Miracle-dz/UniFAR-FTUs huggingface dataset:
Active Extrinsic Calibration of Encoder and LIDAR for Robot

Robotic extrinsic calibration is a prerequisite for multi-sensor data fusion, which provides a transformation between coordinate systems of different sensors. For wheeled robots equipped with encoder and other sensors, the existing usual calibration process is completed by professional calibration engineers, or just randomly, which is not efficient and may cause motion degradation or unknown uncertainty. In this paper, we propose an active extrinsic calibration algorithm for encoder and 2D LIDAR. The trajectory in the calibration process is planned in belief space, which is optimized to get the calibration value with the minimized uncertainty. Our method doesn’t rely on maximum-likelihood observations assumption, and can also work in discontinuous sensing domain. The simulation is conducted to validate the method.
Monolith solid porous carbon approach derived from Garcinia Xanthochymus for high-performance electrode material of supercapacitor

Monolith solid porous carbon approach was prepared from biomass based from Garcinia Xanthochymus by using a high-temperature pyrolysis both of carbonization and physical activation in one stage integrated stage. Effect of the chemical impregnated of the activated carbon was studied. The N2 gas environment was used as inert gas of carbonization, and the CO2 gas was used as a physical activation atmosphere. The dimension of solid coin porous carbon was evaluated in the before and after high-temperature pyrolysis based on reduction of mass, diameter, and thickness. Furthermore, the porous carbon obtained also reviewed microstructure properties by using X-ray diffraction technique. Moreover, the electrode materials were evaluated their electrochemical performances at cyclic voltammetry (CV) and galvanostatic charge discharge (GCD) in different scanning rate of 1, 2, 5, and 10 mV s−1, at window voltage of 0.0-1.0V and current density of 1.0 A g−1. The optimum capacitive properties were found as high as 159 F g−1 at aqueous electrolyte of 1 M H2SO4. The energy density was reviewed of 9.91 Wh kg−1 at optimum power density of 97.17 W kg−1.
Topology Identification of the Hindmarsh-Rose Model via Deterministic Learning

In this paper, the complex dynamic behavior of the Hindmarsh-Rose (HR) model which characterizes the neuron cell is analyzed numerically. And the unknown topology of the system in dynamic environment is locally accurately identified based on the deterministic learning (DL) algorithm. Firstly, the influence of different parameters on the dynamic behavior of the HR model are investigated. Then, the nonlinear dynamics of the HR model under unknown dynamic environment is locally accurately identified. In addition, the identified system dynamics can be stored in the form of constant neural network. The achievement of this work can provide more incentives and possibilities for the application of HR model in clinic and other related researches. Simulation studies are included to demonstrate the effectiveness.
Mapping Grayscale Images to Colour Space Using Deep Learning

People are used to exploring grayscale images in their family albums but it is difficult to grasp the reality without colours. Luckily, with advancements in Machine Learning it has been possible to solve problems previously thought impossible. The authors aim to automatically colourize grayscale images using a subset of Machine Learning called Deep Learning. The system will be trained on an image dataset and given an input grayscale image the model will be able to assign aesthetically believable colours. A grayscale photograph has been provided; our approach solves the problem of visualizing a reasonable colour version of the grayscale picture. This issue is undoubtedly under controlled; therefore earlier methods to this problem have either counted majorly on user interaction or it leads to in unsaturated colourizations. The authors put forward a completely automatic approach that will try to produce realistic and vibrant colourizations as much as possible. The proposed system has been applied as a feed-forward in a Convolutional Neural Network and has been trained on over twenty thousand colour images currently.
Antioxidant and Anti-Infl ammatory Eff ects of Nicotinamide Adenine Dinucleotide (NAD+) Against Acute Hepatorenal Oxidative Injury in An Experimental Sepsis Model [1]

