Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploiting Locality in Graph Analytics through Hardware-Accelerated Traversal Scheduling

Graph processing is increasingly bottlenecked by main memory accesses. On-chip caches are of little help because the irregular structure of graphs causes seemingly random memory references. However, most real-world graphs offer significant potential locality—it is just hard to predict ahead of time. In practice, graphs have well-connected regions where relatively few vertices share edges with many common neighbors. If these vertices were processed together, graph processing would enjoy significant data reuse. Hence, a graph's traversal schedule largely determines its locality. This paper explores online traversal scheduling strategies that exploit the community structure of real-world graphs to improve locality. Software graph processing frameworks use simple, locality-oblivious scheduling because, on general-purpose cores, the benefits of locality-aware scheduling are outweighed by its overheads. Software frameworks rely on offline preprocessing to improve locality. Unfortunately, preprocessing is so expensive that its costs often negate any benefits from improved locality. Recent graph processing accelerators have inherited this design. Our insight is that this misses an opportunity: Hardware acceleration allows for more sophisticated, online locality-aware scheduling than can be realized in software, letting systems significantly improve locality without any preprocessing. To exploit this insight, we present bounded depth-first scheduling (BDFS), a simple online locality-aware scheduling strategy. BDFS restricts each core to explore one small, connected region of the graph at a time, improving locality on graphs with good community structure. We then present HATS, a hardware-accelerated traversal scheduler that adds just 0.4% area and 0.2% power over general-purpose cores. We evaluate BDFS and HATS on several algorithms using large real-world graphs. On a simulated 16-core system, BDFS reduces main memory accesses by up to 2.4x and by 30% on average. However, BDFS is too expensive in software and degrades performance by 21% on average. HATS eliminates these overheads, allowing BDFS to improve performance by 83% on average (up to 3.1x) over a locality-oblivious software implementation and by 31% on average (up to 2.1x) over specialized prefetchers.
The Relationship between the US Economy’s Information Processing and Absorption Ratios: Systematic vs Systemic Risk †

After the 2008 financial collapse, the now popular measure of implied systemic risk called the absorption ratio was introduced. This statistic measures how closely the economy’s markets are coupled. The more closely financial markets are coupled the more susceptible they are to systemic collapse. A new alternative measure of financial market health, the implied information processing ratio or entropic efficiency of the economy, was derived using concepts from information theory. This new entropic measure can also be useful in predicting economic downturns and measuring systematic risk. In the current work, the relationship between these two ratios and types of risks are explored. Potential methods of the joint use of these different measures to optimally reduce systemic and systematic risk are introduced.
Going beyond Visualization. Verbalization as Complementary Medium to Explain Machine Learning Models

In this position paper, we argue that a combination of visualization and verbalization techniques is beneficial for creating broad and versatile insights into the structure and decision-making processes of machine learning models. Explainability of machine learning models is emerging as an important area of research. Hence, insights into the inner workings of a trained model allow users and analysts, alike, to understand the models, develop justifications, and gain trust in the systems they inform. Explanations can be generated through different types of media, such as visualization and verbalization. Both are powerful tools that enable model interpretability. However, while their combination is arguably more powerful than each medium separately, they are currently applied and researched independently. To support our position that the combination of the two techniques is beneficial to explain machine learning models, we describe the design space of such a combination and discuss arising research questions, gaps, and opportunities.
Diagnostic and Vaccine Potentials of ESAT-6 Family Proteins encoded by M. tuberculosis genomic regions absent in M. bovis BCG

