Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifying Mg ii narrow absorption lines with deep learning

Metal absorption line systems in distant quasar spectra probe of the history of gas content in the universe. The MgII $\lambda \lambda$ 2796, 2803 doublet is one of the most important absorption lines since it is a proxy of the star formation rate and a tracer of the cold gas associated with high redshift galaxies. Machine learning algorithms have been used to detect absorption lines systems in large sky surveys, such as Principle Component Analysis (PCA), Gaussian Process (GP) and decision trees. A very powerful algorithm in the field of machine learning called deep neural networks, or '' deep learning'' is a new structure of neural network that automatically extracts semantic features from raw data and represents them at a high level. In this paper, we apply a deep convolutional neural network for absorption line detection. We use the previously published DR7 MgII catalog (Zhu et al. 2013) as the training and validation sample and the DR12 MgII catalog as the test set. Our deep learning algorithm is capable of detecting MgII absorption lines with an accuracy of $\sim$94% . It takes only $\sim 9$ seconds to analyze $\sim$ 50000 quasar spectra with our deep neural network, which is ten thousand times faster than traditional methods, while preserving high accuracy with little human interference. Our study shows that Mg II absorption line detection accuracy of a deep neutral network model strongly depends on the filter size in the filter layer of the neural network, and the best results are obtained when the filter size closely matches the absorption feature size.
Low-latency and high-reliability performance analysis of relay systems

The latency and reliability of single-relay and multi-relay systems are investigated in this study. In a single-relay system with the amplify-and-forward protocol, a new approximation analytical method is proposed for the outage probability, which can also be extended to be used in the multi-relay system. The latency-reliability trade-off degree (LRTD) of single- and multi-relay systems, i.e. the slope of the latency-outage probability curve with logarithmic scales, is analysed and proved to be the same as the number of relay nodes. In the multi-relay system, the relationship between the system LRTD and the channel state information overhead is investigated. The optimum number of relay nodes can be derived according to the approximate expression of the outage probability. A simple iterative method is proposed to obtain the optimum number of relay nodes. The convergence of the iterative method is also proved.
Modeling Complementary Products and Customer Preferences with Context Knowledge for Online Recommendation

Modeling item complementariness and user preferences from purchase data is essential for learning good representations of products and customers, which empowers the modern personalized recommender system for Walmart's e-commerce platform. The intrinsic complementary relationship among products captures the buy-also-buy patterns and provides great sources for recommendations. Product complementary patterns, though often reflected by population purchase behaviors, are not separable from customer-specific bias in purchase data. We propose a unified model with Bayesian network structure that takes account of both factors. In the meantime, we merge the contextual knowledge of both products and customers into their representations. We also use the dual product embeddings to capture the intrinsic properties of complementariness, such as asymmetry. The separating hyperplane theory sheds light on the geometric interpretation of using the additional embedding. We conduct extensive evaluations on our model before final production, and propose a novel ranking criterion based on product and customer embeddings. Our method compares favorably to existing approaches in various offline and online testings, and case studies demonstrate the advantage and usefulness of the dual product embeddings as well as the user embeddings.
Multi-Modal Emotion recognition on IEMOCAP Dataset using Deep Learning

Emotion recognition has become an important field of research in Human Computer Interactions as we improve upon the techniques for modelling the various aspects of behaviour. With the advancement of technology our understanding of emotions are advancing, there is a growing need for automatic emotion recognition systems. One of the directions the research is heading is the use of Neural Networks which are adept at estimating complex functions that depend on a large number and diverse source of input data. In this paper we attempt to exploit this effectiveness of Neural networks to enable us to perform multimodal Emotion recognition on IEMOCAP dataset using data from Speech, Text, and Motion capture data from face expressions, rotation and hand movements. Prior research has concentrated on Emotion detection from Speech on the IEMOCAP dataset, but our approach is the first that uses the multiple modes of data offered by IEMOCAP for a more robust and accurate emotion detection.
Effects of Electrical Stimulation on Skeletal Muscle of Old Sedentary People

Physical activity plays an important role in preventing muscle atrophy and chronic diseases in adults and in the elderly. Calcium (Ca2+) cycling and activation of specific molecular pathways are essential in contraction-induced muscle adaptation. This study attains human muscle sections and total homogenates prepared from biopsies obtained before (control) and after 9 weeks of training by electrical stimulation (ES) on a group of volunteers. The aim of the study was to investigate about the molecular mechanisms that support functional muscle improvement by ES. Evidences of kinase/phosphatase pathways activation after ES were obtained. Moreover, expression of Sarcalumenin, Calsequestrin and sarco/endoplasmic reticulum Ca2+-ATPase (Serca) isoforms was regulated by training. In conclusion, this work shows that neuromuscular ES applied to vastus lateralis muscle of sedentary seniors combines fiber remodeling with activation of Ca2+-Calmodulin molecular pathways and modulation of key Ca2+-handling proteins.
Conducting Fresh Whole Blood Transfusion Training.