The aim of this study is to determine the antioxidant and anti-infl ammatory eff ects of Nicotinamide Adenine Dinucleotide (NAD+) in preventing multi-organ damage caused by sepsis. Twenty-eight male Wistar-albino rats were randomly divided into four groups. The study groups comprised Sham group, sepsis group (CLP), sepsis + 100 mg/kg NAD+ (CLP+N100) and sepsis + 300 mg/kg NAD+ group (CLP+N300). Sepsis was induced by the cecum ligation perforation (CLP) method. NAD+ was administered intraperitoneally for five days before cecum perforation and 6 h after operation. Serum, liver and kidney tissues were taken from the rats 24 h after the operation. MDA, GSH, CAT, TNF-α, IL-6, IL-1β, and caspase-3 parameters were measured in tissue samples with biochemical and immunohistochemical methods. In the histopathological and immunohistochemical examination, increases in TNF-α, IL-6, IL-1β, and caspase-3 expressions were observed in the liver and kidney tissues of the CLP group and severe damage was seen in tissue morphology (P<0.001). Hepatorenal injury was signifi cantly decreased in the treatment groups. Sepsis increased MDA levels in all tissues, but significantly decreased GSH and CAT activities. While NAD+ administration significantly increased GSH and CAT activity in the liver and kidney tissues, it caused a significant decrease in MDA levels. This study shows that nicotinamide may be a potent therapeutic agent for the treatment of sepsis.
Coordination of a single-supplier multi-retailer supply chain via joint ordering policy considering incentives for retailers and utilizing economies of scale

Lead-time fluctuations cause a low supply chain service level through increasing stock-outs. Lack of the supplier’s awareness of the retailers’ ordering policy is one of the main reasons for the lead-time fluctuations. In this paper, a twoechelon supply chain including single supplier, multiple retailers is studied under two scenarios of decentralized and centralized decision-making. In the first scenario, each retailer independently uses a continuous review inventory policy and the supplier does not know when each retailer will order. This policy prolongs order fulfillment by the supplier and increases order-processing costs. In the second scenario, retailers are encouraged to enter into a joint cooperation plan and change their ordering policy from independent continuous review policies to a joint periodic review policy. In this case, the supply chain can utilize the benefits of economies of scale via integrating and shipping several retailers’ orders. The study also determines range of the acceptable lead-time reduction by supplier and retailers for participating in the joint cooperation plan. The results show that joint cooperation plan creates more benefits for the supply chain in terms of cost and service level.
Lab 4: Prefetcher Design and Analysis

In this lab you will implement and evaluate multiple different prefetchers using ChampSim [1], a tracebased microarchitectural simulator written in C++. ChampSim models a modern high-performance out-of-order (OoO) core and enables evaluating ideas on classical problems of microarchitectures like data prefetching, branch prediction and cache replacement policies. Unlike the timing simulator you used in previous labs, ChampSim does not natively run an executable/assembly program but uses an instruction trace, extracted out from an application, to simulate the processor microarchitecture model.
The Relationship between Portfolio Size and Risk Based on Investor's Utility

In actual investment activities, rational investors always allocate their funds to different assets in order to diversify their investment. From the perspective of investor’s utility, this paper studies the relationship between portfolio size and risk under different risk aversion levels by repeating stochastic simulations on the components of CSI 300 index in Chinese stock market. Empirical studies show that: Compared with the equal-weighted method, the idea based on the maximization of the investor's utility function can diversify most of the unsystematic risk with fewer assets. The lower limit of the optimal portfolio risk of the risk seeker is higher, and is lower for the risk averter.
Game over?