Tuberculosis is a major international health problem and its control requires cost-effective diagnostic reagents and protective vaccines. Among the candidates advocated for both applications is the immunodominant 6 kDa early secreted antigenic target (ESAT-6) of M. tuberculosis. The esat6 gene is located in the M. tuberculosis-specific genomic region of difference 1 (RD1), which is absent in all vaccine strains of M. bovis BCG. In addition to ESAT6, RD1 contains the gene for another immunodominant low molecular weight culture filtrate protein 10 (CFP-10). Both ESAT-6 (ESXA) and CFP10 (ESXB) belong to the ESAT-6 family. The sequences of ESXA and ESXB lack significant homology with each other and any other member of ESAT-6 family. Furthermore, four additional immune dominant proteins belonging to ESAT-6 family have been identified, whose genes are present in M. tuberculosis-specific genomic regions absent in M. bovis BCG, i.e. Rv2346c/ESXO and Rv2347c/ESXP in RD7, and Rv3619c/ESXV and Rv3620c/ESXW in RD9. ESXO and ESXV belong to ESAT-6 subfamily-1 and ESXP and ESXW to ESAT-6 subfamily 2. Each subfamily contains five members and has orthologs in M. bovis BCG. Although, members of subfamily 1 lack sequence identity with members of subfamily 2, each of the five members within a given family share >92% sequence identity. Immunization with RD proteins of ESAT-6 family protects animals against challenge with M. tuberculosis, but due to the immunodominant recognition by the majority of TB-infected and exposed individuals, and the absence in M. bovis BCG, ESXA and ESXB should be reserved for diagnostic applications, and the ESAT-6 subfamily 1 and 2 proteins deserve to be considered as subunit vaccine candidates.
The Status of Middle and High School Instruction: Examining Professional Development, Social Desirability, and Teacher Readiness for Blended Pedagogyin the Southeastern United States

Using quantitative methods, the current study addresses the phenomenon of blended learning and the impact of professional development (PD) in blended learning on teacher practice. Two separate but complementary investigations, Oliver’s (2013) focus group data for examining Oliver’s Framework for Blended Instruction and Parks’ (2015) national open-ended survey results, unveiled a common thread, which provided motivation for this study. A purposeful sample of 366 secondary public school teachers located in the Southeastern United States completed the Blended Practice Profile, a self-assessment diagnostic instrument, to measure their practices in blended teaching. Results indicate contradictory data, demonstrating that the majority of participating teachers who self-identify as blended educators actually fell within the novice and emerging range for competency in blended instruction, concluding that blended learning is socially desirable and not fully understood in its entirety. The data illuminate a need for personalized professional development for teachers of blended instruction to meet their specific needs and gaps in understanding of the competencies necessary for effective blended pedagogy. 80 Parks, Oliver, and Carson The authors suggest that both formal and informal professional development should model blended instructional practices and techniques that focus on enhancing learning and measure for efficacy to successfully impact and transform blended instruction and behaviors in the classroom with fidelity.
Fifty years of Cook Inlet beluga whale feeding ecology from isotopes in bone and teeth

Beluga whales Delphinapterus leucas that reside in Cook Inlet (CIBW) are important to coastal Alaska Native culture and subsistence, tourism, and ecologically as a top-level predator. Due to a ~50% population decline in the 1990s, the distinct population segment in Cook Inlet was designated depleted under the Marine Mammal Protection Act in 2000 and listed as endangered under the Endangered Species Act in 2008. Diet changes are a concern in CIBW lack of recovery, but beluga feeding ecology is difficult to study. Skulls from 20 CIBW and tooth growth layer groups (GLGs) from 26 individual CIBW showed decreasing trends for both nitrogen and carbon stable isotope ratios (expressed as δ15N and δ13C values) from 1962 to 2007. The decline in δ15N values (~1 to 2‰) could indicate a trophic level shift, but the magnitude of decline in δ13C values (~3‰) is much greater (>5 times greater) than expected for a trophic level shift. A shifted baseline or increased use of freshwater prey could explain the decline in δ13C values. We compared the strontium isotope composition (87Sr/86Sr ratios) of GLGs with rivers that flow into Cook Inlet and used δ15N values from the essential amino acid phenylalanine to determine that declining δ13C values may be explained by 2 scenarios: (1) CIBW foraged in the same location while the environmental isotopic baseline changed, or (2) CIBW foraged in a different location with a different baseline. This study presents the first evidence for a long-term (~50 yr) change in CIBW feeding ecology.
Effect of lipid parameters on carotid artery disease progression in patients undergoing carotid endarterectomy and unoperated patients with non-severe carotid artery stenosis

