Miracle-dz/UniFAR-FTUs huggingface dataset:
PAPI software-defined events for in-depth performance analysis

The methodology and standardization layer provided by the Performance Application Programming Interface (PAPI) has played a vital role in application profiling for almost two decades. It has enabled sophisticated performance analysis tool designers and performance-conscious scientists to gain insights into their applications by simply instrumenting their code using a handful of PAPI functions that “just work” across different hardware components. In the past, PAPI development had focused primarily on hardware-specific performance metrics. However, the rapidly increasing complexity of software infrastructure poses new measurement and analysis challenges for the developers of large-scale applications. In particular, acquiring information regarding the behavior of libraries and runtimes—used by scientific applications—requires low-level binary instrumentation, or APIs specific to each library and runtime. No uniform API for monitoring events that originate from inside the software stack has emerged. In this article, we present our efforts to extend PAPI’s role so that it becomes the de facto standard for exposing performance-critical events, which we refer to as software-defined events (SDEs), from different software layers. Upgrading PAPI with SDEs enables monitoring of both types of performance events—hardware- and software-related events—in a uniform way, through the same consistent PAPI. The goal of this article is threefold. First, we motivate the need for SDEs and describe our design decisions regarding the functionality we offer through PAPI’s new SDE interface. Second, we illustrate how SDEs can be utilized by different software packages, specifically, by showcasing their use in the numerical linear algebra library MAGMA-Sparse, the tensor algebra library TAMM that is part of the NWChem suite, and the compiler-based performance analysis tool Byfl. Third, we provide a performance analysis of the overhead that results from monitoring SDEs and discuss the trade-offs between overhead and functionality.
Prevalence of capsicum grey mould and characterization of the pathogen associated under protected cultivation in Himachal Pradesh

Gray mould of capsicum (Capsicum annuum var. grossum) caused by Botrytis cinerea Pers. is one of the most destructive diseases of winter season capsicum under polyhouse conditions in Himachal Pradesh. During 2013-14, different polyhouses of Bilaspur, Hamirpur, Kangra and Kullu districts of Himachal Pradesh were surveyed and disease severity was observed between 18.0-52.0 per cent in all the polyhouses. Maximum disease (52.0%) was recorded at Palampur in Kangra district whereas, minimum (18.0%) at Ghumarwin in Bilaspur. Studies on symptomological, morphological and cultural characteristics identified the associated pathogen as Botrytis cinerea. The identity of pathogen was also confirmed through rDNA analysis by targeting ITS1 and region using primer pair ITS1 and 4 which generated an amplicon of ~500bp and phylogentic analysis showed 99 per cent homology of the isolate with Botrytis cinerea.
A Smart Glove to Track Fitness Exercises by Reading Hand Palm

Medical studies have intensively demonstrated that sports activity can enhance both the mental and the physical health of practitioners. In recent years, fitness activity became the most common way to motivate and engage people in sports activity. Recently, there have been multiple attempts to elaborate on the “ideal” IoT-based solution to track and assess these fitness activities. Most fitness activities (except aerobic activities like running) involve one or multiple interactions between the athlete’s hand palms and body or between the hand palms and the workout materials. In this work, we present our idea to exploit these biomechanical interactions of the hand palms to track fitness activities via a smart glove. Our smart glove-based system integrates force-sensitive resistor (FSR) sensors into wearable fitness gloves to identify and count fitness activity, by analyzing the time series of the pressure distribution in the hand palms observed during fitness sessions. To assess the performance of our proposed system, we conducted an experimental study with 10 participants over 10 common fitness activities. For the user-dependent activity recognition case, the experimental results showed 88.90% of the F score for overall activity recognition. The result of leave-one-participant-out cross-validation showed an F score ranging from 58.30% to 100%, with an average of 82.00%. For the exercise repetition count, the system achieved an average counting error of 9.85%, with a standard deviation of 1.38.
A social internet of vehicles sharing SIoT relationships

