Miracle-dz/UniFAR-FTUs huggingface dataset:
Ion velocities in a micro-cathode arc thruster

Ion velocities in the plasma jet generated by the micro-cathode arc thruster are studied by means of time-of-flight method using enhanced ion detection system (EIDS). The EIDS triggers perturbations (spikes) on arc current waveform, and the larger current in the spike generates denser plasma bunches propagating along with the mainstream plasma. The EIDS utilizes double electrostatic probes rather than single probes. The average Ti ion velocity is measured to be around 2×104 m/s without a magnetic field. It was found that the application of a magnetic field does not change ion velocities in the interelectrode region while leads to ion acceleration in the free expanding plasma plume by a factor of about 2. Ion velocities of about 3.5×104 m/s were detected for the magnetic field of about 300 mT at distance of about 100–200 mm from the cathode. It is proposed that plasma is accelerated due to Lorentz force. The average thrust is calculated using the ion velocity measurements and the cathode mass consumption r.
A Case for Richer Cross-Layer Abstractions: Bridging the Semantic Gap with Expressive Memory

This paper makes a case for a new cross-layer interface, Expressive Memory (XMem), to communicate higher-level program semantics from the application to the system software and hardware architecture. XMem provides (i) a flexible and extensible abstraction, called an Atom, enabling the application to express key program semantics in terms of how the program accesses data and the attributes of the data itself, and (ii) new cross-layer interfaces to make the expressed higher-level information available to the underlying OS and architecture. By providing key information that is otherwise unavailable, XMem exposes a new, rich view of the program data to the OS and the different architectural components that optimize memory system performance (e.g., caches, memory controllers). By bridging the semantic gap between the application and the underlying memory resources, XMem provides two key benefits. First, it enables architectural/system-level techniques to leverage key program semantics that are challenging to predict or infer. Second, it improves the efficacy and portability of software optimizations by alleviating the need to tune code for specific hardware resources (e.g., cache space). While XMem is designed to enhance and enable a wide range of memory optimizations, we demonstrate the benefits of XMem using two use cases: (i) improving the performance portability of software-based cache optimization by expressing the semantics of data locality in the optimization and (ii) improving the performance of OS-based page placement in DRAM by leveraging the semantics of data structures and their access properties.
Low-Jitter Multi-Output All-Digital Clock Generator Using DTC-Based Open Loop Fractional Dividers

An all-digital reconfigurable multi-output clock generator is presented. A digital phase-locked loop provides a high-frequency clock to multiple independent open loop <inline-formula> <tex-math notation="LaTeX">$\Delta \Sigma $ </tex-math></inline-formula> fractional dividers (FDIVs). A high resolution digital-to-time converter (DTC) whose range is calibrated in background is used to achieve low-jitter performance that is insensitive to process, voltage, and temperature variations. The proposed open loop FDIV operates over a wide frequency range of 20 MHz–1 GHz, and has programmable spread spectrum modulation and instantaneous frequency switching capabilities. Fabricated in a 65-nm process, the prototype FDIV occupies an active area of 0.017 mm<sup>2</sup>. At 1-GHz output frequency, it consumes 3.2 mW from 0.9-V supply and achieves a worst case integrated jitter of 1.44 ps<sub>rms</sub>.
Formation of functional, non‐amyloidogenic fibres by recombinant Bacillus subtilis TasA

Bacterial biofilms are communities of microbial cells encased within a self-produced protective polymeric matrix. The extracellular, fibrous Bacillus subtilis protein TasA is essential for structural integrity of the biofilm. We have developed a recombinant system that allows us to interrogate TasA form and function, and reveal that in both monomeric and fibre forms the secondary structure is comparable. We show that recombinant TasA fibres are biologically active and form without the aid of a partner protein, whereas a TasA variant restricted to an inactive monomeric form is subjected to extracellular proteolysis. The biophysical properties of the TasA fibres indicate that they are not functional amyloid fibres. We show that a gel formed by TasA fibres can recover after physical shear force, suggesting that the biofilm matrix is not static and that these properties may enable B. subtilis to remodel its local environment in response to external cues. Finally through the use of recombinant fibres formed by TasA orthologues, we uncover species variability in the ability of heterologous fibres to cross-complement the B. subtilis tasA deletion strain. These findings are indicative of specificity in the biophysical requirements of the TasA fibres across different species and/or reflect the precise molecular interactions that are needed to allow the biofilm matrix to assemble. Significance The protein TasA is an abundant component of the Bacillus subtilis biofilm matrix and has previously been classified as a “functional amyloid”. Functional amyloid-like fibres have been identified in bacteria and eukaryotes, and are viewed of interest due to their potential structural relationship to the disease-related amyloid fibres implicated in Alzheimer’s and Parkinson’s disease. Here we show that although TasA is a functional fibre-forming protein, it possesses few of the characteristics of canonical amyloid-like fibres and is likely made up of a helical assembly of globular protein units. This knowledge has implications for how B. subtilis biofilms may respond to their local environment when in a biofilm.
Characterization of Biochar Produced at Different Temperatures and its Effect on Acidic Nitosol of Jimma, Southwest Ethiopia