Objectives: We aim to evaluate the relationship between serum lipid indices and carotid artery stenosis (CAS). Methods: This retrospective observational study included patients who underwent carotid endarterectomy (CEA) and unoperated patients who have CAS less than 50%. CAS was diagnosed with history and physical examination followed by doppler ultrasonography, coronary and carotid angiography. All data retrieved retrospectively from the hospital medical files. Non-high-density lipoprotein-cholesterol (non-HDL-C) level was calculated by removing HDL-C from total cholesterol. In addition, low-density lipoprotein-cholesterol (LDL-C)/HDL-C ratio, non-HDL-C/HDL-C ratio, and triglyceride (TG)/HDL-C ratio were assessed. Results: CEA group (77.5% male, mean age: 71.35 ± 8.35 years) included 40 patients who underwent CEA, and there were 32 patients who have less than 50% CAS in unoperated group (68.8% male, mean age: 63.94 ± 9.92 years). The development of CAS was not significantly correlated with LDL-C/HDL-C ratio ( p = 0.119), non-HDL-C/HDL-C ratio ( p = 0.227) and TG/HDL-C ratio ( p = 0.768). Advanced age and presence of coronary artery disease were identified as an independent predictor of the development of CAS. For predicting development of CAS, there were 50.0% sensitivity and 65.6% specificity for LDL-C/HDL-C ratio and non-HDL-C/ HDL-C ratio (area under the curve [AUC]: 0.592, log rank p = 0.183, AUC: 0.583, log rank p = 0.227, respectively). Conclusions: We found that lipid ratios were not an independent predictors of the development of CAS. But, we think that AUC values which were above 0.5 for lipid ratios are may be important development of CAS.
Determining the elastic modulus of biological samples using atomic force microscopy

Using the atomic force microscope (AFM) for nanoindentation has emerged as a useful tool to determine elastic properties like the elastic modulus for biological samples (figure 1) [1][2][3][4]. Cantilevers serve as soft nanoindenters allowing local testing of small and inhomogeneous samples like cells or tissues. To calculate the parameter of interest various models are used, but most of them are based on the Hertz model and extended to match the experimental conditions concerning the indenters’ shape or the thickness of the sample [5][6][7] [8][9][10][12].
Classical and Quantum Causal Interventions

Characterising causal structure is an activity that is ubiquitous across the sciences. Causal models are representational devices that can be used as oracles for future interventions, to predict how values of some variables will change in response to interventions on others. Recent work has generalised concepts from this field to situations involving quantum systems, resulting in a new notion of quantum causal structure. A key concept in both the classical and quantum context is that of an intervention. Interventions are the controlled operations required to identify causal structure and ultimately the feature that endows causal models with empirical meaning. Although interventions are a crucial feature of both the classical and quantum causal modelling frameworks, to date there has been no discussion of their physical basis. In this paper, we consider interventions from a physical perspective and show that, in both the classical and quantum case, they are constrained by the thermodynamics of measurement and feedback in open systems. We demonstrate that the perfect “atomic” or “surgical” interventions characterised by Pearl’s famous do-calculus are physically impossible, and this is the case for both classical and quantum systems.
Automatic summarization for text simplification: evaluating text understanding by poor readers

In this paper we present experiments on summarization and text simplification for poor readers, more specifically, functional illiteracy readers. We test several summarizers and use summaries as the basis of simplification strategies. We show that each simplification approach has different effects on readers of varied levels of literacy, but that all of them do improve text understanding at some level.
Single-Image Depth Estimation Based on Fourier Domain Analysis