In recent years, the concept of social networking combined with the Internet of Vehicles has brought to the definition of the Social IoV (SIoV) paradigm, i.e., a social network where every vehicle is capable of establishing social relationships in an autonomous way with other vehicles or road infrastructure equipment. In SIoV, social networking is applied to vehicular networks according to how social ties are built upon, i.e., either among vehicles or humans. This creates a twofold nature of SIoV i.e., both based on human social relationships, and as an instance of the Social Internet of Things (SIoT). This twofold nature of SIoV is not in contrast with itself, but allows to distinguish different applications and use-cases. This paper analyzes the SIoT-based social relations in a vehicular network scenario for establishing a Social Internet of Vehicles and providing insights on this growing research area.
Analysis and Controlling of Manufacturing Service Ecosystem: A Research Framework Based on the Parallel System Theory

With the development of cloud manufacturing technology, Manufacturing Service Ecosystem (MSE) is emerging as a typical complex cyber-social system. On the one hand, service strategy (cyber layer) drives the evolution of manufacturing community (social layer); on the other hand, the initial conditions of manufacturing community (social layer) affect the performance of service strategy. In order to promote the evolution of MSE in the expected direction, it is necessary to clarify the loop feedback mechanism between heterogeneous networks. However, how to analyze and intervene in the possible evolution directions of MSE has become a serious challenge in the field. In order to face this challenge, this paper proposes a parallel system theory-based research framework to study the evolution and controlling of MSE. First, the corresponding digital system of MSE is constructed from the perspective of supply and demand matching. Second, the specific computational experiment is executed to present the effect of different service strategies (cyber layer) and different initial conditions (social layer) on the evolution of MSE. Furthermore, the comparison of experiment results with real data verifies the credibility of the proposed approach. It demonstrates that our approach can provide a new way for analyzing the complexity of MSE.
Mindfulness therapies & recent biological science : What matters amidst the hype, for ineffable me, irreducible you?

Mindfulness Therapy has become part of the stable of psychotherapy since the 1980s when Jon Kabat-Zinn established Mindfulness Based Stress Reduction (MBSR) as an effective therapeutic intervention. That programme melded roots from Buddhist Psychology with Western Cognitive Science and Group Therapy. Some terms were deliberately loosely defined at that time, including mindfulness itself. So how is recent research into mindfulness, including cross-disciplinary research with biological neuroscience and other scientific disciplines and the recent cultural popularity of mindfulness, impacting on mindfulness therapy and its clinical delivery? What of recent critiquing of those loose definitions around mindfulness and its research base? This paper explores how mindfulness therapy has been challenged and developed over the last 35 years by biological science and research. This paper discusses cross-disciplinary research involving mindfulness, the inherent difficulties of such cross-disciplinary conversations, how this research is shaping the manner in which therapy that uses aspects of mindfulness is evolving, including highlighting some of the risks and opportunities that may not be widely acknowledged to date.
SHARED TASKS AND COMPARATIVE EVALUATION IN NATURAL LANGUAGE GENERATION

Today’s NLG efforts should be compared against actual human performance, which is fluent and varies randomly and with context. Consequently, evaluations should not be done against a fixed ‘gold standard’ text, and shared task efforts should not assume that they can stipulate the representation of the source content and still let players generate the diversity of texts that the real world calls for. 1 Minimal competency The proper point of reference when making an evaluation of the output of a natural language generation (NLG) system is the output of a person. With the exception of the occasional speech error or other predicable disfluencies such as stuttering or restarts, people speak with complete command of their grammar (not to mention their culturally attuned prosodics), and with complete command of their discourse context as it shapes the coherence of what they say and the cohesion of how they say it. Any NLG system today that does not use pronouns correctly (assuming they use them at all), that does not reduce complex NPs when they describe subsequent references to entities already introduced into the discourse, that does not reduce clauses with common subjects when they are conjoined, or that fails to use any of the other ordinary cohesive techniques available to them in the language they are using is simply not in the running. Human-level fluency is the entrance ticket to any comparative evaluation of NLG systems.
A Price-Maker/Price-Taker Model for the Operation of Battery Storage Systems in Electricity Markets