Physical and chemical properties of the biochar varied as a function of feedstock selection and pyrolysis temperatures. Biochar additions to acidic soils have the potential to improve soil fertility and crop yield. Biochar materials were produced from coffee husk and corn cob at temperatures of 350 and 500°C and characterized by their physical and chemical properties. These were mixed with acidic soil at the rates of 0, 5, 10 and 15 t haG 1 and were laboratory incubated for 2 months at ambient temperature to examine changes in soil properties. Types of feedstock used at two different pyrolysis temperatures and application rate had no significant effects on soil textural classes but showed highly significant effects (p<0.01) on soil pH, Electrical Conductivity (EC), Cation Exchange Capacity (CEC), Organic Carbon (OC), Organic Matter (OM), Total Nitrogen (TN), exchangeable cations and available phosphorous. Application of coffee husk biochar showed relatively better improvement in soil chemical properties (pH, EC, CEC, OC, OM, TN, exchangeable cations and available phosphorous) than corn cob biochar at all application rates. The highest values of chemical properties were recorded when coffee husk biochar produced at 500°C temperature was applied at a rate of 15 t haG 1 . Therefore, we generated an evidence that application of biochar is
The Need, Development, and Validation of the Innovation Test Instrument.

This study discusses the need, development, and validation of the Innovation Test Instrument (ITI). This article outlines how the researchers identified the content domain of the assessment and created test items. Then, it describes initial validation testing of the instrument. The findings suggest that the ITI is a good first step in creating an innovation assessment because it is more inclusive of both divergent and convergent thinking. In comparison, past innovation assessments have only assessed either divergence or convergence. The ITI still needs further validation and improvement to make strong claims about its ability to determine the effectiveness of an innovation course.
Patient’s satisfaction and muscles activity after management of temporomandibular disorders patients using computer-aided design/computer-aided manufacturing versus conventional occlusal splints (randomized clinical trial)

Background: With continuous debate, regarding which are more effective in managements of the temporomandibular disorders (TMDs), CAD/CAM or conventionally made occlusal splint, this study aimed to answer the following question “does the CAD/CAM occlusal splints have different effect regard to patient satisfaction, muscle activity, and time of adjustments when compared with conventional splints? ” Materials and Methods: A sample of 30 TMD patients, with a mean age of 30.2 years were randomly allocated into Groups 1 (CAD/CAM splint 15 patients) and 2 (conventional splint 15 patients). The research diagnostic criteria for TMD were used for TMD Axis I (Groups II disc displacement with reduction). Numeric scales (TMD/NS, 10 cm) were used to measure headaches, face pain, jaw joint pain, jaw joint noises, mastication pain, neck pain, face tension, limitation of mouth opening, complaints during mastication, and teeth sensitivity at baseline and then monthly for 3 months. Electromyography was used to measure the muscle activity of masseter and temporalis muscles at baseline, 1 month, and 3 months. The time needed for adjustment of splint from the beginning of splint insertion until became well fitted and occlusally adjusted was calculated using stopwatch (minutes). Results: TMD/NS showed statistically significant improvement in CAD/CAM splint group when compared to conventional splint after 1 and 3 months P = 0.001 and < 0.0001, respectively. There was highly statistical significant less time required for adjustment for the CAD\CAM occlusal splints when compared to the conventional occlusal splints (P < 0.0001). Conclusion: The CAD/CAM occlusal splints significantly require less time of adjustment and improve the patient’s satisfaction of TMD patients greater than that of conventional occlusal splint. However, both splints are similar in regard to improvements of the muscle activities.
White Matter Features Associated With Autistic Traits in Obsessive-Compulsive Disorder

