Miracle-dz/UniFAR-FTUs huggingface dataset:
Three-Dimensional Unsharp Masking Spatial Filter of a Field of Vectors for Geometrical Edges Magnitude and Direction Detection in Point Cloud Surfaces

The detection of the edges magnitude and direction of the geometrical surfaces of the indoor environment remains an important problem. This problem has a wide spectrum of application including object recognition and detection, autonomous navigation systems, and three-dimensional localization and mapping. The problem statement of the current research is to detect edges magnitude and direction of the geometrical surfaces of the point cloud field of vectors of the indoor environment which is captured by a depth sensor. The objective of the current research is to propose a novel algorithm of the Unsharp masking that is applicable to the field of vectors and analogous to the Unsharp masking of the field of scalars. In this research, we: (1) propose a novel algorithm for the Unsharp Masking of a field of vectors that is analogous to the Unsharp Masking of a field of scalars; (2) do behavioral analyses on the Step edges; Plane areas; and onset, end and along Ramp areas; (3) and do performance analyses on TUM data set, and comparison study with the state of the art edge detectors on NYUD data set that shows it is efficient.
Enhancing CRF-based Chinese Word Segmentation Using a Rapid and Effective Feature Template Selection Algorithm and Character Normalization

Conditional random fields (CRFs) are among the classic models for Chinese word segmentation (CWS). Deep neural networks (DNNs) have recently emerged as a research hotspot in natural language processing (NLP). However, studies exploring the use of DNN for CWS have not yielded significant gains over CRF models. Thus, developing CRFs for CWS remains a viable avenue for research. This paper proposes two methods to enhance CRF-based CWS. First, a rapid and effective sequential forward selection (SFS)-style method is utilized for feature template selection to balance search performance with search speed. Second, it describes a method for character normalization more robust than the traditional method. Incremental evaluations on the second SIGHAN bakeoff show that the two proposed methods reduce the error by 7.8%, and 10.6% respectively in terms of F-score. The final system achieved an F-score of 0.955 (AS), 0.955 (CITYU), 0.970 (MSR), and 0.952 (PKU), which is comparable to those of the best systems reported in the reference.
A Case for Dynamically Programmable Storage Background Tasks

Modern storage infrastructures feature long and complicated I/O paths composed of several layers, each employing their own optimizations to serve varied applications with fluctuating requirements. However, as these layers do not have global infrastructure visibility, they are unable to optimally tune their behavior to achieve maximum performance. Background storage tasks, in particular, can rapidly overload shared resources, but are executed either periodically or whenever a certain threshold is hit regardless of the overall load on the system. In this paper, we argue that to achieve optimal holistic performance, these tasks should be dynamically programmable and handled by a controller with global visibility. To support this argument, we evaluate the impact on performance of compaction and checkpointing in the context of HBase and PostgreSQL. We find that these tasks can respectively increase 99th percentile latencies by 955.2% and 61.9%. We also identify future research directions to achieve programmable background tasks.
Leakage Immune Modified Cross Coupled Inverter Based MI-12T SRAM in Sub-Threshold Regime

Stable SRAM cells at ultra-low power supply voltage is an important design requirement for digital circuits in deep sub-micron region. The paper presents in-depth analysis of low-power, variability-aware, stable 12T SRAM cell using 45nm standard CMOS technology. The high leakage current is reduced by using ON/OFF logic pair. The Modified Inverter-12T (MI-12T) cell obtains 2.5x higher write-SNM and 1.6x improved read-SNM than Standard 6T (S6T) SRAM cell. Varying VDD 0.2 V to 0.4 V, the MI-12T SRAM cell achieves lower average read delay (read access time), 3.57x less average write delay (write access time), 1.476x lower average hold power consumption in comparison to S6T. Thus, the simulation results reveal that MI-12T has high cell stability and enhanced data maintenance ability at 0.4V.
Tinkercad and Codeblocks in a Summer Course: an Attempt to Explain Observed Engagement and Enthusiasm