The goal of this paper is to develop models for estimating the potential profit of a battery storage system that provides multiple services in a competitive electricity market. We assume the size of the battery is small relative to the energy market volume and its actions do not impact the energy market outcomes; thus, it is a price-taker in the energy market. However, considering the relatively smaller market volume for ancillary services, we consider the battery’s strategies to impact the outcomes of the markets for frequency regulation service, spinning reserve, and non-spinning reserve. An optimization model is proposed considering the uncertainties in energy prices, the offers of ancillary services by competitors, and the energy deployment in ancillary services markets. We employ robust and stochastic optimization approaches to account for the different nature of each uncertain variable. The scheduling is done in day-ahead and is later refined closer to real time. Numerical results are provided based on real-life data.
Power flow calculation based on local controller impedance features for the AC microgrid with distributed generations

In AC microgrid, since the impedance features of the power electronic converter are not considered, the accuracy of power flow calculation is degraded. Thus, this study, firstly presents a power flow calculation approach based on local controller impedance features for the AC microgrid consisting of numerous distributed generations. Firstly, the source-side equivalent output impedance matrix of the converter is estimated by small-signal perturbation approach and the equivalent transformation among the dq axis, axis and abc axis. Then, the bus–bus line impedance model is proposed by converting the bus line equivalent impedance model. Moreover, the equivalent impedance model is embedded into Jacobian matrix iterative process to improve the accuracy of the power flow calculation approach. Eventually, the power flow calculation based on local controller impedance features is simulated by IEEE 4-bus MATLAB/Simulink test system, PG&E 69-bus test system and IEEE 118-bus test system, indicating that the proposed power flow calculation approach is more accurate.
Dynamic RGB-D Mapping

Localization and mapping has been an area of great importance and interest to the robotics and computer vision community. It has traditionally been accomplished with range sensors such as lasers and sonars. Recent improvements in processing power coupled with advancements in image matching and motion estimation has allowed development of vision based localization techniques. Despite much progress, there are disadvantages to both range sensing and vision techniques making localization and mapping that is inexpensive and robust hard to attain. With the advent of RGB-D cameras which provide synchronized range and video data, localization and mapping is now able to exploit both range data as well as RGB features. This thesis exploits the strengths of vision and range sensing localization and mapping strategies and proposes novel algorithms using RGB-D cameras. We show how to combine existing strategies and present through evaluation of the resulting algorithms against a dataset of RGB-D benchmarks. Lastly we demonstrate the proposed algorithm on a challenging indoor dataset and demonstrate improvements where either pure range sensing or vision techniques perform poorly.
A State Estimation Method Based on Integration of Linear and Extended Kalman Filters

Abstract The Linear Kalman Filter (LKF) and Extended Kalman Filter (EKF) as correction-prediction observers are two common model-based filters, which are widely used to estimate the unmeasurable states and parameters in the chemical processes. The main disadvantages of LKF and EKF are low accuracy and reliability of estimation and high computational time, respectively. The main object of this work is the modification of conventional Kalman filter to cover the disadvantages of LKF and EKF. The proposed method is planned based on the systematic updating the Jacobian matrix of estimator applied on the nonlinear systems. In this regard, a linearity index is defined based on the characteristics of outputs to determine switching time between LKF and EKF to avoid unnecessary updating the Jacobian matrix. The performance of the proposed method is compared with the LKF and EKF considering four chemical benchmarks as the nonlinear state space models. Altought EKF and proposed filter present similar precision to estimate unmeasurable states, the proposed method has a lower computational time.
Mr.Wolf: An Energy-Precision Scalable Parallel Ultra Low Power SoC for IoT Edge Processing