We present a systematic literature review of gamification in information retrieval, identifying 32 peer-reviewed articles spanning a period from 2012 to present day. Our study highlights that, with the exception of crowdsourcing relevance judgments, very few studies have investigated the application of gamification to search systems. Moreover, in articles that we would strictly define as being related to gamification (i.e. not games with a purpose or serious games), a majority did not present any real-world systems, user studies or empirical results. In numerous other fields, gamification has been used to successfully increase user engagement and productivity. We argue that the potential of gamification has been overlooked in information retrieval and, in particular, in interactive search settings. We conclude by discussing experimental obstacles and the potential benefits of evaluating gamification strategies in future interactive search systems.
Forest Applications 2

The application of polarimetric Synthetic Aperture Radar (SAR) to forest observation for mapping, classification and parameter estimation (especially biomass) has a relatively long history. The radar penetration through forest volume, and hence the multi-layer nature of scattering models, make fully polarimetric data the observation space enabling a robust and full inversion of such models. A critical advance came with the introduction of polarimetric SAR interferometry, where polarimetry provides the parameter diversity, while the interferometric baseline proves a userdefined entropy control as well as spatial separation of scattering components, together with their location in the third dimension (height). Finally, the availability of multiple baselines leads to the full 3-D imaging of forest volumes through TomoSAR, the quality of which is again greatly enhanced by the inclusion of polarimetry. The objective of this Chapter is to review applications of SAR polarimetry, polarimetric interferometry and tomography to forest mapping and classification, height estimation, 3-D structure characterization and biomass estimation. This review includes not only models and algorithms, but it also contains a large number of experimental results in different test sites and forest types, and from airborne and space borne SAR data at different frequencies.
Mental Imagery and Polysemy Processing

Recent research in psycholinguistics suggests that language processing frequently involves mental imagery. This paper focuses on visual imagery and discusses two issues regarding the processing of polysemous words (i.e.words with multiple related meanings or senses) — co-predication and sense-relatedness. It aims to show how mental imagery can illuminate these two issues.
Leveraging Structural Information to Ease Approximation Management

Current approximation management approaches establish the correlation between approximate application configurations and overall quality outcomes and resource requirements through off-line training. At runtime, an approximation manger uses the resulting profiles to select an appropriate configuration. The time needed for the training phase of these largely black box approaches can range from hours to even days depending on the size of the configuration search spaces. Training would have to be repeated when porting an application to a new platform, or when executing in unknown environments, making the approach impractical in many cases. Another drawback of the black box approach is the lack of support for application users to express quality preferences for different aspects of an application, which together determine the subjective overall user experience. This paper introduces RAPID, a framework for writing a class of approximate applications where the application structure is exposed and utilized. Structural information is used (a) by the developer to significantly speed-up the configuration training phase, and (b) by the application user to express relative preferences of different aspects of an application. RAPID formulates configuration selection as an integer programming problem that optimizes quality while respecting resource budgets. Experimental results on six different applications show the benefits of leveraging structural information in approximation management.
AUC Knowledge Fountain AUC Knowledge Fountain

Based on cognitive approaches to second language learning, the thesis emphasizes the correspondence between MSA and ECA, and argues for simultaneous teaching of the two varieties from basic levels onward. Development in CALL technology can effectively implement cognitive principles and realize the goals of the integrated approach in the field of TAFL. The thesis describes a software program designed for teaching beginners MSA and ECA simultaneously using idioms as a unifying conceptual element.
Rejection of Competition Particle Swarm Optimization

In order to enrich the population of particle swarm optimization algorithm diversity,a competitive mechanism was proposed based on the rejection of the particle swarm optimization algorithm.Modified algorithm cancelled the global optimal value of "gbest" in traditional PSO algorithm,and made the current population of all the particles and the previous generation of elite population particles to compete together by setting the competition area.Through adopting competitive strategy of fitness and selection strategy of fitness,it ensures the population diversity and avoids the possibility of initial algorithm into local minima.At the same time,through the competition between particle radiuses of the dynamic adjustment of rejection,it makes the late algorithm convergence speed and accuracy improved.By comparing the simulation tests of several types of typical function,it shows that the improved algorithm has good global searching ability and convergence speed,much better than the traditional algorithms.
Stimulation of acupoint P6 before induction of anesthesia to prevent postoperative nausea and vomiting; a randomized control trial