As part of the 3D printing summer course for undergrads in our university, students were introduced to Autodesk Tinkercad as a design tool for creating and editing 3D models. After a brief period of familiarization with Tinkercad, we presented students with Codeblocks, a tool integrated with Tinkercad that enables the creation of 3D models through programming under the blocks model. To our surprise, students started creating complex and lengthy programs to produce original printable 3D objects. We decided to investigate this level of engagement using a questionnaire to collect opinions and experiences from students and a set of metrics to categorize their programming work. The results obtained are presented in this paper along with our conjectures on the causes of the enthusiasm observed with Codeblocks. Three factors seem to have been key in this case: diversity in the composition of the student group, availability of 3D printers to materialize designs, and a relaxed environment where test and failure were common and acceptable.
Parallel Implementation of Motif-Based Clustering for HT-SELEX Dataset

A clustering method for high-throughput sequencing with SELEX pools (HT-SELEX) is crucial for selecting different types of aptamer candidates. The fast and accurate clustering method is indispensable for an enormous sequence data produced by HT-SELSEX. We have already developed a fast motif-based clustering (FMBC) method for HT-SELEX data implemented by R language. FMBC exhibited high accuracy of sequence clustering compared with conventional methods, while the processing time of FMBC is longer than AptaCluster. This paper proposes the parallel implementation of FMBC using Python with multi-threading to improve the performance of FMBC. Experimental evaluation using the NCBI SRA data of SRR3279661 from BioProject PRJNA315881 demonstrated that parallel FMBC exhibited higher accuracy of clustering and shorter processing time than conventional methods.
Automated Sleep Spindle Detection System using Period-Amplitude Analysis

Sleep spindles are rhythmic transient waveforms present in the electroencephalogram (EEG) of non-rapid eye movement (NREM) sleep. In the present study a period-amplitude analysis method was applied for the automated detection of sleep spindles in all-night sleep EEG recordings of young healthy subjects. The method relies on the characterization of individual half-waves of the EEG data, by estimating electrographic parameters such as amplitude and duration and by assigning a grade to each half-wave depending on where it lies in the amplitude-frequency plane. The grading is followed by the detection system, checking consecutive half-wave characteristics and implementing a set of rules for determining the start and the end of spindle bursts and for retaining or rejecting sleep spindle indications provided during the various stages of the detection system. The sensitivity and false positive rate across subjects was 78.9% and 10.9%, respectively, providing indication that the method could be successfully applied to larger sets of healthy subjects of various age groups, as well as to patient populations.
Detailed Analysis of IRIS Recognition Performance

Iris recognition is a well-known biometric identification system which distinguishes authentic and imposter individuals based on the features of their irides. It employs stringent statistical analyses of the features of irides due to the fact that each person has a unique iris, just like a fingerprint. In this work, the approach adopted towards the iris recognition problem is through an exhaustive and careful analysis of the statistical properties of the iris images and the randomness of spurious noise effects. The ability to differentiate two different templates from each other improves with the increase in the number of the degrees of freedom (DOF). The DOF depends on the encoding schemes utilized and moreover, it is hypothesized that the encoding schemes used in themselves could influence the recognition performance. The CASIA (Chinese Academy of Sciences Institute of Automation) version 1 database of iris images used in this study has been modified by the addition of artificial noise in order to simulate practical real life in situ noisy iris capture environments. The classical and state-of-the-art segmentation techniques have been compared, determining whether they are superior to the others under several conditions. The 1D, 2D Gabor filters and the short window implementation were all tested. The conclusion was that the 2D Gabor Filters produce a lower equal error rate (EER), higher accuracy and decidability than by using the one-dimensional log Gabor filter. After modifying the one-dimensional log Gabor filters, a lower EER and higher accuracy was found as the noise level increased. This makes the modified 1D log Gabor Filters a better proposition in noisy conditions. The generated iris templates have a predetermined theoretical value of DOF and from the statistical analysis, an experimental value can be determined. The relation between these values can be used as a metric to compare different databases.
Comparison of Machine Learning Algorithms and Oversampling Techniques for Urinary Toxicity Prediction After Prostate Cancer Radiotherapy

