF) was studied and the microstructure of selected UHPC compositions was investigated by Mercury porosimetry in this paper. Moreover, the relationship between durability and microstructure of UHPC was analyzed. The mercury porosimetry studies demonstrated the very low porosity and a high proportion of the innocuous pores as well as the very small pore size in UHPCs, whose most probable pore diameter did not exceed 10 nm. This porous structure of UHPCs would definitely enable the material have excellent durability.
Dose and Time‐Dependent Effects of Cyanide on Thiosulfate Sulfurtransferase, 3‐Mercaptopyruvate Sulfurtransferase, and Cystathionine λ‐Lyase Activities

We assessed the dose‐dependent effect of potassium cyanide (KCN) on thiosulfate sulfurtransferase (TST), 3‐mercaptopyruvate sulfurtransferase (3‐MPST), and cystathionine λ‐lyase (CST) activities in mice. The time‐dependent effect of 0.5 LD50 KCN on cyanide level and cytochrome c oxidase (CCO), TST, 3‐MPST, and CST activities was also examined. Furthermore, TST, 3‐MPST, and CST activities were measured in stored mice cadavers. Hepatic and renal TST activity increased by 0.5 LD50 KCN but diminished by ≥2.0 LD50. After 0.5 LD50 KCN, the elevated hepatic cyanide level was accompanied by increased TST, 3‐MPST, and CST activities, and CCO inhibition. Elevated renal cyanide level was only accompanied by increased 3‐MPST activity. No appreciable change in enzyme activities was observed in mice cadavers. The study concludes that high doses of cyanide exert saturating effects on its detoxification enzymes, indicating their exogenous use during cyanide poisoning. Also, these enzymes are not reliable markers of cyanide poisoning in autopsied samples.
A deep CNN method for underwater image enhancement

Underwater images often suffer from color distortion and visibility degradation due to the light absorption and scattering. Existing methods utilize various assumptions/constrains to achieve reasonable solutions for underwater image enhancement. However, these methods share the common limitation that the adopted assumptions may not work for some particular scenes. To address this problem, this paper proposes an end to end framework for underwater image enhancement, where a CNN-based network called UIE-Net is presented. The UIE-net is trained with two tasks, color correction and haze removal. This unified training approach enables learning a strong feature representation for both tasks simultaneously. For better extracting the inherent features in local patches, a pixels disrupting strategy is exploited in the proposed learning framework, which significantly improves the convergent speed and accuracy. To handle the training of UIE-net, we synthesize 200000 training images based on the physical underwater imaging model. Experiments on benchmark underwater images for cross-scenes show that UIE-net achieves superior performance over existing methods.
The LHCb eventbuilder: Design, implementation and operational experience

The LHCb event-builder is implemented using a large Gigabit Ethernet network using a very lean push-protocol for a single stage read-out at 1 MHz event injection rate. Destination assignment and dynamic load-balancing are facilitated by LHCb's Timing and Fast Control system. Assembly of fragments is done on each event-filter farm node instead of having dedicated builder units. The design of the event-builder will be shortly described, followed by a description of the implementation, protocol and performance results. Emphasis will be on experience in running such a large event-building network and the problems which we encountered and how we overcame them.
Proposed system for ultrasonic temperature imaging of the human prostate in vivo during transurethral microwave thermotherapy: data acquisition and initial experience

The feasibility of using a minimally-invasive pulse-echo diagnostic ultrasound technique for estimation of the spatiotemporal temperature distribution in the human prostate in vivo during transurethral microwave thermotherapy (TUMT) was investigated. The tissue temperature distribution was estimated using a method which allows spatially resolved measurement of changes in time of flight. Backscattered ultrasound RF signals were acquired from a commercially-available 7.5 MHz transrectal imaging probe and processed to compute and image the apparent tissue internal displacement distribution. The temperature distribution was determined by exploiting an estimated initial sound speed distribution and an empirical relationship between temperature and ultrasound transmission speed. Results form an in-vitro phantom study showed a linear relationship between induced displacement and temperature for a limited temperature range. Initial results from an exploratory in-vivo study in patients undergoing TUMT showed substantial influence form tissue motion and deformation. Problems inherent to in-vivo data acquisition are discussed and potential solutions are proposed. Possible approaches to further improve the algorithm and to assess the ability of the system to estimate and monitor the intraprostatic temperature distribution during TUMT are presented.
Culturally-Centric Outreach and Engagement for Underserved Groups in STEM

The intersection between Science, Technology and the Arts is providing an insightful and rich playground for engaging historically underrepresented and underserved youth. This paper discusses an innovative and effective approach for learning Science, Technology, Engineering, the Arts and Mathematics (STEAM) with an emphasis on using expressive arts as a culturally-centered engagement tool. Our framework incorporates a multimodal model that considers unconventional learning styles that appeal to underrepresented and underserved students, the power of cultural cues and the presence of minority STEM professionals to shape students learning experiences. In addition to demonstrating the importance of culturally-centered learning approaches, we've identified methods of engaging students' families and communities as a means of strengthening the pipeline to continued education in STEM.