Fresh whole blood is the optimal resuscitation fluid for casualties in hemorrhagic shock according to the Committee on Tactical Combat Casualty Care and has demonstrated to improve outcomes in severely wounded patients. Like all medical interventions, fresh whole blood transfusions are not without risks, but similarly can be mitigated through increased training to develop provider knowledge and proficiency. To date, no literature has been published regarding the proper technique to conduct fresh whole blood transfusion training. This article provides a structured foundation to establish a standardized fresh whole blood transfusion training program in order to increase skill and preparedness for fresh whole blood protocol implementation. Using these techniques in a training environment, providers will be able to provide optimal resuscitation in hemorrhagic shock in austere environments.
Predictors of the trajectories of self-reported attentional fatigue in women with breast cancer undergoing radiation therapy.

PURPOSE/OBJECTIVES To examine how attentional fatigue changed from the time of simulation to four months after the completion of radiation therapy and to investigate whether specific variables predicted initial levels and trajectories of attentional fatigue. DESIGN Descriptive, longitudinal study. SETTING Two radiation therapy departments. SAMPLE 73 women with breast cancer who received primary or adjuvant radiation therapy. METHODS Participants completed questionnaires prior to, during, and after radiation therapy. Descriptive statistics and hierarchical linear modeling were used for data analysis. MAIN RESEARCH VARIABLES Attentional fatigue; demographic, clinical, and symptom characteristics. FINDINGS Large amounts of interindividual variability were found in the trajectories of attentional fatigue. At baseline, higher levels of attentional fatigue were associated with younger age, not working, a higher number of comorbidities, and higher levels of trait anxiety. The trajectory of attentional fatigue improved over time for women with higher body mass index at baseline. CONCLUSIONS This study is the first to identify predictors of interindividual variability in attentional fatigue in women with breast cancer undergoing radiation therapy. The predictors should be considered in the design of future correlational and interventional studies. IMPLICATIONS FOR NURSING Nurses could use knowledge of the predictors to identify patients at risk for higher levels of attentional fatigue. In addition, nurses could use the information to educate patients about how attentional fatigue may change during and following radiation therapy for breast cancer.
1 Spleen , as an optimal site for islet transplantation and 2 a source of mesenchymal stem cells 3

In this review, we show the unique potential of spleen as an optimal site for islet 12 transplantation and a source of mesenchymal stem cells. Islet transplantation is a cellular 13 replacement therapy to treat severe diabetes mellitus, but its clinical outcome is unsatisfactory at 14 present. One factor in clinical success of this therapy is selection of the most appropriate 15 transplantation site. The spleen has been studied for a long time as a candidate site for islet 16 transplantation. Its advantages include physiological insulin drainage and regulation of immunity. 17 Recently it has also been shown that the spleen contributes to the regeneration of transplanted 18 islets. The efficacy of transplantation is not as high as that obtained with intraportal 19 transplantation, which is the current representative method of clinical islet transplantation. Safer 20 and more effective methods of islet transplantation need to be established before the spleen can be 21 effectively used in the clinic. Spleen also has an interesting aspect as a mesenchymal stem cell 22 reservoir. The splenic mesenchymal stem cells contribute to tissue repair in damaged tissue, and 23 thus, the infusion can be a promising therapy for autoimmune diseases, including type 1 diabetes 24 mellitus and Sjogren’s syndrome. 25
Content Attention Model for Aspect Based Sentiment Analysis

Aspect based sentiment classification is a crucial task for sentiment analysis. Recent advances in neural attention models demonstrate that they can be helpful in aspect based sentiment classification tasks, which can help identify the focus words in human. However, according to our empirical study, prevalent content attention mechanisms proposed for aspect based sentiment classification mostly focus on identifying the sentiment words or shifters, without considering the relevance of such words with respect to the given aspects in the sentence. Therefore, they are usually insufficient for dealing with multi-aspect sentences and the syntactically complex sentence structures. To solve this problem, we propose a novel content attention based aspect based sentiment classification model, with two attention enhancing mechanisms: sentence-level content attention mechanism is capable of capturing the important information about given aspects from a global perspective, whiles the context attention mechanism is responsible for simultaneously taking the order of the words and their correlations into account, by embedding them into a series of customized memories. Experimental results demonstrate that our model outperforms the state-of-the-art, in which the proposed mechanisms play a key role.
NVMain Extension for Multi-Level Cache Systems