This paper presents Mr.Wolf, a parallel ultra-low power (PULP) system on chip (SoC) featuring a hierarchical architecture with a small (12 kgates) microcontroller (MCU) class RISC-V core augmented with an autonomous IO subsystem for efficient data transfer from a wide set of peripherals. The small core can offload compute-intensive kernels to an eight-core floating-point capable of processing engine available on demand. The proposed SoC, implemented in a 40-nm LP CMOS technology, features a 108- $\mu \text{W}$ fully retentive memory (512 kB). The IO subsystem is capable of transferring up to 1.6 Gbit/s from external devices to the memory in less than 2.5 mW. The eight-core compute cluster achieves a peak performance of 850 million of 32-bit integer multiply and accumulate per second (MMAC/s) and 500 million of 32-bit floating-point multiply and accumulate per second (MFMAC/s) −1 GFlop/s—with an energy efficiency up to 15 MMAC/s/mW and 9 MFMAC/s/mW. These building blocks are supported by aggressive on-chip power conversion and management, enabling energy-proportional heterogeneous computing for always-on IoT end nodes improving performance by several orders of magnitude with respect to traditional single-core MCUs within a power envelope of 153 mW. We demonstrated the capabilities of the proposed SoC on a wide set of near-sensor processing kernels showing that Mr.Wolf can deliver performance up to 16.4 GOp/s with energy efficiency up to 274 MOp/s/mW on real-life applications, paving the way for always-on data analytics on high-bandwidth sensors at the edge of the Internet of Things.
Use of unmanned aerial vehicles (UAVs) and photogrammetric image analysis to quantify spatial proximity in beef cattle

Spatial proximity is an important metric in cattle behaviour, which is used to study social structure, dyadic relationships, as well as grazing and maternal behaviours. We developed an efficient, novel, non-invasive method to quantify the spatial proximity of beef cattle by using UAV-based image acquisition and photogrammetric analysis. Orthomosaics constructed by images obtained from UAVs were used to measure, with an accuracy of ±1.96 m (95% likelihood), the inter-individual distances between cows and calves. Aerial videos of the calves and their dams, held in a 5 ha pasture, were made over four days using UAVs. We used two UAVs to video-capture the following: (i) the location of all individuals (UAV flown at 100 m) and (ii) the identity of cow–calf pairs (UAV flown at 15–30 m). Still-images extracted from the UAV-acquired video screenshots were used to produce orthomosaics. The orthomosaics captured all the cows and calves in a single image, from which we measured the distance between related and non-related cow–calf pairs. This UAV-based orthomosaic method clearly showed that members of related pairs were closer than non-related ones, and that the distance was greater in the evening, demonstrating the utility of UAVs to accurately measure cattle spatial proximity.
Morphotropic phase boundary, field-induced transitions and dielectric properties of PbxSr1-xTiO3

Abstract A low-temperature rhombohedral-tetragonal morphotropic phase boundary (MPB) is discovered using a phenomenological approach in PbxSr1-xTiO3. Monoclinic structures are readily induced under electric field around the MPB which can be up-shifted to near room temperature by applying a negative pressure. Field-induced phase transition, polarization change, and dielectric response are computed. Under stress-free conditions monoclinic-orthorhombic transition can be induced in Pb0.5Sr0. 5TiO3 by applying an electric field, giving rise to stronger field dependence of room-temperature dielectric constants as compared to Pb0.2Sr0. 8TiO3. Under negative pressure, however, in Pb0.2Sr0. 8TiO3 cubic-tetragonal transition can be induced, around which dielectric anomaly is sensitive to electric field, enabling significant enhancement of dielectric tunability.
Analysis of Whole Exome Sequence Data in Affected Cousin Pairs from High-Risk Alzheimer's Pedigrees