Obsessive-compulsive disorder (OCD) is among the most debilitating psychiatric disorders. Comorbid autism spectrum disorder (ASD) or autistic traits may impair treatment response in OCD. To identify possible neurostructural deficits underlying autistic traits, we performed white matter tractography on diffusion tensor images (DTI) and assessed autistic trait severity using the Autism-Spectrum Quotient (AQ) in 33 OCD patients. Correlations between AQ and the DTI parameters, fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were examined in major white matter tracts that were suggested to be altered in previous OCD studies. We found a negative correlation between AQ and FA and positive correlations between AQ and MD, AD and RD in the left uncinate fasciculus using age, Beck Depression Inventory, Yale-Brown Obsessive-Compulsive Scale, intelligence quotient and medication as covariates. However, we could not detect the significant results between AQ and all DTI parameters when adding gender as a covariate. In addition, in the ASD comorbid group, FA in the left uncinate fasciculus was significantly lower than in the non-ASD comorbid group and MD and RD were significantly higher than in the non-ASD group. These results did not survive correction for multiple comparisons. In ASD, the socio-emotional dysfunction is suggested to be related to the alteration of white matter microstructure in uncinate fasciculus. Our results suggest that variations in white matter features of the left uncinate fasciculus might be partly explained by autistic traits encountered in OCD patients.
An Empirical Study on Software Failure Classification with Multi-label and Problem-Transformation Techniques

Classification techniques have been used in software-engineering research to perform tasks such as categorizing software executions. Traditionally, existing work has proposed single-label failure classification techniques, in which the training and subsequent executions are labeled with a singular fault attribution. Although such approaches have received substantial attention in research on automated software engineering, in reality, recent work shows that the assumption of such a single attribution is often unrealistic: in practice, the inherent characteristics of software behavior, such as multiple faults that contribute to failures and fault interactions, may negatively influence the effectiveness of these techniques. To relax this unrealistic assumption, in the machine learning field, researchers have proposed new approaches for multi-label classification. However, the effectiveness and efficiency of such approaches varies widely based upon application domains. In this paper, we empirically investigate the performance of these new approaches on the failure classification task under different application settings. We conducted experiments using eight classification techniques on five subject programs with more than 8,000 faulty versions to investigate how each such technique accounts for the intricacies of software behavior. Our experimental results show that multi-label techniques provide improved accuracy over single-label. We also evaluated the efficiency of the training and prediction phases of each technique, and offer guidance as to the applicability for each technique for different usage contexts.
Change in Reciprocal Inhibition of the Forearm with Motor Imagery among Patients with Chronic Stroke

We investigated cortically mediated changes in reciprocal inhibition (RI) following motor imagery (MI) in short- and long(er)-term periods. The goals of this study were (1) to describe RI during MI in patients with chronic stroke and (2) to examine the change in RI after MI-based brain-machine interface (BMI) training. Twenty-four chronic stroke patients participated in study 1. All patients imagined wrist extension on the affected side. RI from the extensor carpi radialis to the flexor carpi radialis (FCR) was assessed using a FCR H reflex conditioning-test paradigm. We calculated the “MI effect score on RI” (RI value during MI divided by that at rest) and compared that score according to lesion location. RI during MI showed a significant enhancement compared with RI at rest. The MI effect score on RI in the subcortical lesion group was significantly greater than that in the cortical lesion group. Eleven stroke patients participated in study 2. All patients performed BMI training for 10 days. The MI effect score on RI at a 20 ms interstimulus interval was significantly increased after BMI compared with baseline. In conclusion, mental practice with MI may induce plastic change in spinal reciprocal inhibitory circuits in patients with stroke.
RIDDL at SemEval-2018 Task 1: Rage Intensity Detection with Deep Learning

We present our methods and results for affect analysis in Twitter developed as a part of SemEval-2018 Task 1, where the sub-tasks involve predicting the intensity of emotion, the intensity of sentiment, and valence for tweets. For modeling, though we use a traditional LSTM network, we combine our model with several state-of-the-art techniques to improve its performance in a low-resource setting. For example, we use an encoder-decoder network to initialize the LSTM weights. Without any task specific optimization we achieve competitive results (macro-average Pearson correlation coefficient 0.696) in the El-reg task. In this paper, we describe our development strategy in detail along with an exposition of our results.
The Primal-Dual Active Set Strategy as a Semismooth Newton Method

This paper addresses complementarity problems motivated by constrained optimal control problems. It is shown that the primal-dual active set strategy, which is known to be extremely efficient for this class of problems, and a specific semismooth Newton method lead to identical algorithms. The notion of slant differentiability is recalled and it is argued that the $\max$-function is slantly differentiable in Lp-spaces when appropriately combined with a two-norm concept. This leads to new local convergence results of the primal-dual active set strategy. Global unconditional convergence results are obtained by means of appropriate merit functions.
Amrita_student at SemEval-2018 Task 1: Distributed Representation of Social Media Text for Affects in Tweets