Background & Aims: Laparoscopic surgery has been associated with troublesome postoperative nausea and vomiting (PONV) in patients. Various regimens have been tried by the anesthesiologist to prevent PONV in laparoscopic cholecystectomy (LapChole). The primary objective of this study was to determine the role of stimulation of acupoint P6 for this purpose in patients undergoing LapChole at our institution. Methodology: A randomized clinical trial was conducted at the Aga Khan University Hospital, Karachi, Pakistan. The research setting took place at the Post Anesthesia Care Unit (PACU) of the hospital. A total of 84 participants undergoing LapChole, ASA Status I or II, were enrolled and randomly allocated into two groups: 41 in control and 43 in the intervention group. In the intervention group, a commercially available band – PressureRightTM was applied at the wrist and the beads were placed exactly at P6 point. In the control group, the band was applied at the wrist with the beads placed on the dorsal surface. In both groups, acupressure band was applied before the induction of anesthesia in the waiting area of the operating room and continued during the intraoperative period and six hours postoperatively. Measurements: Postoperatively, patients were monitored for postoperative nausea and vomiting at the time of arrival in post anesthesia care unit [PACU], after half hour, three hour and six hours postoperatively using numerical pain scale [NPS] from 1-10 [1=none, 2-5=mild, 6-7=moderate, 8-10=severe] for PONV separately. Main Results: The results of PONV in PACU, at 30 minutes, three hours and six hours postoperatively showed an insignificant difference in intervention and control group. The frequencies of mild to moderate PONV were gradually reduced in both groups but the reduction was more pronounced in the control group than in the intervention group. There were no reported events of severe PONV at three hours and six hours postoperatively in both groups. The use of rescue antiemetics was statistically insignificant between the two groups [P = 0.744]. Conclusions: Our study reports that acupressure at Neiguan P6 point starting before the induction of anesthesia till 6 hours postoperative has no significant role in preventing PONV in patients undergoing laparoscopic cholecystectomy. Rescue antiemetics were required in both groups with a similar frequency.
Evaluating the detection ability of a range of epistasis detection methods on simulated data for pure and impure epistatic models

Background Numerous approaches have been proposed for the detection of epistatic interactions within GWAS datasets in order to better understand the drivers of disease and genetics. Methods A selection of state-of-the-art approaches were assessed. These included the statistical tests, fast-epistasis, BOOST, logistic regression and wtest; swarm intelligence methods, namely AntEpiSeeker, epiACO and CINOEDV; and data mining approaches, including MDR, GSS, SNPRuler and MPI3SNP. Data were simulated to provide randomly generated models with no individual main effects at different heritabilities (pure epistasis) as well as models based on penetrance tables with some main effects (impure epistasis). Detection of both two and three locus interactions were assessed across a total of 1,560 simulated datasets. The different methods were also applied to a section of the UK biobank cohort for Atrial Fibrillation. Results For pure, two locus interactions, PLINK’s implementation of BOOST recovered the highest number of correct interactions, with 53.9% and significantly better performing than the other methods (p = 4.52e − 36). For impure two locus interactions, MDR exhibited the best performance, recovering 62.2% of the most significant impure epistatic interactions (p = 6.31e − 90 for all but one test). The assessment of three locus interaction prediction revealed that wtest recovered the highest number (17.2%) of pure epistatic interactions(p = 8.49e − 14). wtest also recovered the highest number of three locus impure epistatic interactions (p = 6.76e − 48) while AntEpiSeeker ranked as the most significant the highest number of such interactions (40.5%). Finally, when applied to a real dataset for Atrial Fibrillation, most notably finding an interaction between SYNE2 and DTNB.
It’s Not Always the Appendix: An Unusual Case of Right Lower Quadrant Pain in an Emergency Department Patient Due to Iliopsoas Bursitis From Uncommon Sleep Positioning