We propose a deep learning algorithm for single-image depth estimation based on the Fourier frequency domain analysis. First, we develop a convolutional neural network structure and propose a new loss function, called depth-balanced Euclidean loss, to train the network reliably for a wide range of depths. Then, we generate multiple depth map candidates by cropping input images with various cropping ratios. In general, a cropped image with a small ratio yields depth details more faithfully, while that with a large ratio provides the overall depth distribution more reliably. To take advantage of these complementary properties, we combine the multiple candidates in the frequency domain. Experimental results demonstrate that proposed algorithm provides the state-of-art performance. Furthermore, through the frequency domain analysis, we validate the efficacy of the proposed algorithm in most frequency bands.
Conditions Influencing Municipal Strategy-Making for Sustainable Urban Water Management: Assessment of Three Swedish Municipalities

Strategy-making is key for realizing sustainable urban water management. Though general barriers and factors for change have been identified, fewer studies have assessed how different conditions influence municipalities’ strategy-making ability and, thus, how to plan strategically given these conditions. Healey’s strategy-making notion was applied to delimit a study of how size, finances, development path, and water organization influence Swedish municipalities’ strategy-making ability for urban water. Three municipalities, Laxå, Norrköping, and Skellefteå, with different, yet overlapping, institutional and socio-economic conditions were analyzed using semi-structured interviews, a stakeholder workshop, and document analyses. The study finds that even though key events have filtered urban water issues into the political agenda, this has not induced systemic change, except where the role of water management in urban development has been specified, i.e., has aligned dispersed planning processes. Organizational setup influences the strategy-making ability by prescribing not only when water issues are raised, but also what system perspective should be applied and what actors that should be enrolled. Judging from the three cases, size, finances, and development path do matter for strategy-making ability, but they appear to be less important than the organizational setup. Departures for improving strategy-making under different conditions are discussed.
The ASC-Inclusion Perceptual Serious Gaming Platform for Autistic Children

‘‘Serious games” are becoming extremely relevant to individuals who have specific needs, such as children with an autism spectrum condition (ASC). Often, individuals with an ASC have difficulties in interpreting verbal and nonverbal communication cues during social interactions. The ASC-Inclusion EU-FP7 funded project aims to provide children who have an ASC with a platform to learn emotion expression and recognition, through play in the virtual world. In particular, the ASC-Inclusion platform focuses on the expression of emotion via facial, vocal, and bodily gestures. The platform combines multiple analysis tools, using onboard microphone and webcam capabilities. The platform utilizes these capabilities via training games, text-based communication, animations, video, and audio clips. This paper introduces current findings and evaluations of the ASC-Inclusion platform and provides detailed description for the different modalities.
DNN-Based Audio Scene Classification for DCASE2017: Dual Input Features, Balancing Cost, and Stochastic Data Duplication

In this study, we explored DNN-based audio scene classification systems with dual input features. Dual input features take advantage of simultaneously utilizing two features with different levels of abstraction as inputs: a frame-level mel-filterbank feature and segment-level identity vector. A new fine-tune cost that solves the drawback of dual input features was developed, as well as a data duplication method that enables DNN to clearly discriminate frequently misclassified classes. Combining the proposed methods with the latest DNN techniques such as residual learning achieved a fold-wise accuracy of 95.9% for the validation set and 70.6% for the evaluation set provided by the Detection and Classification of Acoustic Scenes and Events community.
BPEC: Belief-Peaks Evidential Clustering

This paper introduces a new evidential clustering method based on the notion of “belief peaks” in the framework of belief functions. The basic idea is that all data objects in the neighborhood of each sample provide pieces of evidence that induce belief on the possibility of such sample to become a cluster center. A sample having higher belief than its neighbors and located far away from the other local maxima is then characterized as cluster center. Finally, a credal partition is created by minimizing an objective function with the fixed cluster centers. An adaptive distance metric is used to fit for unknown shapes of data structures. We show that the proposed evidential clustering procedure has very good performance with an ability to reveal the data structure in the form of a credal partition, from which hard, fuzzy, possibilistic, and rough partitions can be derived. Simulations on synthetic and real-world datasets validate our conclusions.
Wide-spectrum biomimetic antimicrobial systems