Prostate cancer radiotherapy unavoidably involves the irradiation not only of the target volume, but also of healthy organs-at-risk, neighboring the prostate, likely causing adverse, toxicity-related side-effects. Specifically, in the case of urinary toxicity, these side effects might be associated with a variety of dosimetric, clinical and genetic factors, making its prediction particularly challenging. Given the inconsistency of available data concerning radiation-induced toxicity, it is crucial to develop robust models with superior predictive performance in order to perform tailored treatments. Machine Learning techniques emerge as appealing in this context, nevertheless without any consensus on the best algorithms to be used. This work proposes a comparison of several machine-learning strategies together with different minority class oversampling techniques for prediction of urinary toxicity following prostate cancer radiotherapy using dosimetric and clinical data. The performance of these classifiers was evaluated on the original dataset and using four different synthetic oversampling techniques. The area under the ROC curve (AUC) and the F-measure were employed to evaluate their performance. Results suggest that, regardless of the technique, oversampling always increases the prediction performance of the models (p=0.004). Overall, oversampling with Synthetic Minority Oversampling Technique (SMOTE) followed by Edited Nearest Neighbour algorithm (ENN) together with Regularized Discriminant Analysis (RDA) classifier provide the best performance (AUC=0.71).
Brain Tumor Detection Based on Multimodal Information Fusion and Convolutional Neural Network

Aiming at the problem of low accuracy of traditional brain tumor detection, in this paper, a combination of multimodal information fusion and convolution neural network detection method of brain tumors, we call it a Multi-CNNs. First, this paper uses the extension of the 2D-CNNs to multimodal 3D-CNNs, and can obtain brain lesions under different modal characteristics of three-dimensional space. It can solve the 2D-CNNs raw input requires large neighborhood of faults, at the same time better to extract the modal of the differences between information. Then the real normalization layer is added between the convolution layers and pooling layer to improve the convergence speeds of the network and alleviate the problem of overfitting. In the end, the loss function was improved, and the weighted loss function was used to enhance the feature learning of the lesion area. The experimental results showed that the brain tumor detection method proposed in this paper could effectively locate tumor lesions, and better results were obtained in correlation coefficient, sensitivity, and specificity. Compared with two-dimensional detection network and single mode brain tumor detection methods, the detection accuracy is significantly improved.
Realtime Behavior-Based Continual Authentication of Users in Virtual Reality Environments

In this work, we present a realtime interactive system that uses the trajectories of the device controllers and headset of a virtual reality (VR) system to perform continual authentication of users interacting in VR. Our system identifies a user within . 21 +/- . 04 seconds of provision of a trajectory for a ball-throwing application. Given an interaction session authorized for a user within an organization, our system continually authenticates the current user by computing matches for the positions and orientations of the points on trajectories of the right hand controller, left hand controller, and headset for the user against controller and headset trajectories in a library of users. The system combines the position and orientation matches using a neural network pre-trained to predict confidences on matches computed within the library, and provides a speech interface to output the identified user as the library user with the maximum value of predicted confidence. The system uses the identity of the user authorized to use a session and a confidence threshold to detect if the user is genuine, a malicious user internal to the organization, or an external intruder. Our system can be readily ported into virtual reality applications that require realtime behavioral authentication of users for secure access.
Exploring Telephone-Quality Speech Signals Towards Parkinson's Disease Assessment in a Large Acoustically Non-controlled Study

The use of high-quality speech signals has led to considerable breakthroughs in Parkinson's Disease (PD) in the last decade. These include accurate differentiation of PD versus Healthy Controls (HC) and monitoring longitudinal PD symptom severity. We recently concluded the Parkinson's Voice Initiative (PVI) study collecting data from a very large cohort under non-controlled acoustic conditions. We acoustically characterized 11,942 recordings from 6531 US-based participants using 307 dysphonia measures. We selected a robust subset of 30 dysphonia measures using Gram-Schmidt Orthogonalization (GSO). We projected the data onto a two-dimensional representation using t-distributed stochastic neighbor embedding to facilitate visual exploration, and used hierarchical clustering to understand data homogeneity. We demonstrate that there is considerable overlap in the projected feature space between PD and HC, making the binary classification task particularly challenging. The data was grouped into nine clusters using hierarchical clustering which was in broad agreement with the projected two-dimensional representation. These results provide some new insights into understanding the new challenges posed in the PVI project where acoustic recordings conditions were not controlled.
Solving Diversity Issues in University Staff Training with UNIPS Pedagogical Online Courses