Patients presenting to the emergency department (ED) with right lower quadrant pain will typically have acute appendicitis at the top of a limited differential. We present an unusual case of right lower quadrant pain in a 45-year-old female emergency department patient. She was referred from primary care for evaluation of suspected appendicitis with a final diagnosis of iliopsoas bursitis caused by sleep positioning. Knowledge of the appropriate evaluation and treatment of this unusual ED presentation is important for accurate diagnosis and appropriate referral to avoid unnecessary patient morbidity.
Lift the Veil of Breast Cancers Using 4 or Fewer Critical Genes

Known genes in the breast cancer study literature could not be confirmed whether they are vital to breast cancer formations due to lack of convincing accuracy, although they may be biologically directly related to breast cancer based on present biological knowledge. It is hoped vital genes can be identified with the highest possible accuracy, for example, 100% accuracy and convincing causal patterns beyond what has been known in breast cancer. One hope is that finding gene-gene interaction signatures and functional effects may solve the puzzle. This research uses a recently developed competing linear factor analysis method in differentially expressed gene detection to advance the study of breast cancer formation. Surprisingly, 3 genes are detected to be differentially expressed in TNBC and non-TNBC (Her2, Luminal A, Luminal B) samples with 100% sensitivity and 100% specificity in 1 study of triple-negative breast cancers (TNBC, with 54 675 genes and 265 samples). These 3 genes show a clear signature pattern of how TNBC patients can be grouped. For another TNBC study (with 54 673 genes and 66 samples), 4 genes bring the same accuracy of 100% sensitivity and 100% specificity. Four genes are found to have the same accuracy of 100% sensitivity and 100% specificity in 1 breast cancer study (with 54 675 genes and 121 samples), and the same 4 genes bring an accuracy of 100% sensitivity and 96.5% specificity in the fourth breast cancer study (with 60 483 genes and 1217 samples). These results show the 4-gene-based classifiers are robust and accurate. The detected genes naturally classify patients into subtypes, for example, 7 subtypes. These findings demonstrate the clearest gene-gene interaction patterns and functional effects with the smallest numbers of genes and the highest accuracy compared with findings reported in the literature. The 4 genes are considered to be essential for breast cancer studies and practice. They can provide focused, targeted researches and precision medicine for each subtype of breast cancer. New breast cancer disease types may be detected using the classified subtypes, and hence new effective therapies can be developed.
Socioeconomic status and sleep disturbances among pediatric population: a continental systematic review of empirical research

ABSTRACT To this day, no consensus has been established on the definition and the conceptualization of the socioeconomic status (SES), since all the available studies on the relation between SES and health did not use the same conceptual framework and operationalization to assess SES. While literature reported that SES markers (such as income, social support networks, education, employment or occupation) influence the health of populations by shaping living conditions; empirical research does not tell us which SES markers affect more strongly the sleep components of the individuals, as well as which sleep disorders (SD) are affected and how. Even though several original studies have tried to assess how changes in socioeconomic status of parents may affect the psychosocial environment and mental health of an individual directly or through his community, no systematic reviews on the influence of SES on children’s sleep are available. This systematic review make an update on the different measures of SES and sleep disturbances used for pediatric population across the different regions of the world. Recommendations for a future standardization of SES measures is proposed, for a better understanding of its influence on sleep disturbances.
Near-death experiences and the importance of transparency in subjectivity, ontology and epistemology

I read with great interest and diligence the paper from Peinkhofer et al. regarding how near-death experiences (NDEs) could be understood as the evolutionary product of death feigning (thanatosis). The authors provided this rather novel and interesting hypothesis regarding the evolutionary origin of NDEs, a topic that has most definitely received little attention in research.Could there be anadaptative or survival benefit biologically justifying the rich, subjective perceptions of the NDE? The authors of the paper provide a straightforward approach with a predefined methodology to test their hypothesis. But after this point, the argumentation and evidence brought forward by their quite rigorous approach, in my humble opinion, is less than compelling.