In this paper, we present an extension of the NVMain memory simulator. The objective is to facilitate computer architects to model complex memory designs for future computing systems in an accurate simulation framework. The simulator supports commodity memory models for DRAM as well as emerging non-volatile memories technologies such STT-RAM, ReRAM, PCRAM and hybrid models. The current publicly available version of NVMain, NVMain 2.0, offers support for main memory (using DRAM and NVM technologies) and a die-stacked DRAM cache. We extend the cache model of the simulator by introducing an SRAM cache model and its supporting modules. With this addition, designers can model hybrid multi-level cache hierarchies by using the die-stacked DRAM cache and SRAM caches. We provide a reference implementation of an optimized cache organization scheme for die-stacked DRAM cache along with a tag-cache unit that, together, reduces cache miss latency. To enable integration of the new features in the existing memory hierarchy, we make necessary changes to the memory controller. We provide functional verification of the new modules and put forward our approach for timing and power verification. We run random mixes of the SPEC2006 benchmarks and observe ±10% difference in simulation results.
Agents, agency and autonomy: a formal computational model

In recent years, there has been an explosion of interest in agents and multi-agent systems in a variety of areas including artificial intelligence, and software engineering. Agent technology, however, is still relatively young, and there is much debate and discussion over many important concepts and the relevant terminology. In particular, in a great deal of agent research, agents themselves are defined in wildly different ways, if at all, and this makes it extremely difficult to be explicit about their nature and functionality. These problems have arisen in part due to the lack of a common structure and language for describing and reasoning about both single agents and multi-agent systems, which might facilitate a rigorous organisation of the field. In response to this, a four-tiered theoretical formal framework for agent systems is proposed, which we use as a base from which to develop a detailed model of agents and their dimensions, the properties required by agents for effective operation, and the social organisation of multi-agent systems. This framework essentially comprises entities, objects, agents and autonomous agents, and specifies the relationships between them to provide a rigorous and detailed analysis of the structures underlying all such systems. Key to the understanding of this work is our overarching concern as computer scientists, of building computational systems. The development of formal theories and systems as proposed above is inadequate if they are irrelevant to the needs of practitioners. While the construction of any theory or model is unavoidably somewhat removed from the realities of software development, we address this concern by ensuring that the tools used are standard software engineering ones that are accessible and support practical development. In illustration of these ideas, and as a demonstration of the validity of the arguments made, we show how the framework and models developed can both provide a theoretical foundation and be applied directly to existing agent systems and theories: in particular, the Contract Net Protocol, AgentSpeak(L) and Social Dependence Networks.
A single day of bed rest, irrespective of energy balance, does not affect skeletal muscle gene expression or insulin sensitivity

What is the central question of this study? What are the initial metabolic and molecular events that underpin bed rest‐induced skeletal muscle deconditioning, and what is the contribution of energy balance? What is the main finding and its importance? A single day of bed rest, irrespective of energy balance, did not lead to overt changes in skeletal muscle gene expression or insulin sensitivity. More than 1 day of physical inactivity is required to observe the insulin resistance and robust skeletal muscle transcriptional responses associated with bed rest and consequent alterations in energy balance.
Immunization with a Synthetic Peptide Vaccine Fails to Protect Mule Deer (Odocoileus hemionus) from Chronic Wasting Disease

Chronic wasting disease (CWD) adversely affects both wild and captive cervid populations. A vaccine to prevent CWD would be a highly desirable tool to aid in disease management. To this end, we tested in mule deer a combination of CWD vaccines consisting of cervid prion peptide sequences 168-VDQYNNQNTFVHDC-182 and 145-NDYEDRYYRENMYRYPNQ-164 that had previously been shown to delay onset of clinical disease and increase survival in a mouse-adapted scrapie model. Thirteen captive mule deer (Odocoileus hemionus) were divided into vaccine (n=7) and control groups (n=6), and given prime and boost vaccinations intramuscularly 5 wk apart. Eight weeks postprime (3 wk postboost), all animals were challenged via natural exposure to an environment contaminated with infective CWD prions. Deer were monitored intermittently for prion infection by rectal and tonsil biopsies beginning 275 days postchallenge. All vaccinates responded to both peptide conjugates present in the combination vaccine as measured by enzyme-linked immunosorbent assay. However, all deer eventually became infected regardless of vaccine status.
Factor Structure and Reliability of Test Items for Saudi Teacher Licence Assessment.