In Finland and globally, many university teachers are teaching without pedagogical training. Employee training courses on pedagogy are offered via contact teaching, thus excluding potential students who are too busy to attend sessions at a specific time and place. In addition, majority of teaching is in Finnish, even though, for example, in the University of Turku, 10% of all employees are international. Due to limited teaching resources, university pedagogical studies used to be only available for university staff members who have teaching duties, excluding the majority of doctoral students from the courses. The UNIPS learning platform, developed by eight Finnish universities, was created to solve these problems. The current study investigates the impact UNIPS solution has on the above mentioned issues by looking quantitatively (N=590) at (1) which departments participants come from? (2) Are participants’ doctoral students or university employees and (3) what are the age and gender distributions of participants? In addition, participants’ perceptions of UNIPS studies are analyzed qualitatively. Based on the findings, UNIPS courses and similar MOOCs seem a promising way to support teachers’ pedagogical training. They can not only increase the diversity of offered studies, but also help create a more inclusive environment at universities.
The Appearance of Cytokines and Adhesion Molecules in Saphenous Vein Valves in Chronic Venous Insufficiency

Objective: To evaluate the difference between competent valves and incompetent valves with regards to the development of expression of cytokines and adhesion molecules in primary varicose veins. Methods: Specimens were obtained from 13 patients with primary varicose veins during surgery. Valves were classified according to the angioscopic findings: 8 competent and 17 incompetent valves; type I (7 valves with elongated cusps) and type II (10 valves with expansion of commissures). The mRNA levels of interleukin-1β (IL-1β), interleukin-6 (IL-6), interleukin-8 (IL-8), tumour necrosis factor-α (TNF-α), transforming growth factor-β (TGF-β), intercellular adhesion molecule-1 (ICAM-1), vascular cell adhesion molecule-1 (VCAM-1) and endothelial leucocyte adhesion molecule-1 (ELAM-1) were measured by polymerase chain reaction (PCR) amplification of cDNA reverse-transcribed from RNAs. Results: In all cases there were few appearances of IL-6, IL-8 and TNF-α at the valve cusps. TGF-β and VCAM-1 levels were highly elevated in the cusps of incompetent valves compared with competent valves (p<0.05). At the venous wall IL-6 and IL-8 occurred more frequently in incompetent valves than in competent valves (p<0.05). Conclusion: This study suggests that inflammatory cytokines and adhesions molecules, especially TGF-β, IL-6, IL-8 and VCAM-1, may be related to the occurrence of valve insufficiency.
Static Magnetic Field Accelerates Diabetic Wound Healing by Facilitating Resolution of Inflammation

Impaired wound healing is commonly encountered in patients with diabetes mellitus, which may lead to severe outcomes such as amputation, if untreated timely. Macrophage plays a critical role in the healing process including the resolution phase. Although magnetic therapy is known to improve microcirculation, its effect on wound healing remains uncertain. In the present study, we found that 0.6 T static magnetic field (SMF) significantly accelerated wound closure and elevated reepithelialization and revascularization in diabetic mice. Notably, SMF promoted the wound healing by skewing the macrophage polarization towards M2 phenotype, thus facilitating the resolution of inflammation. In addition, SMF upregulated anti-inflammatory gene expression via activating STAT6 and suppressing STAT1 in macrophage. Taken together, our results indicate that SMF may be a promising adjuvant therapeutic tool for treating diabetic wounds.
Study on Pharmacokinetic and Bioavailability of Tamarixetin after Intravenous and Oral Administration to Rats