Analysis of Whole Exome Sequence Data in Affected Cousin Pairs from High-Risk Alzheimer’s Pedigrees Lyndsay Ann Staley Department of Biology, BYU Master of Science Genetic factors account for about half of Alzheimer’s Disease (AD) risk and only about a quarter of that heritability is accounted for by known variants. Family based approaches to understanding AD genetics may be an effective way to identify additional risk factors. Here we report the results of whole exome sequencing (WES) and analyses done on pairs of AD affected cousins from 19 families from the Utah Population Database (UPDB) with a statistical excess of AD risk. WES variants passing quality control were additionally filtered by population frequency (minor allele < 0.01) and concordance between cousin pairs, resulting in 564 variants shared by at least one pair of cousins. For each of these variants we conducted in depth annotations using Ingenuity Variant Analysis (IVA), Wellderly Data Allele Frequencies, and literature searches. To further aid in variant prioritization we analyzed each variant for association with Age at Onset of AD, AD Risk, CSF AB42, CSF Tau, CSF PTau and Rate of Disease Decline in data from the Alzheimer’s Disease Genetics Consortium (ADGC) and from the Knight Alzheimer’s disease research center. Statistical analyses were conducted using PLINK. Twelve variants (rs201665195, rs28933981, rs148294193, rs147599881, rs61729902, rs140129800, rs191804178, rs200290640, rs199752248, rs45541434, rs141402160 and rs140914494) in eight genes (ABCA7, TTR, PELI3, FCHO1, SNAP91, COX6A2, MUC16, PIDD1, SYT5 and NOTCH3) were prioritized using a clear pipeline of IVA filters and the additional analysis information. We propose that these genes and variants are the most interesting for follow-up based on current knowledge. This family-based approach to finding rare AD variants adds to a growing body of research suggesting a role for NOTCH3 in late-onset AD. This approach replicated two known AD risk variants and also implicated novel putative risk AD variants and genes. These results suggest that further application of this method of using pairs of cousins may result in additional insights into AD genetics and the ability to find novel rare, causal AD variants.
A Forward Equation for Computing Derivatives Exposure

We derive a forward equation for computing the expected exposure of financial derivatives. Under general assumptions about the underlying diffusion process, we give an explicit decomposition of the exposure into an intrinsic value which can be directly deduced from the term structure of the forward mark-to-market, and a time value which expresses the variability of the future mark-to-market. Our approach is inspired by Dupire’s equation for local volatility and leads to an ordinary differential equation qualifying the evolution of the expected exposure with respect to the observation dates. We show how this approach can be linked with local times theory in dimension one and to the co-area formula in a higher dimension. As for numerical considerations, we show how this approach leads to an efficient numerical method in the case of one or two risk factors. The accuracy and time-efficiency of this forward representation in small dimension are of special interest in benchmarking XVA valuation adjustments at trade level.
A Cognitive Study of Metaphors in the Glorious Qur’an: From a Linguistic to a Conceptual Approach

The current study investigates the use of conceptual metaphors in the glorious Qur’an, focusing on four abstract concepts represented in the Qur’an through metaphors. These concepts are REWARD, HUMILITY, HYPOCRISY, and ARROGANCE, which recur in many positions in the Qur’an. The researcher takes up selected Qur’anic verses that carry these abstract concepts and analyzes them at two levels: linguistic and conceptual. The study’s main theoretical contribution is to show how a linguistic approach can be transformed into a conceptual one and how this enriches our understanding of abstraction. The linguistic analysis of the verses is supported by translations of Qur’anic meanings, interpretation of the verses, and cultivating the use of Arabic and English dictionaries. To perform the cognitive analysis, the researcher uses Lakoff and Johnson’s Conceptual Metaphor Theory (CMT), which examines metaphor from two domains: the target domain, which is the abstract concept to be explained and the source domain, which is the mental mapping that helps us understand that abstract concept. The study finds that metaphors in the Qur’an are meant to be understood not only linguistically but conceptually too. It concludes by emphasizing the significance of a conceptual approach to the study of metaphor in the Qur’an not only for conceptual metaphor theory but also for the interpretation of the Qur’an and for Arabic linguistics.