In this paper we did an analysis of “Affects in Tweets” which was one of the task conducted by semeval 2018. Task was to build a model which is able to do regression and classification of different emotions from the given tweets data set. We developed a base model for all the subtasks using distributed representation (Doc2Vec) and applied machine learning techniques for classification and regression. Distributed representation is an unsupervised algorithm which is capable of learning fixed length feature representation from variable length texts. Machine learning techniques used for regression is ’Linear Regression’ while ’Random Forest Tree’ is used for classification purpose. Empirical results obtained for all the subtasks by our model are shown in this paper.
Cone beam computed tomography: Adding three dimensions to endodontics

The need for evaluating the structures in three dimensions led to the revolutionary invention of cone beam computed tomography (CBCT). CBCT had found its application in various regions of dentistry. The literature demonstrates the use of CBCT for specifi c endodontic applications. The purpose of this article is to review the history and evolution of CBCT, its advantages over conventional radiography and to discuss the literature validating its application in endodontics.
Tools and techniques for solving cold start recommendation

Recommendation Systems are very important systems that saves users time and resources by saving them from searching the bulk data. The best example is googling which searches and gives list of hundreds of pages. Therefore, a major challenge of Recommendation Systems can be how to make recommendations for a new user, that is called cold-start user problem in this papers we are trying to identify different kinds of cold start problems in Recommendation Systems. We are also trying to explore different types of solutions to these problems in last 10 years. are very important systems that saves users time and resources by saving them from searching the bulk data. The best example is googling which searches and gives list of hundreds of pages. Therefore, a major challenge of Recommender systems can be how to make recommendations for a new user, that is called cold-start user problem in this papers we are trying to identify different kinds of cold start problems recommender systems. We are also trying to explore different types of solutions to these problems in last 10 years.
Effect of coherence and polarization on the polychromatic partially coherent dark hollow beam generated from axicon-lens system

An experimental study is carried out to investigate the effect of coherence and polarization on the polychromatic partially coherent dark hollow beam (PCDHB). The experimental results show that the spatial coherence and source polarization affect the dark region of the generated hollow beam. The study shows that by varying the source degree of polarization (DOP), we get a tunable dark region. We find that the longer the spatial coherence length of the input beam, the larger the central dark size of the resultant PCDHB. Further, it is shown that polychromatic PCDHB with low spatial coherence travel a longer distance without being distorted than a beam with a high spatial coherence. These kinds of polychromatic beams may find potential application in the field of polychromatic light based free-space optical (FSO) communications.
Irony Detector at SemEval-2018 Task 3: Irony Detection in English Tweets using Word Graph

This paper describes the Irony detection system that participates in SemEval-2018 Task 3: Irony detection in English tweets. The system participated in the subtasks A and B. This paper discusses the results of our system in the development, evaluation and post evaluation. Each class in the dataset is represented as directed unweighted graphs. Then, the comparison is carried out with each class graph which results in a vector. This vector is used as features by machine learning algorithm. The model is evaluated on a hold on strategy. The organizers randomly split 80% (3,833 instances) training set (provided to the participant in training their system) and testing set 20%(958 instances). The test set is reserved to evaluate the performance of participants systems. During the evaluation, our system ranked 23 in the Coda Lab result of the subtask A (binary class problem). The binary class system achieves accuracy 0.6135, precision 0.5091, recall 0.7170 and F measure 0.5955. The subtask B (multi-class problem) system is ranked 22 in Coda Lab results. The multiclass model achieves the accuracy 0.4158, precision 0.4055, recall 0.3526 and f measure 0.3101.
The Risk Assessment of Breast Tumors Based on Text Data of Ultrasound Reports

The breast tumor has become a common and prevalent disease. If it can be found timely, and treated early, the life risk of patients will be reduced, and the survival rate will be improved. The accurate assessment of the breast tumor, benign or malignant, is the primary key to cure the disease in practical clinical diagnosis. The paper proposes a risk assessment method of breast tumors based on text data of ultrasound reports, using practical ultrasound reports of patients with breast tumors from a 3A grade hospital in Shanghai, and focuses on an innovative point of depending on reinforcement learning to extract index values from text data of ultrasound reports. First, this method chooses normative text data which index values are complete as external files, iterates to compute the reward function of the reinforcement learning algorithm with index parameters, and associate external files to dynamically update the corresponding index values to ultimately form the structured data. And then, this method inputs the structured data into three models based on machine learning algorithms—Support Vector Machine, K-Nearest Neighbor, and Random Forest—to assess the risk, benign or malignant, of the breast tumor. The experiments show the accuracy of every classification algorithm increases by nearly 10% via the method based on reinforcement learning, comparing with the common structured approach relying on dependency parsing, which reaches up to 84% in practical text data of ultrasound reports. Because of this method, manual workload can drop a lot, doctors can improve the accuracy of diagnosis and decrease misdiagnosis and missed diagnosis, and patients can reduce unnecessary surgical pain.