In this study, a sensitive and reliable HPLC-MS/MS method was established to quantify tamarixetin in rat plasma. This method was then applied to research on the pharmacokinetic and bioavailability of tamarixetin after intravenous and oral administration in vivo. The study was performed on CORTECS C18 column (4.6 mm × 150 mm, 2.7 μm) using mobile phase composed of methanol-water-formic acid (85 : 15 : 0.1, v/v) at a flow rate of 0.3 mL/min by a tandem mass system with an electrospray ionization (ESI) source in the negative multiple-reaction monitoring (MRM) mode. The calibration curves showed good linearity in the range of 5–4000 ng/mL. The intra- and interday precision of tamarixetin was less than 8.7% and 4.8%, respectively, and accuracy was within ±9.5%. Extraction recovery (91.4–100.0%) and matrix effect (99.4–107.4%) met the guidelines published by regulatory authorities. The oral bioavailability of tamarixetin was 20.3 ± 12.4%.
A Five-Year Data Report of Long-Term Central Venous Catheters Focusing on Early Complications

Background Long-term venous access has become the standard practice for the administration of chemotherapy, fluid therapy, antibiotics, and parenteral nutrition. The most commonly used methods are percutaneous puncture of the subclavian and internal jugular veins using the Seldinger technique or surgical cutdown of the cephalic vein. Methods This study is based on a quality registry including all long-term central venous catheter insertion procedures performed in patients >18 years at our department during a five-year period. The following data were registered: demographic data, main diagnosis and indications for the procedure, preoperative blood samples, type of catheter, the venous access used, and the procedure time. In addition, procedural and early postoperative complications were registered: unsuccessful procedures, malpositioned catheters, pneumothorax, hematoma complications, infections, nerve injuries, and wound ruptures. The Seldinger technique using anatomical landmarks at the left subclavian vein was the preferred access. Fluoroscopy was not used. Results One thousand one hundred and one procedures were performed. In eight (0.7%) cases, the insertion of a catheter was not possible, 23 (2.1%) catheters were incorrectly positioned, twelve (1.1%) patients developed pneumothorax, nine (0.8%) developed hematoma, and three (0.27%) developed infection postoperatively. One (0.1%) patient suffered nerve injury, which totally recovered. No wound ruptures were observed. Conclusions We have a high success rate of first-attempt insertions compared with other published data, as well as an acceptable and low rate of pneumothorax, hematoma, and infections. However, the number of malpositioned catheters was relatively high. This could probably have been avoided with routine use of fluoroscopy during the procedure.
The Impact of Step Recommendations on Body Composition and Physical Activity Patterns in College Freshman Women: A Randomized Trial

Purpose Transitioning from high school to college generally results in reduced physical activity and weight gain at a rate that is higher than the general population. The purpose of this study was to examine the effects of three progressively higher step recommendations over 24 weeks on changes in body weight and body composition. Methods Ninety-two freshmen college women wore a multifunction pedometer for 24 weeks after being randomly assigned to a daily step level: 10,000, 12,500, or 15,000. Pedometer data were downloaded every two weeks and participants were counseled on meeting their step recommendation. Body weight and body composition were assessed at baseline and 24 weeks. Body composition was assessed by dual X-ray absorptiometry. Results On average, women took 10,786 ± 1501, 12,650 ± 2001, and 13,762 ± 2098 steps per day for the 10,000-, 12,500-, and 15,000-step groups, respectively (F = 15.48, P < 0.0001). Participants gained 1.4 ± 2.6, 1.8 ± 2.1, and 1.4 ± 2.1 kg for the 10,000-, 12,500-, and 15,000-step groups, respectively (F = 37.74, P < 0.0001). Weight gain was not significantly different between groups (F = 0.18, P=0.8385). There was also no difference in fat weight gain (F = 0.41, P=0.7954). Discussion A step recommendation beyond 10,000 does not prevent weight or fat gain over the first year of college. Future research should focus on either intensity of physical activity or the addition of dietary interventions to prevent weight gain during the first year of college.