Antimicrobial peptides (AMPs) are effective components of the host immune response and are widely distributed throughout nature. Recently, nontoxic antimicrobial polymers that mimic the structures of naturally occurring AMPs have been designed and are under development commercially as novel therapeutics. These compounds have several potential advantages over natural AMPs, including greater stability and reduced immunogenicity compared to natural peptides, relatively simple and scalable syntheses and the ability to tailor or “fine tune” their activities through combinatorial approaches. In previous work, we demonstrated the utility of certain generally regarded as safe (GRAS) flavorant and aroma compounds as enhancers of uptake and activity of clinically important antibiotics (Brehm-Stecher & Johnson, 2003). Here, we have extended this approach to include enhancement of biomimetic antimicrobial polymers. Three low molecular weight (<1000 >D), broad-spectrum arylamide polymers (PolyMedix, Inc., Radnor, PA) were examined for their antimicrobial activities against gram-negative bacteria, gram-positive bacteria, yeast and filamentous fungi, both alone and when co-administered with sesquiterpenoid enhancers. Assay formats included disk diffusion, automated turbidimetry, time course (kinetic) plating of antimicrobial-treated cell suspensions, outer membrane assays with 1-N-phenylnaphthylamine (NPN) and transmission electron microscopy (TEM). Although results differed according to the polymer and test organism used, treatments containing sesquiterpenoids were marked by either increased ZOIs, decreased MICs or more rapid inactivation when compared with polymer-only treatments. Antimicrobial activity, expressed as decimal reduction times (D-value), showed that after 5 min, the combination of sesquiterpenoid and polymer was significantly different from the controls (p < 0.05) with a D-value of 3.92 min when incubated with Escherichia coli ATCC 25922. Collectively, our results indicate that the combination of sesquiterpenoidenhancing agents with biomimetic antimicrobial polymers shows promise for the development of new, fasteracting and more broadly effective antimicrobial therapies.
Prevalence of Metabolic Syndrome Among Iranian Female Teachers Residing in Yazd , Iran

Prevalence of Metabolic Syndrome Among Iranian Female Teachers Residing in Yazd, Iran Simin Shahvazi1,2, Ziba Mehri1,2, Azadeh Nadjarzadeh1,2, Amin Salehi-Abargouei1,2 1Nutrition and Food Security Research Center, Shahid Sadoughi University of Medical Sciences, Yazd, Iran. 2Department of Nutrition, Faculty of Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran. Corresponding Author: Amin Salehi-Abargouei, PhD in Nutritional Sciences, Department of Nutrition, Faculty of Health, Sahid Sadoughi University of Medical Sciences, Yazd, Iran, Tel: +98-35-38209100; Email: abargouei@hlth.mui. ac.ir
Cooking DBMS Operations using Granular Primitives An overview on a primitive-based RDBMS query evaluation

The increasing heterogeneity of the underlying hardware forces modern database system engineers to implement multiple variants of a single database operator (e.g., join, selection). With increasing heterogeneity, these variants become too complex to maintain and tune for different devices. To overcome these disadvantages, developers use an alternative, primitivebased operator design. This design paradigm splits the database operators into granular functions or primitives and executes a given operator by combing the necessary primitives. Hence, we require only a limited set of these primitives as we reuse them for multiple database operations. Thus, tuning a single primitive improves efficiency of all the database operations using it. In this survey, we provide an overview of a primitivebased database engine. First, we list different primitives from literature and place them in a hierarchy from the finest granular level to a complete database operator. Second, for each of primitive we list its possible tuning opportunities. Finally, we discuss the significance of primitive-based execution on the query engine. Overall, this survey aims to serve as a general reference for implementing a primitive-based query engine and possible strategies to tune it for specific processors.