The Saudi National Assessment Centre administers the Computer Science Teacher Test for teacher certification. The aim of this study is to explore gender differences in candidates’ scores, and investigate dimensionality, reliability, and differential item functioning using confirmatory factor analysis and item response theory. The confirmatory factor analysis results for 6 371 examinees’ scores of 66 multiple-choice items when grouped into three content domains showed that the test data were unidimensional (ability, trait). The domains were highly correlated (0.883 to 0.949) within this dimension. Data reliability estimated through latent variable modelling was acceptable at 0.848. Gender results for DIF signalled 13 items, five cases against males and eight cases against females; a finding of some balance in DIF direction against males and females. The study results confirm the validity of the Computer Science Teacher Test and support further refinement of multiple forms of the test.
Control of a myoelectric arm considering cooperated motion of elbow and shoulder joints

In order to improve the quality of life of persons who lost their limb due to an accident or a sickness, many myoelectric artificial arms have been proposed. To control the motion of the elbow joint of the myoelectric arm, the EMG signals of the biceps brachii and the triceps brachii muscles are frequently used as input signals. However, since both muscles are the biarticular muscles between the shoulder joint and elbow joint, the amount of the EMG signals of these muscles are affected by the motion of the shoulder joint. In this paper, a control method for a myoelectric arm is proposed in order to cancel the influence of the shoulder joint motion on EMG signals is removed by using the neuro-fuzzy modifier in order to realize proper elbow motion. The effectiveness of the proposed method has been evaluated by performing experiments.
Stochastic Three-Composite Convex Minimization with a Linear Operator

We develop a primal-dual convex minimization framework to solve a class of stochastic convex three-composite problem with a linear operator. We consider the cases where the problem is both convex and strongly convex and analyze the convergence of the proposed algorithm in both cases. In addition, we extend the proposed framework to deal with additional constraint sets and multiple non-smooth terms. We provide numerical evidence on graph-guided sparse logistic regression, fused lasso and overlapped group lasso, to demonstrate the superiority of our approach to the state-of-the-art.
Flexible and Efficient Decision-Making for Proactive Latency-Aware Self-Adaptation

Proactive latency-aware adaptation is an approach for self-adaptive systems that considers both the current and anticipated adaptation needs when making adaptation decisions, taking into account the latency of the available adaptation tactics. Since this is a problem of selecting adaptation actions in the context of the probabilistic behavior of the environment, Markov decision processes (MDPs) are a suitable approach. However, given all the possible interactions between the different and possibly concurrent adaptation tactics, the system, and the environment, constructing the MDP is a complex task. Probabilistic model checking has been used to deal with this problem, but it requires constructing the MDP every time an adaptation decision is made to incorporate the latest predictions of the environment behavior. In this article, we describe PLA-SDP, an approach that eliminates that runtime overhead by constructing most of the MDP offline. At runtime, the adaptation decision is made by solving the MDP through stochastic dynamic programming, weaving in the environment model as the solution is computed. We also present extensions that support different notions of utility, such as maximizing reward gain subject to the satisfaction of a probabilistic constraint, making PLA-SDP applicable to systems with different kinds of adaptation goals.
Design Considerations for 3D Printed, Soft, Multimaterial Resistive Sensors for Soft Robotics

Sensor design for soft robots is a challenging problem because of the wide range of design parameters (e.g., geometry, material, actuation type, etc.) critical to their function. While conventional rigid sensors work effectively for soft robotics in specific situations, sensors that are directly integrated into the bodies of soft robots could help improve both their exteroceptive and interoceptive capabilities. To address this challenge, we designed sensors that can be co-fabricated with soft robot bodies using commercial 3D printers, without additional modification. We describe an approach to the design and fabrication of compliant, resistive soft sensors using a Connex3 Objet350 multimaterial printer and investigated an analytical comparison to sensors of similar geometries. The sensors consist of layers of commercial photopolymers with varying conductivities. We characterized the conductivity of TangoPlus, TangoBlackPlus, VeroClear, and Support705 materials under various conditions and demonstrate applications in which we can take advantage of these embedded sensors.
Scaling of body composition to height: relevance to height-normalized indexes.

BACKGROUND Body weight scales to height with a power of ≈2, thus forming the basis of body mass index (weight/height(2)). The corresponding scaling of body composition to height has not been established in a representative sample of US adults. OBJECTIVE The aim of the study was to examine the scaling of weight, fat, fat-free mass, and bone mineral content to height. DESIGN Adult non-Hispanic white (NHW), non-Hispanic black (NHB), and Mexican American National Health and Nutrition Examination Survey (NHANES) participants were included in allometric analyses if they had complete age, weight, height, and body-composition data as measured by dual-energy X-ray absorptiometry. Powers of height in allometric regression models were developed for each measure and adjusted for age. RESULTS The analyses included 13,183 subjects (6699 NHW, 3015 NHB, and 3469 Mexican American). The scaling of weight to height across sex-race groups provided powers (mean ± SE) ranging from 1.85 ± 0.12 in Mexican American women to 2.48 ± 0.17 in Mexican American men. Powers of height for body composition similarly ranged widely and were often outside the 95% CI for a power of 2. Of the 3 body-composition measures, the mean age-adjusted powers of height rounded to 2 as the nearest integer in 16 of 18 sex-race groups. CONCLUSIONS Adult weight and body composition scale to height with variable age-adjusted powers that are sometimes outside the 95% CI for a power of 2 but frequently round to 2 as the nearest integer. These observations have implications for developing height-adjusted body-composition indexes.